Split (1)

train · 1.3k rows

model_id stringlengths 9 102	model_card stringlengths 4 343k	model_labels listlengths 2 50.8k
machinelearningzuu/queue_detection	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # queue_detection This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1249 - Map: 0.9571 - Map 50: 0.9901 - Map 75: 0.9844 - Map Small: -1.0 - Map Medium: 0.3322 - Map Large: 0.9614 - Mar 1: 0.5052 - Mar 10: 0.9726 - Mar 100: 0.9733 - Mar Small: -1.0 - Mar Medium: 0.3654 - Mar Large: 0.9759 - Map Cashier: 0.9657 - Mar 100 Cashier: 0.9777 - Map Cx: 0.9486 - Mar 100 Cx: 0.969 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Small \| Map Medium \| Map Large \| Mar 1 \| Mar 10 \| Mar 100 \| Mar Small \| Mar Medium \| Mar Large \| Map Cashier \| Mar 100 Cashier \| Map Cx \| Mar 100 Cx \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:---------:\|:----------:\|:---------:\|:-----------:\|:---------------:\|:------:\|:----------:\| \| No log \| 1.0 \| 218 \| 1.6511 \| 0.1028 \| 0.2445 \| 0.079 \| -1.0 \| 0.0 \| 0.1036 \| 0.105 \| 0.4632 \| 0.6421 \| -1.0 \| 0.0 \| 0.6464 \| 0.1151 \| 0.6993 \| 0.0905 \| 0.5848 \| \| No log \| 2.0 \| 436 \| 1.2010 \| 0.3717 \| 0.5667 \| 0.4222 \| -1.0 \| 0.0003 \| 0.3749 \| 0.3095 \| 0.6671 \| 0.7472 \| -1.0 \| 0.0071 \| 0.754 \| 0.4302 \| 0.8025 \| 0.3132 \| 0.6919 \| \| 2.8665 \| 3.0 \| 654 \| 0.9101 \| 0.5265 \| 0.7814 \| 0.6068 \| -1.0 \| 0.0 \| 0.5298 \| 0.3399 \| 0.7167 \| 0.7588 \| -1.0 \| 0.0 \| 0.7642 \| 0.6719 \| 0.8041 \| 0.3811 \| 0.7136 \| \| 2.8665 \| 4.0 \| 872 \| 0.7512 \| 0.6528 \| 0.9101 \| 0.7828 \| -1.0 \| 0.0453 \| 0.6571 \| 0.3744 \| 0.7556 \| 0.78 \| -1.0 \| 0.0577 \| 0.7856 \| 0.7274 \| 0.8003 \| 0.5782 \| 0.7597 \| \| 0.8677 \| 5.0 \| 1090 \| 0.7338 \| 0.6431 \| 0.9366 \| 0.7893 \| -1.0 \| 0.0381 \| 0.6472 \| 0.3563 \| 0.7437 \| 0.7641 \| -1.0 \| 0.1773 \| 0.7672 \| 0.6835 \| 0.7876 \| 0.6026 \| 0.7406 \| \| 0.8677 \| 6.0 \| 1308 \| 0.6059 \| 0.7148 \| 0.9548 \| 0.8683 \| 0.0 \| 0.0207 \| 0.7205 \| 0.3998 \| 0.783 \| 0.7947 \| 0.0 \| 0.1038 \| 0.8001 \| 0.7552 \| 0.8218 \| 0.6744 \| 0.7677 \| \| 0.6584 \| 7.0 \| 1526 \| 0.5631 \| 0.7425 \| 0.9666 \| 0.8883 \| -1.0 \| 0.0418 \| 0.7475 \| 0.4104 \| 0.809 \| 0.8218 \| -1.0 \| 0.1227 \| 0.8266 \| 0.7871 \| 0.8501 \| 0.698 \| 0.7935 \| \| 0.6584 \| 8.0 \| 1744 \| 0.5143 \| 0.7573 \| 0.9724 \| 0.9071 \| -1.0 \| 0.084 \| 0.7621 \| 0.4178 \| 0.8205 \| 0.8286 \| -1.0 \| 0.1643 \| 0.8332 \| 0.782 \| 0.8544 \| 0.7326 \| 0.8028 \| \| 0.6584 \| 9.0 \| 1962 \| 0.5286 \| 0.7525 \| 0.9715 \| 0.9094 \| -1.0 \| 0.0377 \| 0.7577 \| 0.4114 \| 0.8123 \| 0.8209 \| -1.0 \| 0.0962 \| 0.8261 \| 0.809 \| 0.8672 \| 0.696 \| 0.7747 \| \| 0.5455 \| 10.0 \| 2180 \| 0.4969 \| 0.7716 \| 0.9762 \| 0.9247 \| 0.0 \| 0.0586 \| 0.7759 \| 0.4247 \| 0.8186 \| 0.8268 \| 0.0 \| 0.1444 \| 0.8306 \| 0.8082 \| 0.8532 \| 0.735 \| 0.8005 \| \| 0.5455 \| 11.0 \| 2398 \| 0.4934 \| 0.7701 \| 0.97 \| 0.9216 \| 0.0 \| 0.0231 \| 0.779 \| 0.4245 \| 0.8256 \| 0.8403 \| 0.0 \| 0.0676 \| 0.8499 \| 0.8059 \| 0.8694 \| 0.7343 \| 0.8112 \| \| 0.5034 \| 12.0 \| 2616 \| 0.4560 \| 0.7917 \| 0.9764 \| 0.9315 \| 0.0 \| 0.0278 \| 0.7974 \| 0.4331 \| 0.8416 \| 0.8521 \| 0.0 \| 0.1542 \| 0.8568 \| 0.8305 \| 0.8874 \| 0.753 \| 0.8168 \| \| 0.5034 \| 13.0 \| 2834 \| 0.4293 \| 0.8 \| 0.9814 \| 0.9299 \| -1.0 \| 0.0552 \| 0.8042 \| 0.4353 \| 0.8556 \| 0.8627 \| -1.0 \| 0.15 \| 0.8664 \| 0.85 \| 0.8956 \| 0.7499 \| 0.8297 \| \| 0.4705 \| 14.0 \| 3052 \| 0.3860 \| 0.8269 \| 0.9848 \| 0.9404 \| -1.0 \| 0.0996 \| 0.8328 \| 0.4497 \| 0.8701 \| 0.876 \| -1.0 \| 0.1969 \| 0.881 \| 0.8573 \| 0.9017 \| 0.7965 \| 0.8502 \| \| 0.4705 \| 15.0 \| 3270 \| 0.4005 \| 0.8193 \| 0.9764 \| 0.9476 \| 0.0 \| 0.0535 \| 0.8277 \| 0.4467 \| 0.8643 \| 0.8703 \| 0.0 \| 0.1806 \| 0.8771 \| 0.8521 \| 0.9021 \| 0.7864 \| 0.8384 \| \| 0.4705 \| 16.0 \| 3488 \| 0.3911 \| 0.813 \| 0.9827 \| 0.9434 \| -1.0 \| 0.1665 \| 0.8177 \| 0.4442 \| 0.8633 \| 0.8712 \| -1.0 \| 0.3929 \| 0.875 \| 0.8513 \| 0.9011 \| 0.7748 \| 0.8413 \| \| 0.4375 \| 17.0 \| 3706 \| 0.3538 \| 0.8352 \| 0.9871 \| 0.9687 \| -1.0 \| 0.1586 \| 0.8391 \| 0.4533 \| 0.8801 \| 0.8832 \| -1.0 \| 0.2423 \| 0.8867 \| 0.8697 \| 0.9096 \| 0.8006 \| 0.8569 \| \| 0.4375 \| 18.0 \| 3924 \| 0.3740 \| 0.8233 \| 0.9867 \| 0.9561 \| -1.0 \| 0.1498 \| 0.827 \| 0.4492 \| 0.8731 \| 0.8774 \| -1.0 \| 0.2333 \| 0.8801 \| 0.8532 \| 0.9028 \| 0.7935 \| 0.852 \| \| 0.4093 \| 19.0 \| 4142 \| 0.3847 \| 0.8151 \| 0.9831 \| 0.9572 \| -1.0 \| 0.2685 \| 0.8213 \| 0.4446 \| 0.8628 \| 0.8662 \| -1.0 \| 0.3286 \| 0.8717 \| 0.863 \| 0.907 \| 0.7672 \| 0.8254 \| \| 0.4093 \| 20.0 \| 4360 \| 0.3744 \| 0.8148 \| 0.9869 \| 0.9585 \| -1.0 \| 0.109 \| 0.8196 \| 0.4464 \| 0.8659 \| 0.8716 \| -1.0 \| 0.1458 \| 0.8762 \| 0.8403 \| 0.8967 \| 0.7894 \| 0.8466 \| \| 0.4047 \| 21.0 \| 4578 \| 0.3521 \| 0.8355 \| 0.9879 \| 0.9669 \| -1.0 \| 0.1021 \| 0.8414 \| 0.4537 \| 0.8797 \| 0.8847 \| -1.0 \| 0.1773 \| 0.8887 \| 0.8627 \| 0.9097 \| 0.8083 \| 0.8597 \| \| 0.4047 \| 22.0 \| 4796 \| 0.3752 \| 0.8357 \| 0.9798 \| 0.9508 \| 0.0 \| 0.1229 \| 0.8447 \| 0.4572 \| 0.8752 \| 0.8787 \| 0.0 \| 0.1676 \| 0.8874 \| 0.866 \| 0.9047 \| 0.8054 \| 0.8527 \| \| 0.3804 \| 23.0 \| 5014 \| 0.3472 \| 0.8435 \| 0.9865 \| 0.9601 \| -1.0 \| 0.1067 \| 0.848 \| 0.4574 \| 0.8844 \| 0.8893 \| -1.0 \| 0.2214 \| 0.8934 \| 0.8729 \| 0.9158 \| 0.8142 \| 0.8627 \| \| 0.3804 \| 24.0 \| 5232 \| 0.3095 \| 0.8554 \| 0.9881 \| 0.9711 \| -1.0 \| 0.0987 \| 0.8604 \| 0.4696 \| 0.8963 \| 0.9001 \| -1.0 \| 0.1727 \| 0.9042 \| 0.8888 \| 0.9297 \| 0.8221 \| 0.8706 \| \| 0.3804 \| 25.0 \| 5450 \| 0.3470 \| 0.8389 \| 0.9882 \| 0.9602 \| -1.0 \| 0.1231 \| 0.843 \| 0.4527 \| 0.8786 \| 0.881 \| -1.0 \| 0.175 \| 0.8858 \| 0.8827 \| 0.9194 \| 0.7952 \| 0.8426 \| \| 0.3534 \| 26.0 \| 5668 \| 0.3446 \| 0.8281 \| 0.9889 \| 0.9618 \| 0.0 \| 0.1064 \| 0.8336 \| 0.4528 \| 0.8751 \| 0.8784 \| 0.0 \| 0.22 \| 0.8828 \| 0.8574 \| 0.9055 \| 0.7988 \| 0.8512 \| \| 0.3534 \| 27.0 \| 5886 \| 0.3234 \| 0.844 \| 0.9881 \| 0.9593 \| -1.0 \| 0.1256 \| 0.8493 \| 0.4591 \| 0.8856 \| 0.8881 \| -1.0 \| 0.18 \| 0.8933 \| 0.8751 \| 0.9161 \| 0.8129 \| 0.8601 \| \| 0.3461 \| 28.0 \| 6104 \| 0.2976 \| 0.862 \| 0.9881 \| 0.9588 \| -1.0 \| 0.1683 \| 0.8664 \| 0.47 \| 0.8979 \| 0.9015 \| -1.0 \| 0.2083 \| 0.9054 \| 0.8989 \| 0.9344 \| 0.825 \| 0.8685 \| \| 0.3461 \| 29.0 \| 6322 \| 0.2960 \| 0.8607 \| 0.9891 \| 0.9744 \| -1.0 \| 0.1628 \| 0.8649 \| 0.4662 \| 0.9 \| 0.9031 \| -1.0 \| 0.26 \| 0.9058 \| 0.891 \| 0.9311 \| 0.8304 \| 0.875 \| \| 0.344 \| 30.0 \| 6540 \| 0.3070 \| 0.8599 \| 0.9881 \| 0.9694 \| -1.0 \| 0.1578 \| 0.8648 \| 0.4628 \| 0.8975 \| 0.8992 \| -1.0 \| 0.2 \| 0.9035 \| 0.8877 \| 0.9233 \| 0.832 \| 0.8751 \| \| 0.344 \| 31.0 \| 6758 \| 0.3176 \| 0.852 \| 0.9864 \| 0.97 \| 0.0 \| 0.0895 \| 0.859 \| 0.462 \| 0.8915 \| 0.8934 \| 0.0 \| 0.1538 \| 0.8996 \| 0.8791 \| 0.9194 \| 0.8249 \| 0.8675 \| \| 0.344 \| 32.0 \| 6976 \| 0.3198 \| 0.8478 \| 0.9881 \| 0.963 \| -1.0 \| 0.1354 \| 0.8527 \| 0.4637 \| 0.8891 \| 0.8919 \| -1.0 \| 0.2143 \| 0.8967 \| 0.8859 \| 0.925 \| 0.8097 \| 0.8588 \| \| 0.3308 \| 33.0 \| 7194 \| 0.3002 \| 0.8564 \| 0.989 \| 0.9708 \| -1.0 \| 0.1237 \| 0.8609 \| 0.463 \| 0.8954 \| 0.8969 \| -1.0 \| 0.1636 \| 0.9011 \| 0.8887 \| 0.9251 \| 0.8241 \| 0.8688 \| \| 0.3308 \| 34.0 \| 7412 \| 0.2953 \| 0.8659 \| 0.9891 \| 0.9737 \| -1.0 \| 0.1593 \| 0.8716 \| 0.471 \| 0.9014 \| 0.9033 \| -1.0 \| 0.2393 \| 0.9073 \| 0.8934 \| 0.9261 \| 0.8385 \| 0.8805 \| \| 0.321 \| 35.0 \| 7630 \| 0.2858 \| 0.8733 \| 0.9873 \| 0.9703 \| -1.0 \| 0.1392 \| 0.8801 \| 0.4696 \| 0.9071 \| 0.9098 \| -1.0 \| 0.1861 \| 0.916 \| 0.8998 \| 0.9346 \| 0.8468 \| 0.8849 \| \| 0.321 \| 36.0 \| 7848 \| 0.2604 \| 0.8845 \| 0.9889 \| 0.9716 \| -1.0 \| 0.1392 \| 0.8895 \| 0.4769 \| 0.9175 \| 0.9195 \| -1.0 \| 0.1818 \| 0.9237 \| 0.9109 \| 0.9427 \| 0.858 \| 0.8963 \| \| 0.3074 \| 37.0 \| 8066 \| 0.3035 \| 0.8609 \| 0.987 \| 0.9704 \| 0.0 \| 0.096 \| 0.8695 \| 0.4672 \| 0.8995 \| 0.9005 \| 0.0 \| 0.1733 \| 0.9077 \| 0.8853 \| 0.9234 \| 0.8365 \| 0.8777 \| \| 0.3074 \| 38.0 \| 8284 \| 0.2746 \| 0.8641 \| 0.9894 \| 0.977 \| -1.0 \| 0.0994 \| 0.8686 \| 0.4711 \| 0.9035 \| 0.9053 \| -1.0 \| 0.2167 \| 0.9083 \| 0.8866 \| 0.9246 \| 0.8416 \| 0.886 \| \| 0.2989 \| 39.0 \| 8502 \| 0.2851 \| 0.864 \| 0.9894 \| 0.9684 \| -1.0 \| 0.1414 \| 0.8703 \| 0.4702 \| 0.9033 \| 0.904 \| -1.0 \| 0.24 \| 0.9083 \| 0.8873 \| 0.9265 \| 0.8407 \| 0.8815 \| \| 0.2989 \| 40.0 \| 8720 \| 0.2456 \| 0.8843 \| 0.9896 \| 0.9771 \| -1.0 \| 0.1205 \| 0.8897 \| 0.4798 \| 0.9172 \| 0.9178 \| -1.0 \| 0.2062 \| 0.923 \| 0.9189 \| 0.946 \| 0.8496 \| 0.8895 \| \| 0.2989 \| 41.0 \| 8938 \| 0.2567 \| 0.8777 \| 0.9897 \| 0.9742 \| -1.0 \| 0.162 \| 0.8843 \| 0.4771 \| 0.9134 \| 0.9136 \| -1.0 \| 0.2094 \| 0.9188 \| 0.8967 \| 0.9335 \| 0.8586 \| 0.8936 \| \| 0.2782 \| 42.0 \| 9156 \| 0.2444 \| 0.8858 \| 0.9897 \| 0.9779 \| -1.0 \| 0.2728 \| 0.8918 \| 0.4768 \| 0.9187 \| 0.9189 \| -1.0 \| 0.3885 \| 0.923 \| 0.9172 \| 0.9496 \| 0.8544 \| 0.8882 \| \| 0.2782 \| 43.0 \| 9374 \| 0.2389 \| 0.8879 \| 0.9899 \| 0.9793 \| -1.0 \| 0.1582 \| 0.8934 \| 0.4798 \| 0.9199 \| 0.9204 \| -1.0 \| 0.2 \| 0.9249 \| 0.9142 \| 0.9448 \| 0.8617 \| 0.896 \| \| 0.2694 \| 44.0 \| 9592 \| 0.2401 \| 0.8962 \| 0.9896 \| 0.9783 \| -1.0 \| 0.1936 \| 0.9007 \| 0.481 \| 0.9255 \| 0.9263 \| -1.0 \| 0.2227 \| 0.93 \| 0.9183 \| 0.9471 \| 0.8742 \| 0.9055 \| \| 0.2694 \| 45.0 \| 9810 \| 0.2550 \| 0.8906 \| 0.9895 \| 0.9801 \| 0.0 \| 0.224 \| 0.8969 \| 0.4784 \| 0.9219 \| 0.923 \| 0.0 \| 0.2625 \| 0.9284 \| 0.9153 \| 0.9463 \| 0.8659 \| 0.8996 \| \| 0.2592 \| 46.0 \| 10028 \| 0.2364 \| 0.8963 \| 0.9893 \| 0.9776 \| 0.0 \| 0.1886 \| 0.9016 \| 0.4845 \| 0.9282 \| 0.9291 \| 0.0 \| 0.2615 \| 0.9333 \| 0.9224 \| 0.9512 \| 0.8703 \| 0.9069 \| \| 0.2592 \| 47.0 \| 10246 \| 0.2435 \| 0.8838 \| 0.9871 \| 0.9719 \| 0.0 \| 0.215 \| 0.889 \| 0.4792 \| 0.92 \| 0.921 \| 0.0 \| 0.2833 \| 0.9246 \| 0.9062 \| 0.939 \| 0.8614 \| 0.903 \| \| 0.2592 \| 48.0 \| 10464 \| 0.2241 \| 0.9014 \| 0.9899 \| 0.9836 \| -1.0 \| 0.2242 \| 0.9065 \| 0.4842 \| 0.9326 \| 0.9333 \| -1.0 \| 0.25 \| 0.9366 \| 0.9184 \| 0.9493 \| 0.8844 \| 0.9172 \| \| 0.25 \| 49.0 \| 10682 \| 0.2348 \| 0.8975 \| 0.9899 \| 0.9773 \| 0.0 \| 0.3675 \| 0.9037 \| 0.483 \| 0.9308 \| 0.931 \| 0.0 \| 0.39 \| 0.9369 \| 0.9166 \| 0.9463 \| 0.8784 \| 0.9156 \| \| 0.25 \| 50.0 \| 10900 \| 0.2374 \| 0.895 \| 0.9896 \| 0.9734 \| -1.0 \| 0.2121 \| 0.8994 \| 0.4789 \| 0.9266 \| 0.9274 \| -1.0 \| 0.3 \| 0.9325 \| 0.9135 \| 0.943 \| 0.8765 \| 0.9118 \| \| 0.2505 \| 51.0 \| 11118 \| 0.1953 \| 0.9169 \| 0.99 \| 0.9895 \| -1.0 \| 0.2485 \| 0.9218 \| 0.4934 \| 0.9467 \| 0.9472 \| -1.0 \| 0.2833 \| 0.9498 \| 0.9326 \| 0.9596 \| 0.9012 \| 0.9348 \| \| 0.2505 \| 52.0 \| 11336 \| 0.2362 \| 0.9006 \| 0.99 \| 0.9787 \| -1.0 \| 0.2266 \| 0.9066 \| 0.4818 \| 0.9303 \| 0.931 \| -1.0 \| 0.3423 \| 0.9359 \| 0.9319 \| 0.9552 \| 0.8694 \| 0.9069 \| \| 0.2291 \| 53.0 \| 11554 \| 0.2253 \| 0.9052 \| 0.9891 \| 0.9735 \| -1.0 \| 0.1798 \| 0.9126 \| 0.4885 \| 0.9326 \| 0.9339 \| -1.0 \| 0.2225 \| 0.9403 \| 0.9298 \| 0.9528 \| 0.8807 \| 0.9151 \| \| 0.2291 \| 54.0 \| 11772 \| 0.2214 \| 0.9093 \| 0.9898 \| 0.9822 \| -1.0 \| 0.2074 \| 0.9132 \| 0.4883 \| 0.9365 \| 0.9372 \| -1.0 \| 0.2192 \| 0.9416 \| 0.9322 \| 0.9572 \| 0.8864 \| 0.9172 \| \| 0.2291 \| 55.0 \| 11990 \| 0.2088 \| 0.9133 \| 0.99 \| 0.9848 \| -1.0 \| 0.2959 \| 0.918 \| 0.4882 \| 0.9416 \| 0.9417 \| -1.0 \| 0.3333 \| 0.9437 \| 0.934 \| 0.9586 \| 0.8926 \| 0.9247 \| \| 0.2323 \| 56.0 \| 12208 \| 0.2029 \| 0.9127 \| 0.9897 \| 0.9789 \| 0.0 \| 0.1756 \| 0.9168 \| 0.4915 \| 0.9394 \| 0.9406 \| 0.0 \| 0.24 \| 0.9455 \| 0.9382 \| 0.961 \| 0.8872 \| 0.9201 \| \| 0.2323 \| 57.0 \| 12426 \| 0.2137 \| 0.9074 \| 0.99 \| 0.9849 \| -1.0 \| 0.2304 \| 0.9101 \| 0.488 \| 0.9359 \| 0.9362 \| -1.0 \| 0.25 \| 0.9401 \| 0.9335 \| 0.9573 \| 0.8813 \| 0.9151 \| \| 0.2114 \| 58.0 \| 12644 \| 0.1903 \| 0.9231 \| 0.9899 \| 0.9841 \| -1.0 \| 0.1404 \| 0.9299 \| 0.4932 \| 0.9463 \| 0.9466 \| -1.0 \| 0.2077 \| 0.9513 \| 0.947 \| 0.9664 \| 0.8992 \| 0.9269 \| \| 0.2114 \| 59.0 \| 12862 \| 0.1921 \| 0.9211 \| 0.99 \| 0.9841 \| 0.0 \| 0.2628 \| 0.9254 \| 0.4942 \| 0.9439 \| 0.945 \| 0.0 \| 0.305 \| 0.9487 \| 0.9453 \| 0.9645 \| 0.8969 \| 0.9255 \| \| 0.2076 \| 60.0 \| 13080 \| 0.1737 \| 0.9335 \| 0.99 \| 0.984 \| -1.0 \| 0.2575 \| 0.9382 \| 0.4982 \| 0.9559 \| 0.9563 \| -1.0 \| 0.3 \| 0.959 \| 0.9592 \| 0.975 \| 0.9078 \| 0.9376 \| \| 0.2076 \| 61.0 \| 13298 \| 0.1795 \| 0.9272 \| 0.99 \| 0.9807 \| -1.0 \| 0.2635 \| 0.9305 \| 0.4958 \| 0.9502 \| 0.9513 \| -1.0 \| 0.31 \| 0.9548 \| 0.9488 \| 0.9661 \| 0.9057 \| 0.9365 \| \| 0.2086 \| 62.0 \| 13516 \| 0.1887 \| 0.9266 \| 0.9901 \| 0.9847 \| 0.0 \| 0.2762 \| 0.9321 \| 0.493 \| 0.95 \| 0.9507 \| 0.0 \| 0.3083 \| 0.9547 \| 0.9502 \| 0.969 \| 0.9029 \| 0.9323 \| \| 0.2086 \| 63.0 \| 13734 \| 0.1734 \| 0.9348 \| 0.9901 \| 0.9859 \| -1.0 \| 0.2761 \| 0.9397 \| 0.4978 \| 0.9581 \| 0.9588 \| -1.0 \| 0.315 \| 0.9613 \| 0.9517 \| 0.9718 \| 0.9178 \| 0.9458 \| \| 0.2086 \| 64.0 \| 13952 \| 0.1788 \| 0.9374 \| 0.9899 \| 0.9843 \| 0.0 \| 0.2255 \| 0.9417 \| 0.4992 \| 0.9589 \| 0.9592 \| 0.0 \| 0.2417 \| 0.9643 \| 0.9573 \| 0.9759 \| 0.9174 \| 0.9424 \| \| 0.1951 \| 65.0 \| 14170 \| 0.1665 \| 0.9374 \| 0.99 \| 0.985 \| -1.0 \| 0.2792 \| 0.9424 \| 0.4987 \| 0.9602 \| 0.9607 \| -1.0 \| 0.2909 \| 0.9638 \| 0.9537 \| 0.9734 \| 0.9212 \| 0.948 \| \| 0.1951 \| 66.0 \| 14388 \| 0.1813 \| 0.933 \| 0.99 \| 0.9834 \| 0.0 \| 0.2715 \| 0.9369 \| 0.4979 \| 0.9549 \| 0.9558 \| 0.0 \| 0.2944 \| 0.9601 \| 0.9521 \| 0.9727 \| 0.9138 \| 0.9388 \| \| 0.1904 \| 67.0 \| 14606 \| 0.1797 \| 0.9347 \| 0.9894 \| 0.9798 \| 0.0 \| 0.2227 \| 0.9389 \| 0.4961 \| 0.9534 \| 0.9547 \| 0.0 \| 0.2346 \| 0.9602 \| 0.9585 \| 0.9737 \| 0.9109 \| 0.9357 \| \| 0.1904 \| 68.0 \| 14824 \| 0.1792 \| 0.9321 \| 0.9897 \| 0.984 \| 0.0 \| 0.2146 \| 0.9378 \| 0.4954 \| 0.9523 \| 0.9532 \| 0.0 \| 0.2633 \| 0.9582 \| 0.9523 \| 0.9697 \| 0.9119 \| 0.9368 \| \| 0.186 \| 69.0 \| 15042 \| 0.1472 \| 0.9461 \| 0.9901 \| 0.9843 \| -1.0 \| 0.2637 \| 0.9524 \| 0.5018 \| 0.9665 \| 0.9673 \| -1.0 \| 0.3143 \| 0.9708 \| 0.9595 \| 0.9774 \| 0.9327 \| 0.9573 \| \| 0.186 \| 70.0 \| 15260 \| 0.1736 \| 0.931 \| 0.9899 \| 0.9842 \| -1.0 \| 0.269 \| 0.9352 \| 0.4961 \| 0.9538 \| 0.9543 \| -1.0 \| 0.2821 \| 0.9581 \| 0.9443 \| 0.9654 \| 0.9178 \| 0.9432 \| \| 0.186 \| 71.0 \| 15478 \| 0.1550 \| 0.9481 \| 0.99 \| 0.9835 \| -1.0 \| 0.2275 \| 0.9532 \| 0.5019 \| 0.9671 \| 0.9678 \| -1.0 \| 0.3321 \| 0.9723 \| 0.9601 \| 0.9771 \| 0.9361 \| 0.9584 \| \| 0.178 \| 72.0 \| 15696 \| 0.1609 \| 0.9455 \| 0.99 \| 0.9849 \| -1.0 \| 0.2252 \| 0.951 \| 0.5004 \| 0.9642 \| 0.9652 \| -1.0 \| 0.2731 \| 0.969 \| 0.9614 \| 0.9768 \| 0.9296 \| 0.9535 \| \| 0.178 \| 73.0 \| 15914 \| 0.1482 \| 0.9416 \| 0.9901 \| 0.9843 \| 0.0 \| 0.2421 \| 0.947 \| 0.5025 \| 0.9627 \| 0.9639 \| 0.0 \| 0.2967 \| 0.9684 \| 0.9554 \| 0.9751 \| 0.9278 \| 0.9527 \| \| 0.1822 \| 74.0 \| 16132 \| 0.1403 \| 0.9471 \| 0.995 \| 0.9832 \| -1.0 \| 0.6037 \| 0.9486 \| 0.5016 \| 0.9665 \| 0.9675 \| -1.0 \| 0.7182 \| 0.969 \| 0.9657 \| 0.9787 \| 0.9285 \| 0.9562 \| \| 0.1822 \| 75.0 \| 16350 \| 0.1673 \| 0.9424 \| 0.9901 \| 0.9845 \| -1.0 \| 0.2102 \| 0.9452 \| 0.4995 \| 0.9619 \| 0.9628 \| -1.0 \| 0.3667 \| 0.9656 \| 0.9615 \| 0.976 \| 0.9232 \| 0.9496 \| \| 0.1707 \| 76.0 \| 16568 \| 0.1508 \| 0.9457 \| 0.9885 \| 0.9816 \| -1.0 \| 0.1736 \| 0.9543 \| 0.5016 \| 0.9636 \| 0.9641 \| -1.0 \| 0.1971 \| 0.9705 \| 0.9577 \| 0.9745 \| 0.9338 \| 0.9538 \| \| 0.1707 \| 77.0 \| 16786 \| 0.1395 \| 0.9501 \| 0.9898 \| 0.9839 \| -1.0 \| 0.1955 \| 0.9581 \| 0.5034 \| 0.9681 \| 0.9687 \| -1.0 \| 0.2324 \| 0.9745 \| 0.9667 \| 0.9809 \| 0.9334 \| 0.9566 \| \| 0.1655 \| 78.0 \| 17004 \| 0.1451 \| 0.9502 \| 0.99 \| 0.9887 \| 0.0 \| 0.3398 \| 0.9525 \| 0.5042 \| 0.97 \| 0.9702 \| 0.0 \| 0.3667 \| 0.9726 \| 0.9611 \| 0.9771 \| 0.9393 \| 0.9632 \| \| 0.1655 \| 79.0 \| 17222 \| 0.1654 \| 0.9385 \| 0.9949 \| 0.9889 \| -1.0 \| 0.5696 \| 0.9406 \| 0.4997 \| 0.9592 \| 0.9599 \| -1.0 \| 0.6625 \| 0.9627 \| 0.9582 \| 0.9724 \| 0.9188 \| 0.9474 \| \| 0.1655 \| 80.0 \| 17440 \| 0.1388 \| 0.9536 \| 0.99 \| 0.9844 \| -1.0 \| 0.2026 \| 0.9602 \| 0.5051 \| 0.9689 \| 0.9694 \| -1.0 \| 0.2536 \| 0.9739 \| 0.9647 \| 0.979 \| 0.9425 \| 0.9599 \| \| 0.164 \| 81.0 \| 17658 \| 0.1296 \| 0.957 \| 0.9901 \| 0.9894 \| -1.0 \| 0.2818 \| 0.9625 \| 0.505 \| 0.9723 \| 0.9729 \| -1.0 \| 0.3042 \| 0.9767 \| 0.9681 \| 0.9801 \| 0.946 \| 0.9657 \| \| 0.164 \| 82.0 \| 17876 \| 0.1309 \| 0.9519 \| 0.9901 \| 0.9847 \| -1.0 \| 0.2714 \| 0.9584 \| 0.5047 \| 0.9707 \| 0.9709 \| -1.0 \| 0.3 \| 0.9746 \| 0.9635 \| 0.9791 \| 0.9403 \| 0.9627 \| \| 0.1556 \| 83.0 \| 18094 \| 0.1295 \| 0.9564 \| 0.9901 \| 0.9839 \| -1.0 \| 0.2769 \| 0.9594 \| 0.505 \| 0.9729 \| 0.9731 \| -1.0 \| 0.2833 \| 0.9767 \| 0.9644 \| 0.981 \| 0.9483 \| 0.9653 \| \| 0.1556 \| 84.0 \| 18312 \| 0.1259 \| 0.9572 \| 0.9899 \| 0.9846 \| 0.0 \| 0.2512 \| 0.963 \| 0.5059 \| 0.973 \| 0.9736 \| 0.0 \| 0.2786 \| 0.9783 \| 0.9671 \| 0.9812 \| 0.9473 \| 0.966 \| \| 0.1436 \| 85.0 \| 18530 \| 0.1465 \| 0.9517 \| 0.99 \| 0.9842 \| -1.0 \| 0.2796 \| 0.9544 \| 0.5025 \| 0.9679 \| 0.9685 \| -1.0 \| 0.3 \| 0.972 \| 0.9611 \| 0.9762 \| 0.9423 \| 0.9608 \| \| 0.1436 \| 86.0 \| 18748 \| 0.1230 \| 0.9577 \| 0.9899 \| 0.9847 \| -1.0 \| 0.3562 \| 0.9618 \| 0.5064 \| 0.9733 \| 0.9735 \| -1.0 \| 0.3773 \| 0.9755 \| 0.9689 \| 0.9826 \| 0.9465 \| 0.9644 \| \| 0.1436 \| 87.0 \| 18966 \| 0.1319 \| 0.9608 \| 0.99 \| 0.9823 \| 0.0 \| 0.3379 \| 0.967 \| 0.506 \| 0.9741 \| 0.9752 \| 0.0 \| 0.38 \| 0.9805 \| 0.9695 \| 0.9814 \| 0.952 \| 0.969 \| \| 0.1514 \| 88.0 \| 19184 \| 0.1253 \| 0.9554 \| 0.9901 \| 0.985 \| 0.0 \| 0.262 \| 0.9628 \| 0.5063 \| 0.9725 \| 0.9727 \| 0.0 \| 0.3231 \| 0.9765 \| 0.9624 \| 0.9782 \| 0.9484 \| 0.9673 \| \| 0.1514 \| 89.0 \| 19402 \| 0.1261 \| 0.9561 \| 0.9901 \| 0.985 \| -1.0 \| 0.2761 \| 0.9602 \| 0.5057 \| 0.9719 \| 0.9722 \| -1.0 \| 0.3125 \| 0.9753 \| 0.9658 \| 0.9791 \| 0.9464 \| 0.9653 \| \| 0.1481 \| 90.0 \| 19620 \| 0.1187 \| 0.9579 \| 0.9901 \| 0.9901 \| -1.0 \| 0.5624 \| 0.9604 \| 0.5069 \| 0.9752 \| 0.9754 \| -1.0 \| 0.5875 \| 0.9769 \| 0.9669 \| 0.9809 \| 0.9489 \| 0.9699 \| \| 0.1481 \| 91.0 \| 19838 \| 0.1352 \| 0.9513 \| 0.9901 \| 0.9836 \| 0.0 \| 0.3876 \| 0.9532 \| 0.5016 \| 0.97 \| 0.9704 \| 0.0 \| 0.515 \| 0.9733 \| 0.9618 \| 0.9774 \| 0.9408 \| 0.9634 \| \| 0.1443 \| 92.0 \| 20056 \| 0.1253 \| 0.9586 \| 0.9901 \| 0.9846 \| -1.0 \| 0.2831 \| 0.9643 \| 0.5055 \| 0.9749 \| 0.9753 \| -1.0 \| 0.3292 \| 0.9782 \| 0.9679 \| 0.9818 \| 0.9494 \| 0.9688 \| \| 0.1443 \| 93.0 \| 20274 \| 0.1259 \| 0.9598 \| 0.99 \| 0.9845 \| -1.0 \| 0.3228 \| 0.9639 \| 0.5055 \| 0.9736 \| 0.9745 \| -1.0 \| 0.3636 \| 0.9768 \| 0.9707 \| 0.9823 \| 0.9489 \| 0.9667 \| \| 0.1443 \| 94.0 \| 20492 \| 0.1257 \| 0.9639 \| 0.99 \| 0.985 \| -1.0 \| 0.3265 \| 0.9672 \| 0.5055 \| 0.9766 \| 0.9769 \| -1.0 \| 0.3625 \| 0.9793 \| 0.972 \| 0.9831 \| 0.9557 \| 0.9707 \| \| 0.1447 \| 95.0 \| 20710 \| 0.1168 \| 0.9606 \| 0.9901 \| 0.9842 \| 0.0 \| 0.2617 \| 0.9662 \| 0.5064 \| 0.9759 \| 0.9762 \| 0.0 \| 0.3 \| 0.9801 \| 0.9706 \| 0.9836 \| 0.9506 \| 0.9688 \| \| 0.1447 \| 96.0 \| 20928 \| 0.1196 \| 0.9625 \| 0.99 \| 0.9847 \| 0.0 \| 0.3062 \| 0.9677 \| 0.5078 \| 0.9769 \| 0.9776 \| 0.0 \| 0.3273 \| 0.9809 \| 0.9711 \| 0.9844 \| 0.9538 \| 0.9707 \| \| 0.1478 \| 97.0 \| 21146 \| 0.1220 \| 0.9558 \| 0.9901 \| 0.9843 \| -1.0 \| 0.2922 \| 0.9596 \| 0.5057 \| 0.9729 \| 0.9735 \| -1.0 \| 0.3071 \| 0.9771 \| 0.9661 \| 0.981 \| 0.9455 \| 0.966 \| \| 0.1478 \| 98.0 \| 21364 \| 0.1339 \| 0.9503 \| 0.9872 \| 0.9788 \| 0.0 \| 0.2824 \| 0.9558 \| 0.5034 \| 0.9663 \| 0.9672 \| 0.0 \| 0.375 \| 0.9717 \| 0.9655 \| 0.9778 \| 0.9351 \| 0.9565 \| \| 0.1421 \| 99.0 \| 21582 \| 0.1183 \| 0.9625 \| 0.9901 \| 0.9845 \| -1.0 \| 0.304 \| 0.9682 \| 0.509 \| 0.9765 \| 0.9771 \| -1.0 \| 0.3875 \| 0.9807 \| 0.9736 \| 0.9847 \| 0.9514 \| 0.9695 \| \| 0.1421 \| 100.0 \| 21800 \| 0.1249 \| 0.9571 \| 0.9901 \| 0.9844 \| -1.0 \| 0.3322 \| 0.9614 \| 0.5052 \| 0.9726 \| 0.9733 \| -1.0 \| 0.3654 \| 0.9759 \| 0.9657 \| 0.9777 \| 0.9486 \| 0.969 \| ### Framework versions - Transformers 4.42.3 - Pytorch 2.3.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1	[ "cashier", "cx" ]
ArrayDice/detr_finetuned_cppe5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr_finetuned_cppe5 This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3461 - Map: 0.2811 - Map 50: 0.561 - Map 75: 0.239 - Map Small: 0.0945 - Map Medium: 0.2317 - Map Large: 0.419 - Mar 1: 0.2736 - Mar 10: 0.4208 - Mar 100: 0.4388 - Mar Small: 0.2191 - Mar Medium: 0.3871 - Mar Large: 0.598 - Map Coverall: 0.5394 - Mar 100 Coverall: 0.6554 - Map Face Shield: 0.2284 - Mar 100 Face Shield: 0.4405 - Map Gloves: 0.1791 - Mar 100 Gloves: 0.3598 - Map Goggles: 0.1786 - Mar 100 Goggles: 0.3354 - Map Mask: 0.2802 - Mar 100 Mask: 0.4027 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Small \| Map Medium \| Map Large \| Mar 1 \| Mar 10 \| Mar 100 \| Mar Small \| Mar Medium \| Mar Large \| Map Coverall \| Mar 100 Coverall \| Map Face Shield \| Mar 100 Face Shield \| Map Gloves \| Mar 100 Gloves \| Map Goggles \| Mar 100 Goggles \| Map Mask \| Mar 100 Mask \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:---------:\|:----------:\|:---------:\|:------------:\|:----------------:\|:---------------:\|:-------------------:\|:----------:\|:--------------:\|:-----------:\|:---------------:\|:--------:\|:------------:\| \| No log \| 1.0 \| 107 \| 2.4496 \| 0.0083 \| 0.0289 \| 0.0024 \| 0.0047 \| 0.01 \| 0.0114 \| 0.019 \| 0.0813 \| 0.1191 \| 0.0915 \| 0.1324 \| 0.1166 \| 0.0124 \| 0.136 \| 0.0099 \| 0.1278 \| 0.0067 \| 0.1192 \| 0.0 \| 0.0 \| 0.0124 \| 0.2124 \| \| No log \| 2.0 \| 214 \| 2.2684 \| 0.0299 \| 0.0782 \| 0.0173 \| 0.0074 \| 0.0189 \| 0.0401 \| 0.0639 \| 0.1661 \| 0.1995 \| 0.0976 \| 0.1604 \| 0.2428 \| 0.0954 \| 0.391 \| 0.0145 \| 0.1772 \| 0.0077 \| 0.1871 \| 0.0 \| 0.0 \| 0.0322 \| 0.2422 \| \| No log \| 3.0 \| 321 \| 2.0298 \| 0.0535 \| 0.1159 \| 0.0446 \| 0.0097 \| 0.0477 \| 0.0618 \| 0.0981 \| 0.2257 \| 0.268 \| 0.133 \| 0.2193 \| 0.3214 \| 0.1778 \| 0.5667 \| 0.0256 \| 0.2215 \| 0.0099 \| 0.2138 \| 0.0008 \| 0.0323 \| 0.0533 \| 0.3058 \| \| No log \| 4.0 \| 428 \| 1.8945 \| 0.0859 \| 0.1853 \| 0.0702 \| 0.0334 \| 0.0778 \| 0.0961 \| 0.1189 \| 0.293 \| 0.3341 \| 0.1919 \| 0.2761 \| 0.4265 \| 0.2585 \| 0.6189 \| 0.049 \| 0.3177 \| 0.0166 \| 0.2567 \| 0.0041 \| 0.1538 \| 0.1011 \| 0.3231 \| \| 3.4087 \| 5.0 \| 535 \| 1.8465 \| 0.0983 \| 0.2227 \| 0.0737 \| 0.031 \| 0.0773 \| 0.1063 \| 0.1557 \| 0.308 \| 0.3463 \| 0.1614 \| 0.2826 \| 0.486 \| 0.3287 \| 0.6045 \| 0.0574 \| 0.3468 \| 0.036 \| 0.2786 \| 0.0086 \| 0.2369 \| 0.061 \| 0.2644 \| \| 3.4087 \| 6.0 \| 642 \| 1.7933 \| 0.1135 \| 0.2609 \| 0.0872 \| 0.029 \| 0.0843 \| 0.1564 \| 0.1418 \| 0.3147 \| 0.3511 \| 0.1587 \| 0.2775 \| 0.5219 \| 0.382 \| 0.6018 \| 0.0429 \| 0.3215 \| 0.0267 \| 0.2728 \| 0.011 \| 0.2169 \| 0.1048 \| 0.3427 \| \| 3.4087 \| 7.0 \| 749 \| 1.7358 \| 0.128 \| 0.2893 \| 0.1046 \| 0.0681 \| 0.1009 \| 0.1695 \| 0.1657 \| 0.3336 \| 0.371 \| 0.202 \| 0.3124 \| 0.5316 \| 0.3881 \| 0.5892 \| 0.0586 \| 0.3747 \| 0.0395 \| 0.3089 \| 0.0332 \| 0.2569 \| 0.1203 \| 0.3253 \| \| 3.4087 \| 8.0 \| 856 \| 1.6967 \| 0.1554 \| 0.346 \| 0.1184 \| 0.0488 \| 0.1298 \| 0.2155 \| 0.1863 \| 0.3443 \| 0.372 \| 0.1588 \| 0.3188 \| 0.5313 \| 0.4219 \| 0.5959 \| 0.0906 \| 0.3658 \| 0.0653 \| 0.3058 \| 0.0395 \| 0.2569 \| 0.1595 \| 0.3356 \| \| 3.4087 \| 9.0 \| 963 \| 1.6399 \| 0.1636 \| 0.3504 \| 0.1231 \| 0.0682 \| 0.139 \| 0.2298 \| 0.1889 \| 0.3603 \| 0.3918 \| 0.2191 \| 0.3224 \| 0.5498 \| 0.418 \| 0.5883 \| 0.0996 \| 0.4038 \| 0.0647 \| 0.3232 \| 0.0303 \| 0.2708 \| 0.2056 \| 0.3729 \| \| 1.5778 \| 10.0 \| 1070 \| 1.5569 \| 0.1838 \| 0.3943 \| 0.143 \| 0.0643 \| 0.1485 \| 0.2628 \| 0.2179 \| 0.3785 \| 0.3994 \| 0.2143 \| 0.3385 \| 0.5573 \| 0.4564 \| 0.6185 \| 0.1166 \| 0.3899 \| 0.091 \| 0.3165 \| 0.0554 \| 0.3108 \| 0.1994 \| 0.3613 \| \| 1.5778 \| 11.0 \| 1177 \| 1.5197 \| 0.1939 \| 0.4116 \| 0.1664 \| 0.081 \| 0.1464 \| 0.2871 \| 0.2228 \| 0.3794 \| 0.4032 \| 0.1872 \| 0.3522 \| 0.5597 \| 0.4738 \| 0.6203 \| 0.1204 \| 0.4089 \| 0.0977 \| 0.3058 \| 0.0806 \| 0.3231 \| 0.197 \| 0.3582 \| \| 1.5778 \| 12.0 \| 1284 \| 1.4805 \| 0.2164 \| 0.466 \| 0.1818 \| 0.0903 \| 0.1561 \| 0.3301 \| 0.2355 \| 0.3812 \| 0.4044 \| 0.1933 \| 0.3425 \| 0.5688 \| 0.4865 \| 0.6279 \| 0.1528 \| 0.4418 \| 0.122 \| 0.321 \| 0.0801 \| 0.28 \| 0.2405 \| 0.3511 \| \| 1.5778 \| 13.0 \| 1391 \| 1.4775 \| 0.216 \| 0.4679 \| 0.1811 \| 0.096 \| 0.1596 \| 0.3276 \| 0.2327 \| 0.3889 \| 0.4121 \| 0.2207 \| 0.3467 \| 0.5831 \| 0.4643 \| 0.6054 \| 0.1596 \| 0.438 \| 0.1273 \| 0.3299 \| 0.0984 \| 0.3231 \| 0.2303 \| 0.364 \| \| 1.5778 \| 14.0 \| 1498 \| 1.4550 \| 0.2185 \| 0.4864 \| 0.16 \| 0.0813 \| 0.1483 \| 0.3481 \| 0.2396 \| 0.3863 \| 0.409 \| 0.2272 \| 0.3341 \| 0.5899 \| 0.4974 \| 0.6428 \| 0.1488 \| 0.4203 \| 0.143 \| 0.325 \| 0.0954 \| 0.2985 \| 0.2082 \| 0.3587 \| \| 1.3087 \| 15.0 \| 1605 \| 1.4381 \| 0.2388 \| 0.4949 \| 0.2116 \| 0.0844 \| 0.184 \| 0.3583 \| 0.2521 \| 0.3961 \| 0.4194 \| 0.1964 \| 0.3474 \| 0.5867 \| 0.5148 \| 0.6432 \| 0.1774 \| 0.4228 \| 0.1375 \| 0.3152 \| 0.1241 \| 0.3385 \| 0.2401 \| 0.3773 \| \| 1.3087 \| 16.0 \| 1712 \| 1.4094 \| 0.2482 \| 0.5241 \| 0.2015 \| 0.0931 \| 0.1922 \| 0.3754 \| 0.2535 \| 0.4039 \| 0.4216 \| 0.2291 \| 0.3709 \| 0.576 \| 0.5054 \| 0.6419 \| 0.1876 \| 0.4114 \| 0.1622 \| 0.3393 \| 0.1283 \| 0.3338 \| 0.2578 \| 0.3818 \| \| 1.3087 \| 17.0 \| 1819 \| 1.4044 \| 0.2528 \| 0.5311 \| 0.2088 \| 0.096 \| 0.2052 \| 0.3609 \| 0.2595 \| 0.4056 \| 0.4228 \| 0.223 \| 0.3795 \| 0.5587 \| 0.516 \| 0.6396 \| 0.2009 \| 0.4177 \| 0.1449 \| 0.3384 \| 0.143 \| 0.3354 \| 0.2593 \| 0.3831 \| \| 1.3087 \| 18.0 \| 1926 \| 1.3968 \| 0.2581 \| 0.5282 \| 0.2107 \| 0.101 \| 0.2083 \| 0.3787 \| 0.2659 \| 0.4174 \| 0.4363 \| 0.2289 \| 0.3908 \| 0.5821 \| 0.5132 \| 0.6387 \| 0.213 \| 0.4797 \| 0.1584 \| 0.3371 \| 0.1375 \| 0.3431 \| 0.2682 \| 0.3827 \| \| 1.1363 \| 19.0 \| 2033 \| 1.3735 \| 0.2592 \| 0.5349 \| 0.2235 \| 0.0869 \| 0.2076 \| 0.3936 \| 0.2661 \| 0.4127 \| 0.4335 \| 0.2159 \| 0.3829 \| 0.5907 \| 0.5222 \| 0.6414 \| 0.2115 \| 0.4468 \| 0.1608 \| 0.3549 \| 0.1408 \| 0.3431 \| 0.2609 \| 0.3813 \| \| 1.1363 \| 20.0 \| 2140 \| 1.3686 \| 0.266 \| 0.5447 \| 0.2204 \| 0.0897 \| 0.2138 \| 0.3875 \| 0.268 \| 0.4117 \| 0.4326 \| 0.2102 \| 0.3875 \| 0.5781 \| 0.5323 \| 0.6446 \| 0.1933 \| 0.4418 \| 0.1727 \| 0.3558 \| 0.1537 \| 0.3246 \| 0.278 \| 0.396 \| \| 1.1363 \| 21.0 \| 2247 \| 1.3672 \| 0.2659 \| 0.5446 \| 0.2266 \| 0.0859 \| 0.2198 \| 0.3884 \| 0.2687 \| 0.4164 \| 0.4339 \| 0.2135 \| 0.3914 \| 0.5779 \| 0.5341 \| 0.6505 \| 0.1966 \| 0.419 \| 0.1624 \| 0.3513 \| 0.1698 \| 0.3554 \| 0.2668 \| 0.3933 \| \| 1.1363 \| 22.0 \| 2354 \| 1.3658 \| 0.2731 \| 0.5472 \| 0.2303 \| 0.095 \| 0.2174 \| 0.4119 \| 0.27 \| 0.4179 \| 0.4383 \| 0.202 \| 0.3904 \| 0.5994 \| 0.5311 \| 0.6473 \| 0.2131 \| 0.4494 \| 0.179 \| 0.3545 \| 0.1672 \| 0.3492 \| 0.2754 \| 0.3911 \| \| 1.1363 \| 23.0 \| 2461 \| 1.3644 \| 0.2692 \| 0.5458 \| 0.228 \| 0.0937 \| 0.2188 \| 0.4008 \| 0.2681 \| 0.4205 \| 0.4394 \| 0.2104 \| 0.3876 \| 0.6031 \| 0.534 \| 0.6518 \| 0.2028 \| 0.4418 \| 0.175 \| 0.3594 \| 0.1635 \| 0.3477 \| 0.2708 \| 0.3964 \| \| 1.0307 \| 24.0 \| 2568 \| 1.3586 \| 0.269 \| 0.5419 \| 0.2327 \| 0.0933 \| 0.2161 \| 0.4067 \| 0.2699 \| 0.4208 \| 0.439 \| 0.2168 \| 0.392 \| 0.5983 \| 0.5384 \| 0.6527 \| 0.2045 \| 0.443 \| 0.1728 \| 0.3536 \| 0.1581 \| 0.3508 \| 0.2709 \| 0.3951 \| \| 1.0307 \| 25.0 \| 2675 \| 1.3503 \| 0.2788 \| 0.5573 \| 0.239 \| 0.0992 \| 0.2301 \| 0.4157 \| 0.273 \| 0.4225 \| 0.4416 \| 0.2261 \| 0.388 \| 0.6083 \| 0.5361 \| 0.6581 \| 0.2253 \| 0.4519 \| 0.1805 \| 0.3634 \| 0.1742 \| 0.3338 \| 0.2779 \| 0.4009 \| \| 1.0307 \| 26.0 \| 2782 \| 1.3441 \| 0.2803 \| 0.5605 \| 0.2394 \| 0.0922 \| 0.2291 \| 0.4206 \| 0.2702 \| 0.4213 \| 0.4406 \| 0.2174 \| 0.387 \| 0.6034 \| 0.5392 \| 0.6604 \| 0.2319 \| 0.4481 \| 0.1831 \| 0.3634 \| 0.1761 \| 0.3369 \| 0.2711 \| 0.3942 \| \| 1.0307 \| 27.0 \| 2889 \| 1.3461 \| 0.2811 \| 0.5603 \| 0.2465 \| 0.0939 \| 0.23 \| 0.4185 \| 0.2741 \| 0.4231 \| 0.4418 \| 0.2217 \| 0.3879 \| 0.6041 \| 0.537 \| 0.6545 \| 0.2329 \| 0.4506 \| 0.181 \| 0.3638 \| 0.1792 \| 0.3385 \| 0.2753 \| 0.4013 \| \| 1.0307 \| 28.0 \| 2996 \| 1.3462 \| 0.2799 \| 0.5611 \| 0.2378 \| 0.0943 \| 0.231 \| 0.4151 \| 0.2732 \| 0.4211 \| 0.4392 \| 0.2194 \| 0.3863 \| 0.5988 \| 0.5376 \| 0.655 \| 0.2291 \| 0.4456 \| 0.179 \| 0.3616 \| 0.176 \| 0.3323 \| 0.2775 \| 0.4013 \| \| 0.9602 \| 29.0 \| 3103 \| 1.3458 \| 0.2811 \| 0.5599 \| 0.2393 \| 0.094 \| 0.2311 \| 0.4197 \| 0.2734 \| 0.4208 \| 0.4385 \| 0.2191 \| 0.3862 \| 0.5986 \| 0.5393 \| 0.6559 \| 0.2282 \| 0.4405 \| 0.1785 \| 0.3594 \| 0.1788 \| 0.3338 \| 0.2806 \| 0.4031 \| \| 0.9602 \| 30.0 \| 3210 \| 1.3461 \| 0.2811 \| 0.561 \| 0.239 \| 0.0945 \| 0.2317 \| 0.419 \| 0.2736 \| 0.4208 \| 0.4388 \| 0.2191 \| 0.3871 \| 0.598 \| 0.5394 \| 0.6554 \| 0.2284 \| 0.4405 \| 0.1791 \| 0.3598 \| 0.1786 \| 0.3354 \| 0.2802 \| 0.4027 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
nsugianto/tblstructrecog_tuned_tbltransstrucrecog_noncomplex_complex_conlash_b5_1807s_lr5e5_dec1e4_bs8	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tblstructrecog_tuned_tbltransstrucrecog_noncomplex_complex_conlash_b5_1807s_lr5e5_dec1e4_bs8 This model is a fine-tuned version of [nsugianto/tblstructrecog_tuned_tbltransstrucrecog_noncomplex_complex_conlash_b5_1807s_lr1e6_dec1e5_bs4](https://huggingface.co/nsugianto/tblstructrecog_tuned_tbltransstrucrecog_noncomplex_complex_conlash_b5_1807s_lr1e6_dec1e5_bs4) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 750 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.19.1	[ "table", "table column", "table row", "table column header", "table projected row header", "table spanning cell" ]
schoonhovenra/20240704	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 20240704 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2392 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 0.4262 \| 32.0 \| 800 \| 0.3110 \| \| 0.3444 \| 64.0 \| 1600 \| 0.2795 \| \| 0.3122 \| 96.0 \| 2400 \| 0.2704 \| \| 0.2893 \| 128.0 \| 3200 \| 0.2639 \| \| 0.2823 \| 160.0 \| 4000 \| 0.2518 \| \| 0.2679 \| 192.0 \| 4800 \| 0.2502 \| \| 0.2491 \| 224.0 \| 5600 \| 0.2444 \| \| 0.2354 \| 256.0 \| 6400 \| 0.2495 \| \| 0.2284 \| 288.0 \| 7200 \| 0.2406 \| \| 0.2318 \| 320.0 \| 8000 \| 0.2453 \| \| 0.217 \| 352.0 \| 8800 \| 0.2432 \| \| 0.2124 \| 384.0 \| 9600 \| 0.2469 \| \| 0.2097 \| 416.0 \| 10400 \| 0.2516 \| \| 0.1998 \| 448.0 \| 11200 \| 0.2428 \| \| 0.1944 \| 480.0 \| 12000 \| 0.2445 \| \| 0.1909 \| 512.0 \| 12800 \| 0.2468 \| \| 0.1952 \| 544.0 \| 13600 \| 0.2401 \| \| 0.1903 \| 576.0 \| 14400 \| 0.2408 \| \| 0.1867 \| 608.0 \| 15200 \| 0.2478 \| \| 0.1845 \| 640.0 \| 16000 \| 0.2443 \| \| 0.179 \| 672.0 \| 16800 \| 0.2432 \| \| 0.1744 \| 704.0 \| 17600 \| 0.2410 \| \| 0.1747 \| 736.0 \| 18400 \| 0.2432 \| \| 0.1674 \| 768.0 \| 19200 \| 0.2386 \| \| 0.166 \| 800.0 \| 20000 \| 0.2384 \| \| 0.1686 \| 832.0 \| 20800 \| 0.2399 \| \| 0.1647 \| 864.0 \| 21600 \| 0.2391 \| \| 0.1599 \| 896.0 \| 22400 \| 0.2398 \| \| 0.1633 \| 928.0 \| 23200 \| 0.2401 \| \| 0.1654 \| 960.0 \| 24000 \| 0.2397 \| \| 0.1632 \| 992.0 \| 24800 \| 0.2392 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.3.0 - Datasets 2.12.0 - Tokenizers 0.15.1	[ "back mirror", "backseat", "center console", "dashboard", "door front left", "door front right", "driving wheel", "window front left", "window front right" ]
monica1106/detr-resnet-50	# DETR (End-to-End Object Detection) model with ResNet-50 backbone DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Carion et al. and first released in [this repository](https://github.com/facebookresearch/detr). Disclaimer: The team releasing DETR did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100. The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/detr_architecture.png) ## Intended uses & limitations You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=facebook/detr) to look for all available DETR models. ### How to use Here is how to use this model: ```python from transformers import DetrImageProcessor, DetrForObjectDetection import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) # you can specify the revision tag if you don't want the timm dependency processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50", revision="no_timm") model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50", revision="no_timm") inputs = processor(images=image, return_tensors="pt") outputs = model(inputs) # convert outputs (bounding boxes and class logits) to COCO API # let's only keep detections with score > 0.9 target_sizes = torch.tensor([image.size[::-1]]) results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0] for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): box = [round(i, 2) for i in box.tolist()] print( f"Detected {model.config.id2label[label.item()]} with confidence " f"{round(score.item(), 3)} at location {box}" ) ``` This should output: ``` Detected remote with confidence 0.998 at location [40.16, 70.81, 175.55, 117.98] Detected remote with confidence 0.996 at location [333.24, 72.55, 368.33, 187.66] Detected couch with confidence 0.995 at location [-0.02, 1.15, 639.73, 473.76] Detected cat with confidence 0.999 at location [13.24, 52.05, 314.02, 470.93] Detected cat with confidence 0.999 at location [345.4, 23.85, 640.37, 368.72] ``` Currently, both the feature extractor and model support PyTorch. ## Training data The DETR model was trained on [COCO 2017 object detection](https://cocodataset.org/#download), a dataset consisting of 118k/5k annotated images for training/validation respectively. ## Training procedure ### Preprocessing The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py). Images are resized/rescaled such that the shortest side is at least 800 pixels and the largest side at most 1333 pixels, and normalized across the RGB channels with the ImageNet mean (0.485, 0.456, 0.406) and standard deviation (0.229, 0.224, 0.225). ### Training The model was trained for 300 epochs on 16 V100 GPUs. This takes 3 days, with 4 images per GPU (hence a total batch size of 64). ## Evaluation results This model achieves an AP (average precision) of 42.0** on COCO 2017 validation. For more details regarding evaluation results, we refer to table 1 of the original paper. ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2005-12872, author = {Nicolas Carion and Francisco Massa and Gabriel Synnaeve and Nicolas Usunier and Alexander Kirillov and Sergey Zagoruyko}, title = {End-to-End Object Detection with Transformers}, journal = {CoRR}, volume = {abs/2005.12872}, year = {2020}, url = {https://arxiv.org/abs/2005.12872}, archivePrefix = {arXiv}, eprint = {2005.12872}, timestamp = {Thu, 28 May 2020 17:38:09 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2005-12872.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```	[ "mature", "semi_mature", "unmature", "clod", "mold", "rotten" ]
machinelearningzuu/queue_detection_cctv	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # queue_detection_cctv This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1291 - Map: 0.9532 - Map 50: 0.9901 - Map 75: 0.9845 - Map Small: -1.0 - Map Medium: 0.3203 - Map Large: 0.9578 - Mar 1: 0.5044 - Mar 10: 0.9715 - Mar 100: 0.972 - Mar Small: -1.0 - Mar Medium: 0.3538 - Mar Large: 0.9747 - Map Cashier: 0.9618 - Mar 100 Cashier: 0.9775 - Map Cx: 0.9447 - Mar 100 Cx: 0.9664 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Small \| Map Medium \| Map Large \| Mar 1 \| Mar 10 \| Mar 100 \| Mar Small \| Mar Medium \| Mar Large \| Map Cashier \| Mar 100 Cashier \| Map Cx \| Mar 100 Cx \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:---------:\|:----------:\|:---------:\|:-----------:\|:---------------:\|:------:\|:----------:\| \| No log \| 1.0 \| 218 \| 1.3927 \| 0.1975 \| 0.3459 \| 0.1995 \| -1.0 \| 0.0 \| 0.1988 \| 0.2409 \| 0.5283 \| 0.7011 \| -1.0 \| 0.0 \| 0.7055 \| 0.2115 \| 0.8043 \| 0.1834 \| 0.5979 \| \| No log \| 2.0 \| 436 \| 0.9964 \| 0.5247 \| 0.8011 \| 0.591 \| -1.0 \| 0.0079 \| 0.5292 \| 0.3316 \| 0.6966 \| 0.7387 \| -1.0 \| 0.0071 \| 0.7453 \| 0.5772 \| 0.8086 \| 0.4723 \| 0.6688 \| \| 2.7418 \| 3.0 \| 654 \| 0.8535 \| 0.6031 \| 0.9058 \| 0.6954 \| -1.0 \| 0.0349 \| 0.6069 \| 0.3603 \| 0.7079 \| 0.733 \| -1.0 \| 0.2 \| 0.7362 \| 0.6576 \| 0.769 \| 0.5485 \| 0.6969 \| \| 2.7418 \| 4.0 \| 872 \| 0.7406 \| 0.6499 \| 0.9356 \| 0.752 \| -1.0 \| 0.0479 \| 0.6543 \| 0.3756 \| 0.7387 \| 0.7586 \| -1.0 \| 0.0923 \| 0.7634 \| 0.7052 \| 0.7953 \| 0.5947 \| 0.7219 \| \| 0.8155 \| 5.0 \| 1090 \| 0.6721 \| 0.6731 \| 0.9516 \| 0.8113 \| -1.0 \| 0.0249 \| 0.6773 \| 0.3819 \| 0.7501 \| 0.7654 \| -1.0 \| 0.0455 \| 0.7701 \| 0.7451 \| 0.8203 \| 0.601 \| 0.7105 \| \| 0.8155 \| 6.0 \| 1308 \| 0.5804 \| 0.7244 \| 0.9632 \| 0.8738 \| 0.0 \| 0.0712 \| 0.7288 \| 0.4038 \| 0.7882 \| 0.8023 \| 0.0 \| 0.1731 \| 0.8066 \| 0.7818 \| 0.8419 \| 0.6671 \| 0.7627 \| \| 0.6668 \| 7.0 \| 1526 \| 0.5430 \| 0.7484 \| 0.9667 \| 0.9041 \| -1.0 \| 0.076 \| 0.7527 \| 0.417 \| 0.8027 \| 0.813 \| -1.0 \| 0.2205 \| 0.8171 \| 0.8068 \| 0.8602 \| 0.69 \| 0.7658 \| \| 0.6668 \| 8.0 \| 1744 \| 0.5524 \| 0.7361 \| 0.9691 \| 0.8958 \| -1.0 \| 0.0273 \| 0.7416 \| 0.4045 \| 0.7839 \| 0.7933 \| -1.0 \| 0.1286 \| 0.7981 \| 0.7845 \| 0.8274 \| 0.6877 \| 0.7592 \| \| 0.6668 \| 9.0 \| 1962 \| 0.5359 \| 0.7415 \| 0.9737 \| 0.901 \| -1.0 \| 0.0845 \| 0.7462 \| 0.4112 \| 0.7999 \| 0.8044 \| -1.0 \| 0.1462 \| 0.8088 \| 0.7844 \| 0.8376 \| 0.6986 \| 0.7713 \| \| 0.5735 \| 10.0 \| 2180 \| 0.5154 \| 0.7497 \| 0.9744 \| 0.907 \| 0.0 \| 0.0368 \| 0.7538 \| 0.414 \| 0.8042 \| 0.8093 \| 0.0 \| 0.1333 \| 0.813 \| 0.8085 \| 0.86 \| 0.6909 \| 0.7586 \| \| 0.5735 \| 11.0 \| 2398 \| 0.4543 \| 0.7824 \| 0.9754 \| 0.9337 \| 0.0 \| 0.0709 \| 0.7908 \| 0.4307 \| 0.8323 \| 0.8368 \| 0.0 \| 0.1794 \| 0.8449 \| 0.8312 \| 0.8765 \| 0.7336 \| 0.7972 \| \| 0.5189 \| 12.0 \| 2616 \| 0.4802 \| 0.7679 \| 0.9769 \| 0.9274 \| 0.0 \| 0.1201 \| 0.7724 \| 0.426 \| 0.8197 \| 0.825 \| 0.0 \| 0.1917 \| 0.8291 \| 0.7985 \| 0.85 \| 0.7374 \| 0.8 \| \| 0.5189 \| 13.0 \| 2834 \| 0.4306 \| 0.7906 \| 0.9825 \| 0.9332 \| -1.0 \| 0.0708 \| 0.7941 \| 0.435 \| 0.8394 \| 0.8448 \| -1.0 \| 0.23 \| 0.8474 \| 0.8474 \| 0.889 \| 0.7339 \| 0.8006 \| \| 0.4874 \| 14.0 \| 3052 \| 0.4660 \| 0.7649 \| 0.9818 \| 0.9264 \| -1.0 \| 0.0504 \| 0.7713 \| 0.4219 \| 0.8155 \| 0.8222 \| -1.0 \| 0.0875 \| 0.8288 \| 0.805 \| 0.8527 \| 0.7248 \| 0.7917 \| \| 0.4874 \| 15.0 \| 3270 \| 0.4392 \| 0.7867 \| 0.9773 \| 0.9278 \| 0.0 \| 0.0256 \| 0.7961 \| 0.4372 \| 0.8336 \| 0.8385 \| 0.0 \| 0.1028 \| 0.8466 \| 0.8243 \| 0.8725 \| 0.7492 \| 0.8045 \| \| 0.4874 \| 16.0 \| 3488 \| 0.4178 \| 0.8018 \| 0.9847 \| 0.9355 \| -1.0 \| 0.2037 \| 0.8061 \| 0.4387 \| 0.8493 \| 0.8551 \| -1.0 \| 0.3714 \| 0.8589 \| 0.8394 \| 0.8881 \| 0.7641 \| 0.822 \| \| 0.4646 \| 17.0 \| 3706 \| 0.3859 \| 0.8138 \| 0.9838 \| 0.9502 \| -1.0 \| 0.1217 \| 0.8189 \| 0.4459 \| 0.8584 \| 0.863 \| -1.0 \| 0.2038 \| 0.8669 \| 0.8508 \| 0.8956 \| 0.7769 \| 0.8303 \| \| 0.4646 \| 18.0 \| 3924 \| 0.4041 \| 0.7987 \| 0.9822 \| 0.9457 \| -1.0 \| 0.097 \| 0.8032 \| 0.4378 \| 0.8486 \| 0.8518 \| -1.0 \| 0.1611 \| 0.8551 \| 0.8323 \| 0.881 \| 0.7652 \| 0.8226 \| \| 0.4317 \| 19.0 \| 4142 \| 0.4013 \| 0.8086 \| 0.9838 \| 0.9442 \| -1.0 \| 0.1816 \| 0.814 \| 0.4412 \| 0.8513 \| 0.8557 \| -1.0 \| 0.2571 \| 0.8605 \| 0.8522 \| 0.8919 \| 0.765 \| 0.8195 \| \| 0.4317 \| 20.0 \| 4360 \| 0.3869 \| 0.8123 \| 0.9823 \| 0.9388 \| -1.0 \| 0.1597 \| 0.8163 \| 0.4475 \| 0.8579 \| 0.8617 \| -1.0 \| 0.2042 \| 0.8653 \| 0.8542 \| 0.896 \| 0.7705 \| 0.8274 \| \| 0.4215 \| 21.0 \| 4578 \| 0.3721 \| 0.816 \| 0.9864 \| 0.9536 \| -1.0 \| 0.1206 \| 0.8198 \| 0.4478 \| 0.8598 \| 0.863 \| -1.0 \| 0.2727 \| 0.8655 \| 0.8607 \| 0.9003 \| 0.7713 \| 0.8258 \| \| 0.4215 \| 22.0 \| 4796 \| 0.3777 \| 0.8245 \| 0.9806 \| 0.9507 \| 0.0 \| 0.1034 \| 0.8324 \| 0.4537 \| 0.8621 \| 0.8649 \| 0.0 \| 0.2118 \| 0.8724 \| 0.8651 \| 0.9012 \| 0.7839 \| 0.8287 \| \| 0.3925 \| 23.0 \| 5014 \| 0.3387 \| 0.8411 \| 0.9872 \| 0.9577 \| -1.0 \| 0.1184 \| 0.845 \| 0.4593 \| 0.8775 \| 0.8799 \| -1.0 \| 0.2429 \| 0.8835 \| 0.8813 \| 0.9153 \| 0.8008 \| 0.8444 \| \| 0.3925 \| 24.0 \| 5232 \| 0.3234 \| 0.842 \| 0.9887 \| 0.9671 \| -1.0 \| 0.1229 \| 0.8463 \| 0.4604 \| 0.8794 \| 0.8812 \| -1.0 \| 0.1864 \| 0.885 \| 0.8736 \| 0.909 \| 0.8104 \| 0.8534 \| \| 0.3925 \| 25.0 \| 5450 \| 0.3463 \| 0.8356 \| 0.9869 \| 0.9556 \| -1.0 \| 0.0775 \| 0.8411 \| 0.4552 \| 0.8769 \| 0.8793 \| -1.0 \| 0.1929 \| 0.8838 \| 0.8788 \| 0.913 \| 0.7925 \| 0.8456 \| \| 0.3676 \| 26.0 \| 5668 \| 0.3170 \| 0.846 \| 0.988 \| 0.9666 \| 0.0 \| 0.1172 \| 0.8515 \| 0.4603 \| 0.886 \| 0.8872 \| 0.0 \| 0.285 \| 0.8907 \| 0.8831 \| 0.9182 \| 0.8089 \| 0.8562 \| \| 0.3676 \| 27.0 \| 5886 \| 0.3552 \| 0.8246 \| 0.9832 \| 0.9545 \| -1.0 \| 0.13 \| 0.8285 \| 0.4535 \| 0.8704 \| 0.8745 \| -1.0 \| 0.2367 \| 0.8785 \| 0.8559 \| 0.9005 \| 0.7932 \| 0.8484 \| \| 0.3669 \| 28.0 \| 6104 \| 0.3342 \| 0.8427 \| 0.9876 \| 0.9665 \| -1.0 \| 0.1369 \| 0.8468 \| 0.4585 \| 0.8813 \| 0.8843 \| -1.0 \| 0.2625 \| 0.8874 \| 0.8587 \| 0.898 \| 0.8267 \| 0.8707 \| \| 0.3669 \| 29.0 \| 6322 \| 0.3033 \| 0.854 \| 0.9892 \| 0.9687 \| -1.0 \| 0.1795 \| 0.8572 \| 0.4663 \| 0.8954 \| 0.8968 \| -1.0 \| 0.3 \| 0.8991 \| 0.8813 \| 0.9193 \| 0.8268 \| 0.8744 \| \| 0.349 \| 30.0 \| 6540 \| 0.3099 \| 0.8515 \| 0.9863 \| 0.9676 \| -1.0 \| 0.1251 \| 0.8571 \| 0.4666 \| 0.8917 \| 0.8936 \| -1.0 \| 0.2 \| 0.8978 \| 0.8868 \| 0.9261 \| 0.8162 \| 0.8611 \| \| 0.349 \| 31.0 \| 6758 \| 0.3247 \| 0.842 \| 0.9884 \| 0.963 \| 0.0 \| 0.1145 \| 0.8491 \| 0.4607 \| 0.8828 \| 0.8854 \| 0.0 \| 0.1462 \| 0.8916 \| 0.8704 \| 0.9104 \| 0.8137 \| 0.8605 \| \| 0.349 \| 32.0 \| 6976 \| 0.2943 \| 0.8529 \| 0.9887 \| 0.9651 \| -1.0 \| 0.1639 \| 0.8587 \| 0.4683 \| 0.8916 \| 0.8949 \| -1.0 \| 0.225 \| 0.8997 \| 0.89 \| 0.9246 \| 0.8158 \| 0.8653 \| \| 0.3378 \| 33.0 \| 7194 \| 0.2923 \| 0.8605 \| 0.989 \| 0.9695 \| -1.0 \| 0.1212 \| 0.8657 \| 0.4687 \| 0.8985 \| 0.9006 \| -1.0 \| 0.2136 \| 0.9042 \| 0.8893 \| 0.9257 \| 0.8317 \| 0.8756 \| \| 0.3378 \| 34.0 \| 7412 \| 0.2878 \| 0.8616 \| 0.9895 \| 0.9673 \| -1.0 \| 0.1464 \| 0.8665 \| 0.4712 \| 0.897 \| 0.899 \| -1.0 \| 0.2 \| 0.9036 \| 0.8907 \| 0.9246 \| 0.8325 \| 0.8734 \| \| 0.3206 \| 35.0 \| 7630 \| 0.3342 \| 0.837 \| 0.9866 \| 0.9674 \| -1.0 \| 0.1634 \| 0.8423 \| 0.4584 \| 0.8772 \| 0.8802 \| -1.0 \| 0.2611 \| 0.8844 \| 0.8684 \| 0.906 \| 0.8057 \| 0.8544 \| \| 0.3206 \| 36.0 \| 7848 \| 0.2796 \| 0.8713 \| 0.989 \| 0.9716 \| -1.0 \| 0.1054 \| 0.8759 \| 0.4699 \| 0.9066 \| 0.9084 \| -1.0 \| 0.15 \| 0.9128 \| 0.9052 \| 0.9373 \| 0.8373 \| 0.8795 \| \| 0.3152 \| 37.0 \| 8066 \| 0.2894 \| 0.8667 \| 0.987 \| 0.9746 \| 0.0 \| 0.1359 \| 0.8743 \| 0.4716 \| 0.9022 \| 0.9037 \| 0.0 \| 0.1667 \| 0.9109 \| 0.8966 \| 0.9309 \| 0.8367 \| 0.8765 \| \| 0.3152 \| 38.0 \| 8284 \| 0.2641 \| 0.8744 \| 0.9894 \| 0.9722 \| -1.0 \| 0.1413 \| 0.8793 \| 0.4727 \| 0.9132 \| 0.9148 \| -1.0 \| 0.2333 \| 0.9178 \| 0.8909 \| 0.9305 \| 0.858 \| 0.8992 \| \| 0.3082 \| 39.0 \| 8502 \| 0.2834 \| 0.8703 \| 0.9873 \| 0.9702 \| -1.0 \| 0.132 \| 0.8764 \| 0.473 \| 0.9082 \| 0.9128 \| -1.0 \| 0.2633 \| 0.9168 \| 0.8988 \| 0.9347 \| 0.8417 \| 0.891 \| \| 0.3082 \| 40.0 \| 8720 \| 0.2774 \| 0.8655 \| 0.9897 \| 0.9738 \| -1.0 \| 0.2021 \| 0.8711 \| 0.4694 \| 0.9025 \| 0.9043 \| -1.0 \| 0.275 \| 0.9081 \| 0.8971 \| 0.9314 \| 0.8339 \| 0.8772 \| \| 0.3082 \| 41.0 \| 8938 \| 0.2935 \| 0.8598 \| 0.988 \| 0.9699 \| -1.0 \| 0.0999 \| 0.8666 \| 0.4688 \| 0.8961 \| 0.8976 \| -1.0 \| 0.15 \| 0.9037 \| 0.8889 \| 0.9255 \| 0.8308 \| 0.8697 \| \| 0.3078 \| 42.0 \| 9156 \| 0.2746 \| 0.868 \| 0.9895 \| 0.9777 \| -1.0 \| 0.2159 \| 0.8738 \| 0.4712 \| 0.9021 \| 0.9032 \| -1.0 \| 0.275 \| 0.9079 \| 0.9016 \| 0.933 \| 0.8343 \| 0.8734 \| \| 0.3078 \| 43.0 \| 9374 \| 0.2662 \| 0.8731 \| 0.9897 \| 0.9798 \| -1.0 \| 0.1849 \| 0.8794 \| 0.4752 \| 0.9083 \| 0.9091 \| -1.0 \| 0.2 \| 0.9136 \| 0.888 \| 0.9206 \| 0.8582 \| 0.8975 \| \| 0.2898 \| 44.0 \| 9592 \| 0.2564 \| 0.8824 \| 0.9868 \| 0.9732 \| -1.0 \| 0.1263 \| 0.8871 \| 0.4775 \| 0.9148 \| 0.9165 \| -1.0 \| 0.15 \| 0.9211 \| 0.9076 \| 0.9377 \| 0.8571 \| 0.8954 \| \| 0.2898 \| 45.0 \| 9810 \| 0.2813 \| 0.8753 \| 0.9876 \| 0.977 \| 0.0 \| 0.1325 \| 0.8817 \| 0.4714 \| 0.911 \| 0.9123 \| 0.0 \| 0.2167 \| 0.9179 \| 0.9042 \| 0.9381 \| 0.8464 \| 0.8865 \| \| 0.2758 \| 46.0 \| 10028 \| 0.2633 \| 0.8786 \| 0.9872 \| 0.9719 \| 0.0 \| 0.1841 \| 0.8854 \| 0.4758 \| 0.9164 \| 0.9177 \| 0.0 \| 0.2615 \| 0.9218 \| 0.9012 \| 0.9374 \| 0.856 \| 0.898 \| \| 0.2758 \| 47.0 \| 10246 \| 0.2479 \| 0.8795 \| 0.9895 \| 0.9765 \| 0.0 \| 0.2066 \| 0.8849 \| 0.4765 \| 0.9146 \| 0.9171 \| 0.0 \| 0.275 \| 0.9207 \| 0.9114 \| 0.9448 \| 0.8476 \| 0.8893 \| \| 0.2758 \| 48.0 \| 10464 \| 0.2373 \| 0.8894 \| 0.9897 \| 0.9799 \| -1.0 \| 0.1994 \| 0.8939 \| 0.4795 \| 0.9253 \| 0.926 \| -1.0 \| 0.2545 \| 0.9293 \| 0.9076 \| 0.9431 \| 0.8713 \| 0.909 \| \| 0.2708 \| 49.0 \| 10682 \| 0.2538 \| 0.8846 \| 0.9893 \| 0.9793 \| 0.0 \| 0.2669 \| 0.8903 \| 0.4799 \| 0.9213 \| 0.9224 \| 0.0 \| 0.315 \| 0.9284 \| 0.9052 \| 0.9383 \| 0.8641 \| 0.9065 \| \| 0.2708 \| 50.0 \| 10900 \| 0.2445 \| 0.8919 \| 0.9896 \| 0.9745 \| -1.0 \| 0.2193 \| 0.8972 \| 0.4765 \| 0.9228 \| 0.925 \| -1.0 \| 0.3969 \| 0.9294 \| 0.9239 \| 0.9511 \| 0.8599 \| 0.8989 \| \| 0.2595 \| 51.0 \| 11118 \| 0.2110 \| 0.9037 \| 0.99 \| 0.9845 \| -1.0 \| 0.2267 \| 0.9093 \| 0.4882 \| 0.9339 \| 0.9346 \| -1.0 \| 0.25 \| 0.9374 \| 0.9299 \| 0.9574 \| 0.8776 \| 0.9117 \| \| 0.2595 \| 52.0 \| 11336 \| 0.2374 \| 0.897 \| 0.99 \| 0.9792 \| -1.0 \| 0.2066 \| 0.9029 \| 0.48 \| 0.9267 \| 0.9285 \| -1.0 \| 0.3179 \| 0.9335 \| 0.9257 \| 0.9531 \| 0.8684 \| 0.9039 \| \| 0.2378 \| 53.0 \| 11554 \| 0.2517 \| 0.8826 \| 0.9894 \| 0.9716 \| -1.0 \| 0.1494 \| 0.8901 \| 0.4782 \| 0.9162 \| 0.9188 \| -1.0 \| 0.2475 \| 0.9242 \| 0.9152 \| 0.9455 \| 0.8501 \| 0.892 \| \| 0.2378 \| 54.0 \| 11772 \| 0.2260 \| 0.8971 \| 0.9899 \| 0.9771 \| -1.0 \| 0.1848 \| 0.9029 \| 0.4825 \| 0.9304 \| 0.9315 \| -1.0 \| 0.2077 \| 0.936 \| 0.9255 \| 0.9544 \| 0.8687 \| 0.9087 \| \| 0.2378 \| 55.0 \| 11990 \| 0.2144 \| 0.9118 \| 0.9899 \| 0.9844 \| -1.0 \| 0.2843 \| 0.9158 \| 0.4875 \| 0.9417 \| 0.9435 \| -1.0 \| 0.3333 \| 0.9456 \| 0.9351 \| 0.9608 \| 0.8885 \| 0.9263 \| \| 0.2494 \| 56.0 \| 12208 \| 0.2028 \| 0.9107 \| 0.9897 \| 0.9814 \| 0.0 \| 0.1831 \| 0.9168 \| 0.4906 \| 0.9395 \| 0.9414 \| 0.0 \| 0.22 \| 0.9466 \| 0.935 \| 0.9585 \| 0.8864 \| 0.9243 \| \| 0.2494 \| 57.0 \| 12426 \| 0.2341 \| 0.8897 \| 0.9897 \| 0.9812 \| -1.0 \| 0.1783 \| 0.8932 \| 0.4822 \| 0.9242 \| 0.926 \| -1.0 \| 0.2154 \| 0.9303 \| 0.9168 \| 0.948 \| 0.8625 \| 0.9039 \| \| 0.2228 \| 58.0 \| 12644 \| 0.2075 \| 0.9084 \| 0.9899 \| 0.9792 \| -1.0 \| 0.1741 \| 0.9142 \| 0.4899 \| 0.9375 \| 0.9379 \| -1.0 \| 0.2308 \| 0.9421 \| 0.932 \| 0.9581 \| 0.8849 \| 0.9177 \| \| 0.2228 \| 59.0 \| 12862 \| 0.2059 \| 0.9096 \| 0.9896 \| 0.9803 \| 0.0 \| 0.2969 \| 0.9138 \| 0.4893 \| 0.9375 \| 0.9395 \| 0.0 \| 0.31 \| 0.9431 \| 0.9311 \| 0.957 \| 0.8881 \| 0.9219 \| \| 0.2218 \| 60.0 \| 13080 \| 0.2028 \| 0.9136 \| 0.9899 \| 0.984 \| -1.0 \| 0.2316 \| 0.9164 \| 0.4875 \| 0.9408 \| 0.9416 \| -1.0 \| 0.295 \| 0.9442 \| 0.9433 \| 0.9654 \| 0.884 \| 0.9177 \| \| 0.2218 \| 61.0 \| 13298 \| 0.2013 \| 0.911 \| 0.99 \| 0.9786 \| -1.0 \| 0.253 \| 0.9158 \| 0.4904 \| 0.9388 \| 0.94 \| -1.0 \| 0.3 \| 0.9435 \| 0.9325 \| 0.9572 \| 0.8895 \| 0.9228 \| \| 0.2238 \| 62.0 \| 13516 \| 0.2033 \| 0.9134 \| 0.9899 \| 0.9825 \| 0.0 \| 0.2228 \| 0.9199 \| 0.4896 \| 0.9426 \| 0.9438 \| 0.0 \| 0.2667 \| 0.9484 \| 0.9367 \| 0.9624 \| 0.8902 \| 0.9252 \| \| 0.2238 \| 63.0 \| 13734 \| 0.1893 \| 0.9216 \| 0.99 \| 0.9836 \| -1.0 \| 0.1905 \| 0.9271 \| 0.4942 \| 0.9509 \| 0.9512 \| -1.0 \| 0.235 \| 0.9546 \| 0.9403 \| 0.9664 \| 0.9029 \| 0.9361 \| \| 0.2238 \| 64.0 \| 13952 \| 0.1893 \| 0.9267 \| 0.9898 \| 0.9835 \| 0.0 \| 0.2342 \| 0.9317 \| 0.4957 \| 0.9524 \| 0.9536 \| 0.0 \| 0.2583 \| 0.9585 \| 0.9491 \| 0.971 \| 0.9043 \| 0.9363 \| \| 0.2131 \| 65.0 \| 14170 \| 0.1769 \| 0.9322 \| 0.9901 \| 0.9847 \| -1.0 \| 0.2413 \| 0.9349 \| 0.4982 \| 0.9554 \| 0.9559 \| -1.0 \| 0.2864 \| 0.959 \| 0.9463 \| 0.9673 \| 0.9181 \| 0.9445 \| \| 0.2131 \| 66.0 \| 14388 \| 0.1848 \| 0.9312 \| 0.9898 \| 0.9842 \| 0.0 \| 0.2901 \| 0.9358 \| 0.4973 \| 0.9545 \| 0.9551 \| 0.0 \| 0.425 \| 0.9591 \| 0.9517 \| 0.9709 \| 0.9107 \| 0.9394 \| \| 0.2038 \| 67.0 \| 14606 \| 0.1809 \| 0.9277 \| 0.9899 \| 0.9815 \| 0.0 \| 0.2354 \| 0.9329 \| 0.4951 \| 0.9524 \| 0.9539 \| 0.0 \| 0.2846 \| 0.9586 \| 0.9441 \| 0.9668 \| 0.9112 \| 0.9411 \| \| 0.2038 \| 68.0 \| 14824 \| 0.1831 \| 0.9178 \| 0.9899 \| 0.98 \| 0.0 \| 0.1728 \| 0.9256 \| 0.4922 \| 0.9472 \| 0.9483 \| 0.0 \| 0.23 \| 0.9538 \| 0.9396 \| 0.9646 \| 0.896 \| 0.9319 \| \| 0.1995 \| 69.0 \| 15042 \| 0.1631 \| 0.934 \| 0.9901 \| 0.9861 \| -1.0 \| 0.2804 \| 0.9405 \| 0.4982 \| 0.9574 \| 0.9583 \| -1.0 \| 0.325 \| 0.9615 \| 0.954 \| 0.9729 \| 0.914 \| 0.9438 \| \| 0.1995 \| 70.0 \| 15260 \| 0.1685 \| 0.9293 \| 0.9899 \| 0.9846 \| -1.0 \| 0.2397 \| 0.935 \| 0.4964 \| 0.9546 \| 0.9553 \| -1.0 \| 0.2714 \| 0.9593 \| 0.948 \| 0.9698 \| 0.9105 \| 0.9408 \| \| 0.1995 \| 71.0 \| 15478 \| 0.1629 \| 0.9371 \| 0.9901 \| 0.9842 \| -1.0 \| 0.2541 \| 0.942 \| 0.498 \| 0.9603 \| 0.9609 \| -1.0 \| 0.4964 \| 0.965 \| 0.954 \| 0.9741 \| 0.9202 \| 0.9477 \| \| 0.1877 \| 72.0 \| 15696 \| 0.1606 \| 0.944 \| 0.9901 \| 0.9846 \| -1.0 \| 0.277 \| 0.9469 \| 0.4988 \| 0.9636 \| 0.9642 \| -1.0 \| 0.3038 \| 0.9676 \| 0.96 \| 0.9758 \| 0.9281 \| 0.9527 \| \| 0.1877 \| 73.0 \| 15914 \| 0.1532 \| 0.9389 \| 0.99 \| 0.9806 \| 0.0 \| 0.2592 \| 0.9446 \| 0.5009 \| 0.961 \| 0.962 \| 0.0 \| 0.3133 \| 0.9662 \| 0.9564 \| 0.9749 \| 0.9214 \| 0.9492 \| \| 0.1912 \| 74.0 \| 16132 \| 0.1434 \| 0.9488 \| 0.995 \| 0.9934 \| -1.0 \| 0.5552 \| 0.9507 \| 0.5033 \| 0.9673 \| 0.9675 \| -1.0 \| 0.7182 \| 0.969 \| 0.9639 \| 0.9786 \| 0.9336 \| 0.9563 \| \| 0.1912 \| 75.0 \| 16350 \| 0.1726 \| 0.9309 \| 0.9901 \| 0.9832 \| -1.0 \| 0.216 \| 0.9344 \| 0.4964 \| 0.9568 \| 0.9578 \| -1.0 \| 0.2611 \| 0.9607 \| 0.9539 \| 0.9747 \| 0.9079 \| 0.941 \| \| 0.1859 \| 76.0 \| 16568 \| 0.1587 \| 0.9378 \| 0.9901 \| 0.9847 \| -1.0 \| 0.1684 \| 0.944 \| 0.4994 \| 0.9601 \| 0.9607 \| -1.0 \| 0.2382 \| 0.9662 \| 0.952 \| 0.9715 \| 0.9237 \| 0.9499 \| \| 0.1859 \| 77.0 \| 16786 \| 0.1378 \| 0.9509 \| 0.9901 \| 0.9845 \| -1.0 \| 0.2089 \| 0.959 \| 0.5047 \| 0.9688 \| 0.9691 \| -1.0 \| 0.2353 \| 0.9748 \| 0.9666 \| 0.9823 \| 0.9352 \| 0.9559 \| \| 0.1747 \| 78.0 \| 17004 \| 0.1416 \| 0.9478 \| 0.9901 \| 0.985 \| 0.0 \| 0.3334 \| 0.9521 \| 0.5039 \| 0.9685 \| 0.9692 \| 0.0 \| 0.35 \| 0.9719 \| 0.9617 \| 0.9799 \| 0.9338 \| 0.9586 \| \| 0.1747 \| 79.0 \| 17222 \| 0.1615 \| 0.9376 \| 0.9949 \| 0.9873 \| -1.0 \| 0.5057 \| 0.9406 \| 0.5003 \| 0.9599 \| 0.9607 \| -1.0 \| 0.5688 \| 0.9644 \| 0.9583 \| 0.9746 \| 0.917 \| 0.9469 \| \| 0.1747 \| 80.0 \| 17440 \| 0.1482 \| 0.9427 \| 0.99 \| 0.9823 \| -1.0 \| 0.1933 \| 0.9499 \| 0.5025 \| 0.9639 \| 0.9642 \| -1.0 \| 0.2321 \| 0.9689 \| 0.9566 \| 0.9762 \| 0.9289 \| 0.9521 \| \| 0.1707 \| 81.0 \| 17658 \| 0.1379 \| 0.9518 \| 0.9901 \| 0.9894 \| -1.0 \| 0.2838 \| 0.956 \| 0.504 \| 0.97 \| 0.9702 \| -1.0 \| 0.3 \| 0.9742 \| 0.965 \| 0.9787 \| 0.9386 \| 0.9618 \| \| 0.1707 \| 82.0 \| 17876 \| 0.1384 \| 0.9478 \| 0.9901 \| 0.9846 \| -1.0 \| 0.2518 \| 0.9545 \| 0.504 \| 0.9687 \| 0.9691 \| -1.0 \| 0.2643 \| 0.9734 \| 0.9612 \| 0.9787 \| 0.9344 \| 0.9595 \| \| 0.1658 \| 83.0 \| 18094 \| 0.1379 \| 0.9532 \| 0.9901 \| 0.9845 \| -1.0 \| 0.2543 \| 0.9567 \| 0.5043 \| 0.9707 \| 0.9714 \| -1.0 \| 0.2708 \| 0.975 \| 0.9655 \| 0.981 \| 0.9408 \| 0.9617 \| \| 0.1658 \| 84.0 \| 18312 \| 0.1325 \| 0.9544 \| 0.9901 \| 0.9845 \| 0.0 \| 0.256 \| 0.9597 \| 0.5047 \| 0.9712 \| 0.972 \| 0.0 \| 0.3036 \| 0.9762 \| 0.9672 \| 0.9811 \| 0.9417 \| 0.9628 \| \| 0.1532 \| 85.0 \| 18530 \| 0.1558 \| 0.9452 \| 0.99 \| 0.9845 \| -1.0 \| 0.2469 \| 0.9495 \| 0.5009 \| 0.9648 \| 0.9657 \| -1.0 \| 0.2769 \| 0.9695 \| 0.9584 \| 0.9749 \| 0.932 \| 0.9565 \| \| 0.1532 \| 86.0 \| 18748 \| 0.1228 \| 0.9538 \| 0.9901 \| 0.9841 \| -1.0 \| 0.3437 \| 0.9585 \| 0.5056 \| 0.972 \| 0.9726 \| -1.0 \| 0.3727 \| 0.9747 \| 0.9642 \| 0.9806 \| 0.9434 \| 0.9646 \| \| 0.1532 \| 87.0 \| 18966 \| 0.1317 \| 0.9587 \| 0.9901 \| 0.9844 \| 0.0 \| 0.4141 \| 0.965 \| 0.5064 \| 0.9738 \| 0.974 \| 0.0 \| 0.4517 \| 0.9791 \| 0.9676 \| 0.9815 \| 0.9498 \| 0.9664 \| \| 0.1574 \| 88.0 \| 19184 \| 0.1318 \| 0.9508 \| 0.9901 \| 0.9845 \| 0.0 \| 0.2545 \| 0.9581 \| 0.5059 \| 0.9705 \| 0.9706 \| 0.0 \| 0.2962 \| 0.9747 \| 0.9594 \| 0.9778 \| 0.9422 \| 0.9633 \| \| 0.1574 \| 89.0 \| 19402 \| 0.1424 \| 0.9513 \| 0.9899 \| 0.984 \| -1.0 \| 0.2362 \| 0.9547 \| 0.5034 \| 0.9691 \| 0.9695 \| -1.0 \| 0.2875 \| 0.9729 \| 0.9636 \| 0.9786 \| 0.939 \| 0.9603 \| \| 0.1537 \| 90.0 \| 19620 \| 0.1240 \| 0.9565 \| 0.9901 \| 0.9896 \| -1.0 \| 0.5053 \| 0.9592 \| 0.5066 \| 0.9747 \| 0.9752 \| -1.0 \| 0.55 \| 0.9771 \| 0.9669 \| 0.9823 \| 0.9461 \| 0.9681 \| \| 0.1537 \| 91.0 \| 19838 \| 0.1382 \| 0.947 \| 0.9901 \| 0.9835 \| 0.0 \| 0.5316 \| 0.9504 \| 0.5018 \| 0.9681 \| 0.9683 \| 0.0 \| 0.555 \| 0.9712 \| 0.9622 \| 0.9775 \| 0.9319 \| 0.9592 \| \| 0.1547 \| 92.0 \| 20056 \| 0.1276 \| 0.9565 \| 0.9901 \| 0.983 \| -1.0 \| 0.3161 \| 0.9618 \| 0.5058 \| 0.9742 \| 0.9743 \| -1.0 \| 0.3458 \| 0.977 \| 0.9668 \| 0.9818 \| 0.9462 \| 0.9669 \| \| 0.1547 \| 93.0 \| 20274 \| 0.1329 \| 0.9539 \| 0.99 \| 0.9836 \| -1.0 \| 0.2997 \| 0.9593 \| 0.5053 \| 0.9718 \| 0.9728 \| -1.0 \| 0.3318 \| 0.9754 \| 0.9679 \| 0.982 \| 0.9398 \| 0.9635 \| \| 0.1547 \| 94.0 \| 20492 \| 0.1348 \| 0.9571 \| 0.99 \| 0.9846 \| -1.0 \| 0.3267 \| 0.9615 \| 0.5039 \| 0.9732 \| 0.9737 \| -1.0 \| 0.3625 \| 0.9761 \| 0.9678 \| 0.9823 \| 0.9463 \| 0.9652 \| \| 0.1513 \| 95.0 \| 20710 \| 0.1251 \| 0.9546 \| 0.9901 \| 0.9844 \| 0.0 \| 0.2549 \| 0.9626 \| 0.5049 \| 0.9728 \| 0.9731 \| 0.0 \| 0.2625 \| 0.9775 \| 0.965 \| 0.981 \| 0.9442 \| 0.9652 \| \| 0.1513 \| 96.0 \| 20928 \| 0.1264 \| 0.9594 \| 0.9901 \| 0.9899 \| 0.0 \| 0.327 \| 0.9631 \| 0.5068 \| 0.9755 \| 0.9763 \| 0.0 \| 0.3409 \| 0.9794 \| 0.9696 \| 0.9842 \| 0.9492 \| 0.9683 \| \| 0.1635 \| 97.0 \| 21146 \| 0.1306 \| 0.9515 \| 0.9901 \| 0.9843 \| -1.0 \| 0.2685 \| 0.9561 \| 0.5041 \| 0.9696 \| 0.9703 \| -1.0 \| 0.2857 \| 0.9742 \| 0.9626 \| 0.9795 \| 0.9404 \| 0.9611 \| \| 0.1635 \| 98.0 \| 21364 \| 0.1410 \| 0.9481 \| 0.9899 \| 0.9788 \| 0.0 \| 0.4025 \| 0.9542 \| 0.5031 \| 0.9662 \| 0.9678 \| 0.0 \| 0.4458 \| 0.9722 \| 0.9621 \| 0.9789 \| 0.9341 \| 0.9567 \| \| 0.1505 \| 99.0 \| 21582 \| 0.1253 \| 0.9571 \| 0.9901 \| 0.984 \| -1.0 \| 0.3105 \| 0.962 \| 0.5066 \| 0.9737 \| 0.974 \| -1.0 \| 0.3375 \| 0.9777 \| 0.9702 \| 0.9832 \| 0.944 \| 0.9648 \| \| 0.1505 \| 100.0 \| 21800 \| 0.1291 \| 0.9532 \| 0.9901 \| 0.9845 \| -1.0 \| 0.3203 \| 0.9578 \| 0.5044 \| 0.9715 \| 0.972 \| -1.0 \| 0.3538 \| 0.9747 \| 0.9618 \| 0.9775 \| 0.9447 \| 0.9664 \| ### Framework versions - Transformers 4.42.3 - Pytorch 2.3.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1	[ "cashier", "cx" ]
Husseinhaidar6/detr-finetuned-LP-v2	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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leon-cvetkovski/deta-equipment-10-epochs	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Husseinhaidar6/detr-finetuned-LP-v3	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Husseinhaidar6/detr-finetuned-LP-v4	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Husseinhaidar6/detr-finetuned-LP-v5	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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lk66/fashion-rec	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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lk6/fashion-rec	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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leon-cvetkovski/deta-equipment-17.5-epochs	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7", "label_8", "label_9", "label_10", "label_11", "label_12", "label_13", "label_14", "label_15", "label_16", "label_17", "label_18", "label_19", "label_20", "label_21" ]
ArrayDice/facebook_detr_finetuned_cppe5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # facebook_detr_finetuned_cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5232 - Map: 0.2143 - Map 50: 0.4481 - Map 75: 0.1932 - Map Small: 0.0595 - Map Medium: 0.1238 - Map Large: 0.3317 - Mar 1: 0.2418 - Mar 10: 0.4177 - Mar 100: 0.4387 - Mar Small: 0.1234 - Mar Medium: 0.3182 - Mar Large: 0.5933 - Map Coverall: 0.4858 - Mar 100 Coverall: 0.6766 - Map Face Shield: 0.1639 - Mar 100 Face Shield: 0.4278 - Map Gloves: 0.1388 - Mar 100 Gloves: 0.3763 - Map Goggles: 0.0716 - Mar 100 Goggles: 0.3538 - Map Mask: 0.2112 - Mar 100 Mask: 0.3587 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Small \| Map Medium \| Map Large \| Mar 1 \| Mar 10 \| Mar 100 \| Mar Small \| Mar Medium \| Mar Large \| Map Coverall \| Mar 100 Coverall \| Map Face Shield \| Mar 100 Face Shield \| Map Gloves \| Mar 100 Gloves \| Map Goggles \| Mar 100 Goggles \| Map Mask \| Mar 100 Mask \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:---------:\|:----------:\|:---------:\|:------------:\|:----------------:\|:---------------:\|:-------------------:\|:----------:\|:--------------:\|:-----------:\|:---------------:\|:--------:\|:------------:\| \| No log \| 1.0 \| 107 \| 2.6056 \| 0.02 \| 0.0483 \| 0.0156 \| 0.0017 \| 0.0019 \| 0.0219 \| 0.0344 \| 0.1171 \| 0.1547 \| 0.0387 \| 0.0674 \| 0.2003 \| 0.092 \| 0.4671 \| 0.0 \| 0.0 \| 0.0046 \| 0.1647 \| 0.0 \| 0.0 \| 0.0034 \| 0.1418 \| \| No log \| 2.0 \| 214 \| 2.3184 \| 0.036 \| 0.0808 \| 0.0265 \| 0.0098 \| 0.0135 \| 0.0403 \| 0.0512 \| 0.1513 \| 0.1976 \| 0.1004 \| 0.1414 \| 0.2133 \| 0.1389 \| 0.518 \| 0.0 \| 0.0 \| 0.0319 \| 0.2384 \| 0.0 \| 0.0 \| 0.0091 \| 0.2316 \| \| No log \| 3.0 \| 321 \| 2.2718 \| 0.0351 \| 0.0849 \| 0.0248 \| 0.0082 \| 0.0168 \| 0.0345 \| 0.0589 \| 0.1499 \| 0.1965 \| 0.0592 \| 0.112 \| 0.2353 \| 0.1366 \| 0.5689 \| 0.0 \| 0.0 \| 0.0195 \| 0.2018 \| 0.0 \| 0.0 \| 0.0193 \| 0.2116 \| \| No log \| 4.0 \| 428 \| 2.2070 \| 0.0665 \| 0.1472 \| 0.053 \| 0.0093 \| 0.0147 \| 0.0715 \| 0.0796 \| 0.1709 \| 0.199 \| 0.077 \| 0.1116 \| 0.2313 \| 0.2774 \| 0.541 \| 0.0 \| 0.0 \| 0.0167 \| 0.2232 \| 0.0 \| 0.0 \| 0.0384 \| 0.2307 \| \| 2.2509 \| 5.0 \| 535 \| 2.0817 \| 0.0753 \| 0.1631 \| 0.0643 \| 0.0205 \| 0.0263 \| 0.0774 \| 0.0833 \| 0.184 \| 0.2081 \| 0.0762 \| 0.1151 \| 0.2439 \| 0.3053 \| 0.5653 \| 0.0007 \| 0.0152 \| 0.0296 \| 0.2576 \| 0.0 \| 0.0 \| 0.0407 \| 0.2022 \| \| 2.2509 \| 6.0 \| 642 \| 2.1124 \| 0.0897 \| 0.1778 \| 0.0786 \| 0.0128 \| 0.0293 \| 0.0976 \| 0.0862 \| 0.1932 \| 0.2194 \| 0.0752 \| 0.1139 \| 0.2658 \| 0.3802 \| 0.6324 \| 0.007 \| 0.0215 \| 0.028 \| 0.2277 \| 0.0 \| 0.0 \| 0.0331 \| 0.2156 \| \| 2.2509 \| 7.0 \| 749 \| 1.9497 \| 0.1018 \| 0.2201 \| 0.0729 \| 0.0331 \| 0.0416 \| 0.1097 \| 0.1105 \| 0.2187 \| 0.2405 \| 0.076 \| 0.1336 \| 0.2967 \| 0.3631 \| 0.627 \| 0.0265 \| 0.0873 \| 0.0394 \| 0.2518 \| 0.0 \| 0.0 \| 0.0801 \| 0.2364 \| \| 2.2509 \| 8.0 \| 856 \| 2.0218 \| 0.1065 \| 0.2185 \| 0.0902 \| 0.02 \| 0.0531 \| 0.1221 \| 0.1329 \| 0.244 \| 0.2662 \| 0.0686 \| 0.1606 \| 0.3382 \| 0.3752 \| 0.6221 \| 0.0345 \| 0.1658 \| 0.0367 \| 0.2442 \| 0.0129 \| 0.02 \| 0.0734 \| 0.2791 \| \| 2.2509 \| 9.0 \| 963 \| 1.9220 \| 0.1125 \| 0.2452 \| 0.0979 \| 0.0353 \| 0.057 \| 0.1411 \| 0.132 \| 0.2557 \| 0.2703 \| 0.078 \| 0.1537 \| 0.3563 \| 0.3765 \| 0.5878 \| 0.0368 \| 0.2063 \| 0.0474 \| 0.2487 \| 0.0018 \| 0.02 \| 0.0997 \| 0.2884 \| \| 1.734 \| 10.0 \| 1070 \| 1.8888 \| 0.1342 \| 0.284 \| 0.1133 \| 0.0278 \| 0.0755 \| 0.1591 \| 0.1551 \| 0.2793 \| 0.2984 \| 0.0525 \| 0.1895 \| 0.3834 \| 0.447 \| 0.6649 \| 0.0624 \| 0.2241 \| 0.0505 \| 0.2629 \| 0.0101 \| 0.0723 \| 0.1011 \| 0.268 \| \| 1.734 \| 11.0 \| 1177 \| 1.8868 \| 0.1016 \| 0.2399 \| 0.0731 \| 0.0231 \| 0.0587 \| 0.1282 \| 0.1332 \| 0.2687 \| 0.2891 \| 0.0759 \| 0.2153 \| 0.3802 \| 0.3322 \| 0.5811 \| 0.0331 \| 0.2405 \| 0.047 \| 0.2518 \| 0.0102 \| 0.1292 \| 0.0856 \| 0.2427 \| \| 1.734 \| 12.0 \| 1284 \| 1.7685 \| 0.1412 \| 0.3049 \| 0.1177 \| 0.0469 \| 0.0695 \| 0.183 \| 0.1704 \| 0.3233 \| 0.3389 \| 0.0818 \| 0.2284 \| 0.4528 \| 0.4338 \| 0.6293 \| 0.0626 \| 0.3051 \| 0.0604 \| 0.2839 \| 0.0246 \| 0.18 \| 0.1244 \| 0.2964 \| \| 1.734 \| 13.0 \| 1391 \| 1.7098 \| 0.1508 \| 0.3145 \| 0.1249 \| 0.039 \| 0.0923 \| 0.2026 \| 0.1742 \| 0.3351 \| 0.3524 \| 0.0892 \| 0.2417 \| 0.4757 \| 0.4381 \| 0.6559 \| 0.0799 \| 0.3076 \| 0.083 \| 0.3085 \| 0.0079 \| 0.1738 \| 0.1452 \| 0.3164 \| \| 1.734 \| 14.0 \| 1498 \| 1.6927 \| 0.1617 \| 0.3458 \| 0.1351 \| 0.0405 \| 0.0958 \| 0.2241 \| 0.1852 \| 0.3496 \| 0.366 \| 0.1014 \| 0.2475 \| 0.4904 \| 0.4484 \| 0.6581 \| 0.1058 \| 0.3329 \| 0.0674 \| 0.2906 \| 0.0361 \| 0.2323 \| 0.1507 \| 0.316 \| \| 1.5105 \| 15.0 \| 1605 \| 1.6452 \| 0.1748 \| 0.3717 \| 0.145 \| 0.0578 \| 0.11 \| 0.2376 \| 0.2036 \| 0.3582 \| 0.376 \| 0.1233 \| 0.2797 \| 0.4991 \| 0.4443 \| 0.6432 \| 0.1282 \| 0.3241 \| 0.0984 \| 0.3259 \| 0.0301 \| 0.26 \| 0.1727 \| 0.3267 \| \| 1.5105 \| 16.0 \| 1712 \| 1.6752 \| 0.1706 \| 0.3672 \| 0.1338 \| 0.0475 \| 0.1048 \| 0.2499 \| 0.1987 \| 0.3623 \| 0.3886 \| 0.1158 \| 0.2733 \| 0.5125 \| 0.4261 \| 0.6392 \| 0.1294 \| 0.3367 \| 0.0981 \| 0.3562 \| 0.0378 \| 0.2708 \| 0.1616 \| 0.34 \| \| 1.5105 \| 17.0 \| 1819 \| 1.6004 \| 0.1867 \| 0.3897 \| 0.1547 \| 0.045 \| 0.1158 \| 0.2696 \| 0.2178 \| 0.38 \| 0.4003 \| 0.1071 \| 0.2849 \| 0.5289 \| 0.4534 \| 0.6586 \| 0.125 \| 0.3557 \| 0.1058 \| 0.3598 \| 0.0683 \| 0.2892 \| 0.1812 \| 0.3382 \| \| 1.5105 \| 18.0 \| 1926 \| 1.5693 \| 0.1823 \| 0.3801 \| 0.1584 \| 0.0578 \| 0.1034 \| 0.2637 \| 0.2152 \| 0.3791 \| 0.4006 \| 0.1198 \| 0.2829 \| 0.5342 \| 0.4645 \| 0.6676 \| 0.1157 \| 0.3557 \| 0.1143 \| 0.3683 \| 0.0421 \| 0.2754 \| 0.1748 \| 0.336 \| \| 1.3533 \| 19.0 \| 2033 \| 1.5891 \| 0.1895 \| 0.4061 \| 0.1638 \| 0.0519 \| 0.1144 \| 0.2796 \| 0.2145 \| 0.3878 \| 0.406 \| 0.1355 \| 0.2999 \| 0.5346 \| 0.4632 \| 0.655 \| 0.1435 \| 0.357 \| 0.1196 \| 0.3647 \| 0.0407 \| 0.32 \| 0.1808 \| 0.3333 \| \| 1.3533 \| 20.0 \| 2140 \| 1.5850 \| 0.1969 \| 0.3947 \| 0.1688 \| 0.0549 \| 0.1237 \| 0.2798 \| 0.2333 \| 0.3976 \| 0.4146 \| 0.1077 \| 0.3041 \| 0.544 \| 0.4706 \| 0.6486 \| 0.1459 \| 0.3987 \| 0.1287 \| 0.3496 \| 0.0448 \| 0.3323 \| 0.1945 \| 0.344 \| \| 1.3533 \| 21.0 \| 2247 \| 1.5667 \| 0.1971 \| 0.3975 \| 0.1752 \| 0.0607 \| 0.1162 \| 0.2984 \| 0.23 \| 0.3969 \| 0.4131 \| 0.1131 \| 0.3001 \| 0.5511 \| 0.4826 \| 0.6626 \| 0.1445 \| 0.3937 \| 0.1247 \| 0.3536 \| 0.0408 \| 0.3123 \| 0.1927 \| 0.3436 \| \| 1.3533 \| 22.0 \| 2354 \| 1.5281 \| 0.2048 \| 0.4147 \| 0.1843 \| 0.0513 \| 0.1217 \| 0.3206 \| 0.2292 \| 0.3961 \| 0.4149 \| 0.1068 \| 0.314 \| 0.5566 \| 0.4892 \| 0.6698 \| 0.1538 \| 0.3772 \| 0.1308 \| 0.367 \| 0.0526 \| 0.3169 \| 0.1977 \| 0.3436 \| \| 1.3533 \| 23.0 \| 2461 \| 1.5632 \| 0.206 \| 0.423 \| 0.1755 \| 0.0558 \| 0.1123 \| 0.3259 \| 0.2358 \| 0.3957 \| 0.4222 \| 0.1182 \| 0.3006 \| 0.5721 \| 0.4882 \| 0.6739 \| 0.1507 \| 0.3937 \| 0.1371 \| 0.3688 \| 0.055 \| 0.3308 \| 0.1992 \| 0.344 \| \| 1.2451 \| 24.0 \| 2568 \| 1.5177 \| 0.2083 \| 0.4356 \| 0.1778 \| 0.0506 \| 0.1205 \| 0.3281 \| 0.2387 \| 0.4117 \| 0.4344 \| 0.1286 \| 0.3056 \| 0.5865 \| 0.4806 \| 0.6802 \| 0.1463 \| 0.4139 \| 0.1384 \| 0.3768 \| 0.0577 \| 0.34 \| 0.2183 \| 0.3613 \| \| 1.2451 \| 25.0 \| 2675 \| 1.5367 \| 0.2092 \| 0.427 \| 0.1808 \| 0.0553 \| 0.1202 \| 0.3256 \| 0.2386 \| 0.4115 \| 0.4347 \| 0.1224 \| 0.3102 \| 0.5915 \| 0.4732 \| 0.6748 \| 0.1508 \| 0.4203 \| 0.1399 \| 0.3795 \| 0.0613 \| 0.3354 \| 0.2208 \| 0.3636 \| \| 1.2451 \| 26.0 \| 2782 \| 1.5130 \| 0.2093 \| 0.4378 \| 0.1884 \| 0.0558 \| 0.1225 \| 0.3295 \| 0.2394 \| 0.4179 \| 0.439 \| 0.1442 \| 0.3167 \| 0.5958 \| 0.4743 \| 0.6761 \| 0.1552 \| 0.4228 \| 0.1382 \| 0.3759 \| 0.0656 \| 0.3615 \| 0.2131 \| 0.3587 \| \| 1.2451 \| 27.0 \| 2889 \| 1.5194 \| 0.2122 \| 0.4448 \| 0.1893 \| 0.0592 \| 0.1231 \| 0.3307 \| 0.2405 \| 0.4172 \| 0.4352 \| 0.1247 \| 0.3171 \| 0.5864 \| 0.4826 \| 0.6739 \| 0.1597 \| 0.4203 \| 0.1384 \| 0.3696 \| 0.0692 \| 0.3585 \| 0.2108 \| 0.3538 \| \| 1.2451 \| 28.0 \| 2996 \| 1.5213 \| 0.2123 \| 0.4451 \| 0.1918 \| 0.059 \| 0.1221 \| 0.3282 \| 0.2426 \| 0.4157 \| 0.4347 \| 0.1234 \| 0.3165 \| 0.5867 \| 0.4856 \| 0.677 \| 0.1588 \| 0.4177 \| 0.1397 \| 0.3754 \| 0.0692 \| 0.3508 \| 0.208 \| 0.3524 \| \| 1.18 \| 29.0 \| 3103 \| 1.5214 \| 0.2135 \| 0.4461 \| 0.1923 \| 0.0595 \| 0.1228 \| 0.3305 \| 0.2414 \| 0.4191 \| 0.4392 \| 0.1234 \| 0.3189 \| 0.5921 \| 0.4857 \| 0.6775 \| 0.1626 \| 0.4304 \| 0.1393 \| 0.3759 \| 0.07 \| 0.3569 \| 0.2098 \| 0.3551 \| \| 1.18 \| 30.0 \| 3210 \| 1.5232 \| 0.2143 \| 0.4481 \| 0.1932 \| 0.0595 \| 0.1238 \| 0.3317 \| 0.2418 \| 0.4177 \| 0.4387 \| 0.1234 \| 0.3182 \| 0.5933 \| 0.4858 \| 0.6766 \| 0.1639 \| 0.4278 \| 0.1388 \| 0.3763 \| 0.0716 \| 0.3538 \| 0.2112 \| 0.3587 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
leon-cvetkovski/deta-equipment-17.5-epochs-trash	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7", "label_8", "label_9", "label_10", "label_11", "label_12", "label_13", "label_14", "label_15", "label_16", "label_17", "label_18", "label_19", "label_20", "label_21" ]
leon-cvetkovski/deta-equipment-50-epochs	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7", "label_8", "label_9", "label_10", "label_11", "label_12", "label_13", "label_14", "label_15", "label_16", "label_17", "label_18", "label_19", "label_20", "label_21" ]
Rareshika/detr-resnet-50_finetuned_cppe5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet-50_finetuned_cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
Rareshika/yolos_finetuned_cppe5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # yolos_finetuned_cppe5 This model is a fine-tuned version of [hustvl/yolos-tiny](https://huggingface.co/hustvl/yolos-tiny) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
dbihbka/detr_finetuned_cppe5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr_finetuned_cppe5 This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 20 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.42.3 - Pytorch 2.3.1 - Datasets 2.20.0 - Tokenizers 0.19.1	[ "speaker", "participant", "shared screen" ]
Charles95/detr-resnet-50-notimm	# DETR (End-to-End Object Detection) model with ResNet-50 backbone DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Carion et al. and first released in [this repository](https://github.com/facebookresearch/detr). Disclaimer: The team releasing DETR did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100. The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/detr_architecture.png) ## Intended uses & limitations You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=facebook/detr) to look for all available DETR models. ### How to use Here is how to use this model: ```python from transformers import DetrImageProcessor, DetrForObjectDetection import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50") model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50") inputs = processor(images=image, return_tensors="pt") outputs = model(inputs) # convert outputs (bounding boxes and class logits) to COCO API # let's only keep detections with score > 0.9 target_sizes = torch.tensor([image.size[::-1]]) results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0] for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): box = [round(i, 2) for i in box.tolist()] print( f"Detected {model.config.id2label[label.item()]} with confidence " f"{round(score.item(), 3)} at location {box}" ) ``` This should output: ``` Detected remote with confidence 0.998 at location [40.16, 70.81, 175.55, 117.98] Detected remote with confidence 0.996 at location [333.24, 72.55, 368.33, 187.66] Detected couch with confidence 0.995 at location [-0.02, 1.15, 639.73, 473.76] Detected cat with confidence 0.999 at location [13.24, 52.05, 314.02, 470.93] Detected cat with confidence 0.999 at location [345.4, 23.85, 640.37, 368.72] ``` Currently, both the feature extractor and model support PyTorch. ## Training data The DETR model was trained on [COCO 2017 object detection](https://cocodataset.org/#download), a dataset consisting of 118k/5k annotated images for training/validation respectively. ## Training procedure ### Preprocessing The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py). Images are resized/rescaled such that the shortest side is at least 800 pixels and the largest side at most 1333 pixels, and normalized across the RGB channels with the ImageNet mean (0.485, 0.456, 0.406) and standard deviation (0.229, 0.224, 0.225). ### Training The model was trained for 300 epochs on 16 V100 GPUs. This takes 3 days, with 4 images per GPU (hence a total batch size of 64). ## Evaluation results This model achieves an AP (average precision) of 42.0** on COCO 2017 validation. For more details regarding evaluation results, we refer to table 1 of the original paper. ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2005-12872, author = {Nicolas Carion and Francisco Massa and Gabriel Synnaeve and Nicolas Usunier and Alexander Kirillov and Sergey Zagoruyko}, title = {End-to-End Object Detection with Transformers}, journal = {CoRR}, volume = {abs/2005.12872}, year = {2020}, url = {https://arxiv.org/abs/2005.12872}, archivePrefix = {arXiv}, eprint = {2005.12872}, timestamp = {Thu, 28 May 2020 17:38:09 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2005-12872.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```	[ "n/a", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "n/a", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "n/a", "backpack", "umbrella", "n/a", "n/a", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "n/a", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "n/a", "dining table", "n/a", "n/a", "toilet", "n/a", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "n/a", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush" ]
ArrayDice/Vehicle_Detection_Model	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Vehicle_Detection_Model This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7220 - Map: 0.0875 - Map 50: 0.1634 - Map 75: 0.084 - Map Small: 0.355 - Map Medium: 0.1423 - Map Large: 0.0499 - Mar 1: 0.1462 - Mar 10: 0.2602 - Mar 100: 0.2709 - Mar Small: 0.5 - Mar Medium: 0.4013 - Mar Large: 0.3 - Map Camping car: 0.0039 - Mar 100 Camping car: 0.35 - Map Car: 0.4971 - Mar 100 Car: 0.6256 - Map Other: 0.0 - Mar 100 Other: 0.0 - Map Pickup: 0.0239 - Mar 100 Pickup: 0.65 - Map Truck: 0.0 - Mar 100 Truck: 0.0 - Map Van: 0.0 - Mar 100 Van: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Small \| Map Medium \| Map Large \| Mar 1 \| Mar 10 \| Mar 100 \| Mar Small \| Mar Medium \| Mar Large \| Map Camping car \| Mar 100 Camping car \| Map Car \| Mar 100 Car \| Map Other \| Mar 100 Other \| Map Pickup \| Mar 100 Pickup \| Map Truck \| Mar 100 Truck \| Map Van \| Mar 100 Van \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:---------:\|:----------:\|:---------:\|:---------------:\|:-------------------:\|:-------:\|:-----------:\|:---------:\|:-------------:\|:----------:\|:--------------:\|:---------:\|:-------------:\|:-------:\|:-----------:\| \| No log \| 1.0 \| 232 \| 1.3186 \| 0.0125 \| 0.0292 \| 0.0085 \| 0.0059 \| 0.0197 \| 0.004 \| 0.0182 \| 0.0467 \| 0.0903 \| 0.0556 \| 0.1396 \| 0.1 \| 0.0 \| 0.0 \| 0.0751 \| 0.5416 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| No log \| 2.0 \| 464 \| 1.0802 \| 0.0375 \| 0.0832 \| 0.0283 \| 0.263 \| 0.0585 \| 0.0102 \| 0.0254 \| 0.0734 \| 0.0865 \| 0.2667 \| 0.1317 \| 0.15 \| 0.0 \| 0.0 \| 0.2249 \| 0.5189 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.4746 \| 3.0 \| 696 \| 0.9804 \| 0.0608 \| 0.125 \| 0.0483 \| 0.2948 \| 0.0938 \| 0.075 \| 0.0305 \| 0.0821 \| 0.0903 \| 0.3556 \| 0.1372 \| 0.075 \| 0.0 \| 0.0 \| 0.3646 \| 0.5416 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.4746 \| 4.0 \| 928 \| 0.9139 \| 0.0712 \| 0.1426 \| 0.0577 \| 0.2232 \| 0.1091 \| 0.125 \| 0.0315 \| 0.085 \| 0.0959 \| 0.3333 \| 0.1459 \| 0.125 \| 0.0 \| 0.0 \| 0.4274 \| 0.5754 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.0134 \| 5.0 \| 1160 \| 0.9581 \| 0.0637 \| 0.1367 \| 0.0426 \| 0.0804 \| 0.0993 \| 0.075 \| 0.0289 \| 0.0771 \| 0.0846 \| 0.2556 \| 0.1292 \| 0.075 \| 0.0 \| 0.0 \| 0.3823 \| 0.5078 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.0134 \| 6.0 \| 1392 \| 0.8830 \| 0.0743 \| 0.1476 \| 0.0645 \| 0.2171 \| 0.1139 \| 0.075 \| 0.0345 \| 0.0862 \| 0.0967 \| 0.3222 \| 0.1474 \| 0.075 \| 0.0 \| 0.0 \| 0.4456 \| 0.5801 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.9133 \| 7.0 \| 1624 \| 0.8645 \| 0.0716 \| 0.147 \| 0.0571 \| 0.2156 \| 0.1099 \| 0.0752 \| 0.0329 \| 0.0853 \| 0.0966 \| 0.3111 \| 0.147 \| 0.175 \| 0.0 \| 0.0 \| 0.4296 \| 0.5797 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.9133 \| 8.0 \| 1856 \| 0.8776 \| 0.0676 \| 0.1478 \| 0.0445 \| 0.2225 \| 0.1032 \| 0.1254 \| 0.0317 \| 0.0811 \| 0.0934 \| 0.3333 \| 0.1417 \| 0.2 \| 0.0 \| 0.0 \| 0.4056 \| 0.5601 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.8617 \| 9.0 \| 2088 \| 0.8556 \| 0.0754 \| 0.1506 \| 0.0638 \| 0.252 \| 0.1153 \| 0.1254 \| 0.0323 \| 0.0881 \| 0.0999 \| 0.3667 \| 0.1517 \| 0.2 \| 0.0 \| 0.0 \| 0.4525 \| 0.5996 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.8617 \| 10.0 \| 2320 \| 0.7552 \| 0.0821 \| 0.1528 \| 0.0803 \| 0.361 \| 0.1248 \| 0.1257 \| 0.0347 \| 0.1119 \| 0.1312 \| 0.5222 \| 0.1747 \| 0.2625 \| 0.0 \| 0.0 \| 0.4921 \| 0.6206 \| 0.0 \| 0.0 \| 0.0004 \| 0.1667 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.8182 \| 11.0 \| 2552 \| 0.8211 \| 0.0741 \| 0.1525 \| 0.0627 \| 0.3014 \| 0.114 \| 0.0503 \| 0.0381 \| 0.1098 \| 0.1544 \| 0.4222 \| 0.2641 \| 0.125 \| 0.0001 \| 0.1 \| 0.4422 \| 0.5762 \| 0.0 \| 0.0 \| 0.002 \| 0.25 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.8182 \| 12.0 \| 2784 \| 0.8140 \| 0.0734 \| 0.1486 \| 0.056 \| 0.214 \| 0.1129 \| 0.0506 \| 0.0451 \| 0.1066 \| 0.1184 \| 0.3778 \| 0.1941 \| 0.125 \| 0.0 \| 0.0 \| 0.4382 \| 0.594 \| 0.0 \| 0.0 \| 0.0024 \| 0.1167 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.8079 \| 13.0 \| 3016 \| 0.7473 \| 0.0848 \| 0.1559 \| 0.0789 \| 0.2794 \| 0.1307 \| 0.0779 \| 0.078 \| 0.16 \| 0.1727 \| 0.5 \| 0.2668 \| 0.1625 \| 0.0 \| 0.0 \| 0.4983 \| 0.6363 \| 0.0 \| 0.0 \| 0.0103 \| 0.4 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.8079 \| 14.0 \| 3248 \| 0.8514 \| 0.0706 \| 0.1541 \| 0.0485 \| 0.3034 \| 0.1071 \| 0.1046 \| 0.079 \| 0.1726 \| 0.1862 \| 0.4222 \| 0.2556 \| 0.2875 \| 0.0 \| 0.0 \| 0.4149 \| 0.5669 \| 0.0 \| 0.0 \| 0.0089 \| 0.55 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.8079 \| 15.0 \| 3480 \| 0.7615 \| 0.0814 \| 0.1579 \| 0.0709 \| 0.3673 \| 0.1229 \| 0.0691 \| 0.0929 \| 0.161 \| 0.1719 \| 0.4444 \| 0.2177 \| 0.3125 \| 0.0 \| 0.0 \| 0.4774 \| 0.6146 \| 0.0 \| 0.0 \| 0.0109 \| 0.4167 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.7672 \| 16.0 \| 3712 \| 0.7819 \| 0.0786 \| 0.1555 \| 0.0732 \| 0.2782 \| 0.1182 \| 0.1589 \| 0.103 \| 0.1757 \| 0.1878 \| 0.3556 \| 0.2629 \| 0.325 \| 0.0 \| 0.0 \| 0.4557 \| 0.5936 \| 0.0 \| 0.0 \| 0.0159 \| 0.5333 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.7672 \| 17.0 \| 3944 \| 0.7723 \| 0.0807 \| 0.1551 \| 0.0757 \| 0.3302 \| 0.1227 \| 0.0687 \| 0.0812 \| 0.1762 \| 0.1939 \| 0.4 \| 0.274 \| 0.3 \| 0.0 \| 0.0 \| 0.4724 \| 0.6135 \| 0.0 \| 0.0 \| 0.0121 \| 0.55 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.744 \| 18.0 \| 4176 \| 0.7838 \| 0.0784 \| 0.1535 \| 0.0709 \| 0.3594 \| 0.1201 \| 0.0564 \| 0.0865 \| 0.1865 \| 0.2497 \| 0.4 \| 0.3637 \| 0.325 \| 0.0009 \| 0.3 \| 0.4503 \| 0.5982 \| 0.0 \| 0.0 \| 0.0191 \| 0.6 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.744 \| 19.0 \| 4408 \| 0.7714 \| 0.0763 \| 0.1552 \| 0.0625 \| 0.2732 \| 0.116 \| 0.0675 \| 0.0605 \| 0.1696 \| 0.1824 \| 0.4111 \| 0.2502 \| 0.3 \| 0.0 \| 0.0 \| 0.4461 \| 0.5943 \| 0.0 \| 0.0 \| 0.0115 \| 0.5 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.7219 \| 20.0 \| 4640 \| 0.7403 \| 0.0808 \| 0.1543 \| 0.0686 \| 0.2968 \| 0.1251 \| 0.0665 \| 0.0984 \| 0.1889 \| 0.1997 \| 0.5111 \| 0.2859 \| 0.3 \| 0.0 \| 0.0 \| 0.4738 \| 0.6146 \| 0.0 \| 0.0 \| 0.0109 \| 0.5833 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.7219 \| 21.0 \| 4872 \| 0.7421 \| 0.0839 \| 0.1599 \| 0.0803 \| 0.2862 \| 0.2166 \| 0.0451 \| 0.1396 \| 0.2437 \| 0.257 \| 0.5 \| 0.3782 \| 0.275 \| 0.0123 \| 0.4 \| 0.4792 \| 0.6089 \| 0.0 \| 0.0 \| 0.0118 \| 0.5333 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.6955 \| 22.0 \| 5104 \| 0.7459 \| 0.0844 \| 0.1619 \| 0.0753 \| 0.3116 \| 0.1475 \| 0.0462 \| 0.1395 \| 0.2253 \| 0.2342 \| 0.4333 \| 0.3404 \| 0.2875 \| 0.0068 \| 0.25 \| 0.4815 \| 0.6053 \| 0.0 \| 0.0 \| 0.018 \| 0.55 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.6955 \| 23.0 \| 5336 \| 0.7439 \| 0.0844 \| 0.1594 \| 0.0782 \| 0.3246 \| 0.1335 \| 0.0682 \| 0.1559 \| 0.2468 \| 0.2567 \| 0.4667 \| 0.378 \| 0.3 \| 0.0031 \| 0.3 \| 0.4854 \| 0.6071 \| 0.0 \| 0.0 \| 0.018 \| 0.6333 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.6836 \| 24.0 \| 5568 \| 0.7263 \| 0.0879 \| 0.1619 \| 0.0854 \| 0.3509 \| 0.1397 \| 0.0597 \| 0.1718 \| 0.2499 \| 0.2589 \| 0.4333 \| 0.3818 \| 0.275 \| 0.0027 \| 0.3 \| 0.4998 \| 0.6203 \| 0.0 \| 0.0 \| 0.025 \| 0.6333 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.6836 \| 25.0 \| 5800 \| 0.7146 \| 0.0881 \| 0.1632 \| 0.087 \| 0.3606 \| 0.1412 \| 0.0714 \| 0.1518 \| 0.2466 \| 0.2572 \| 0.4667 \| 0.3768 \| 0.3 \| 0.0032 \| 0.3 \| 0.4988 \| 0.6263 \| 0.0 \| 0.0 \| 0.0267 \| 0.6167 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.6671 \| 26.0 \| 6032 \| 0.7180 \| 0.0915 \| 0.1678 \| 0.0918 \| 0.3652 \| 0.2262 \| 0.0483 \| 0.163 \| 0.2506 \| 0.2672 \| 0.5 \| 0.3979 \| 0.2875 \| 0.0186 \| 0.35 \| 0.5069 \| 0.6367 \| 0.0 \| 0.0 \| 0.0235 \| 0.6167 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.6671 \| 27.0 \| 6264 \| 0.7219 \| 0.0907 \| 0.1678 \| 0.0888 \| 0.3596 \| 0.2122 \| 0.0492 \| 0.1375 \| 0.26 \| 0.2709 \| 0.5 \| 0.4012 \| 0.3 \| 0.0184 \| 0.35 \| 0.5014 \| 0.6253 \| 0.0 \| 0.0 \| 0.0243 \| 0.65 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.6671 \| 28.0 \| 6496 \| 0.7177 \| 0.0882 \| 0.1642 \| 0.0866 \| 0.3625 \| 0.1439 \| 0.0505 \| 0.1463 \| 0.2603 \| 0.2715 \| 0.5111 \| 0.4021 \| 0.3 \| 0.0038 \| 0.35 \| 0.4996 \| 0.6288 \| 0.0 \| 0.0 \| 0.0256 \| 0.65 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.656 \| 29.0 \| 6728 \| 0.7257 \| 0.0873 \| 0.1634 \| 0.084 \| 0.355 \| 0.1417 \| 0.0497 \| 0.1433 \| 0.2572 \| 0.2708 \| 0.5 \| 0.401 \| 0.3 \| 0.0039 \| 0.35 \| 0.4962 \| 0.6246 \| 0.0 \| 0.0 \| 0.0237 \| 0.65 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.656 \| 30.0 \| 6960 \| 0.7220 \| 0.0875 \| 0.1634 \| 0.084 \| 0.355 \| 0.1423 \| 0.0499 \| 0.1462 \| 0.2602 \| 0.2709 \| 0.5 \| 0.4013 \| 0.3 \| 0.0039 \| 0.35 \| 0.4971 \| 0.6256 \| 0.0 \| 0.0 \| 0.0239 \| 0.65 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1	[ "cars-trucks-vans", "boat", "camping car", "car", "motorcycle", "other", "pickup", "plane", "tractor", "truck", "van" ]
auduod/detr-resnet-50_finetuned_cppe5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet-50_finetuned_cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
yi-zhao-K/detr-resnet-50_finetuned_test	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet-50_finetuned_test This model was trained from scratch on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 500 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.42.3 - Pytorch 2.3.1+cpu - Datasets 2.20.0 - Tokenizers 0.19.1	[ "blackgum", "chestnut oak", "red maple", "black cherry", "norway spruce", "northern red oak" ]
Charles95/autotrain-detr-cppe-v5	# Model Trained Using AutoTrain - Problem type: Object Detection ## Validation Metrics loss: 1.1211919784545898 map: 0.3566 map_50: 0.7058 map_75: 0.3194 map_small: -1.0 map_medium: 0.2108 map_large: 0.4073 mar_1: 0.3081 mar_10: 0.5415 mar_100: 0.5725 mar_small: -1.0 mar_medium: 0.3701 mar_large: 0.6539	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
machinelearningzuu/emotion_detection_cctv	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # emotion_detection_cctv This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1520 - Map: 0.9392 - Map 50: 0.9998 - Map 75: 0.9948 - Map Small: -1.0 - Map Medium: -1.0 - Map Large: 0.9392 - Mar 1: 0.7807 - Mar 10: 0.9616 - Mar 100: 0.9616 - Mar Small: -1.0 - Mar Medium: -1.0 - Mar Large: 0.9616 - Map Nf: 0.9236 - Mar 100 Nf: 0.9523 - Map F: 0.9547 - Mar 100 F: 0.9708 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Small \| Map Medium \| Map Large \| Mar 1 \| Mar 10 \| Mar 100 \| Mar Small \| Mar Medium \| Mar Large \| Map Nf \| Mar 100 Nf \| Map F \| Mar 100 F \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:---------:\|:----------:\|:---------:\|:------:\|:----------:\|:------:\|:---------:\| \| No log \| 1.0 \| 90 \| 1.8304 \| 0.0733 \| 0.1801 \| 0.038 \| -1.0 \| 0.0 \| 0.0738 \| 0.117 \| 0.4402 \| 0.5369 \| -1.0 \| 0.0 \| 0.5399 \| 0.1099 \| 0.6468 \| 0.0366 \| 0.427 \| \| No log \| 2.0 \| 180 \| 1.3214 \| 0.1882 \| 0.3323 \| 0.2001 \| -1.0 \| -1.0 \| 0.1882 \| 0.2849 \| 0.6302 \| 0.6873 \| -1.0 \| -1.0 \| 0.6873 \| 0.3155 \| 0.7319 \| 0.0609 \| 0.6427 \| \| No log \| 3.0 \| 270 \| 1.1104 \| 0.3338 \| 0.5621 \| 0.3832 \| -1.0 \| -1.0 \| 0.3338 \| 0.3734 \| 0.6759 \| 0.7053 \| -1.0 \| -1.0 \| 0.7053 \| 0.5266 \| 0.7375 \| 0.141 \| 0.673 \| \| No log \| 4.0 \| 360 \| 1.0762 \| 0.3481 \| 0.6067 \| 0.3563 \| -1.0 \| -1.0 \| 0.3481 \| 0.3946 \| 0.6117 \| 0.6466 \| -1.0 \| -1.0 \| 0.6466 \| 0.5588 \| 0.6875 \| 0.1374 \| 0.6056 \| \| No log \| 5.0 \| 450 \| 0.9652 \| 0.3807 \| 0.6111 \| 0.443 \| -1.0 \| 0.0 \| 0.3815 \| 0.3975 \| 0.6352 \| 0.6545 \| -1.0 \| 0.0 \| 0.6578 \| 0.6101 \| 0.7259 \| 0.1513 \| 0.5831 \| \| 4.5043 \| 6.0 \| 540 \| 0.9340 \| 0.463 \| 0.7541 \| 0.5336 \| -1.0 \| 0.0 \| 0.4652 \| 0.4403 \| 0.675 \| 0.6853 \| -1.0 \| 0.0 \| 0.689 \| 0.5897 \| 0.7167 \| 0.3364 \| 0.6539 \| \| 4.5043 \| 7.0 \| 630 \| 0.9507 \| 0.4406 \| 0.7489 \| 0.4748 \| 0.0 \| -1.0 \| 0.442 \| 0.411 \| 0.6556 \| 0.6616 \| 0.0 \| -1.0 \| 0.6633 \| 0.568 \| 0.7042 \| 0.3132 \| 0.6191 \| \| 4.5043 \| 8.0 \| 720 \| 0.7813 \| 0.5193 \| 0.8038 \| 0.6507 \| -1.0 \| -1.0 \| 0.5194 \| 0.4466 \| 0.7254 \| 0.7274 \| -1.0 \| -1.0 \| 0.7274 \| 0.6451 \| 0.7324 \| 0.3936 \| 0.7225 \| \| 4.5043 \| 9.0 \| 810 \| 0.7608 \| 0.5436 \| 0.8306 \| 0.6426 \| -1.0 \| -1.0 \| 0.5436 \| 0.4949 \| 0.7161 \| 0.7166 \| -1.0 \| -1.0 \| 0.7166 \| 0.6642 \| 0.7366 \| 0.423 \| 0.6966 \| \| 4.5043 \| 10.0 \| 900 \| 0.7774 \| 0.5606 \| 0.858 \| 0.6495 \| -1.0 \| 0.0 \| 0.5619 \| 0.4995 \| 0.7321 \| 0.735 \| -1.0 \| 0.0 \| 0.7367 \| 0.6378 \| 0.7329 \| 0.4833 \| 0.7371 \| \| 4.5043 \| 11.0 \| 990 \| 0.7060 \| 0.5533 \| 0.8493 \| 0.6853 \| -1.0 \| -1.0 \| 0.5533 \| 0.5187 \| 0.7171 \| 0.7174 \| -1.0 \| -1.0 \| 0.7174 \| 0.6789 \| 0.7583 \| 0.4278 \| 0.6764 \| \| 0.8542 \| 12.0 \| 1080 \| 0.7088 \| 0.5999 \| 0.9146 \| 0.7448 \| -1.0 \| 0.0 \| 0.6031 \| 0.5423 \| 0.7027 \| 0.7043 \| -1.0 \| 0.0 \| 0.7081 \| 0.6665 \| 0.7435 \| 0.5334 \| 0.6652 \| \| 0.8542 \| 13.0 \| 1170 \| 0.6888 \| 0.6113 \| 0.9251 \| 0.7798 \| -1.0 \| 0.0 \| 0.6128 \| 0.5378 \| 0.7202 \| 0.7209 \| -1.0 \| 0.0 \| 0.7227 \| 0.6805 \| 0.7486 \| 0.5422 \| 0.6933 \| \| 0.8542 \| 14.0 \| 1260 \| 0.6578 \| 0.6267 \| 0.931 \| 0.7653 \| -1.0 \| -1.0 \| 0.627 \| 0.5623 \| 0.7258 \| 0.7272 \| -1.0 \| -1.0 \| 0.7272 \| 0.6656 \| 0.7375 \| 0.5878 \| 0.7169 \| \| 0.8542 \| 15.0 \| 1350 \| 0.6039 \| 0.6602 \| 0.954 \| 0.8066 \| -1.0 \| -1.0 \| 0.6604 \| 0.5807 \| 0.7465 \| 0.7474 \| -1.0 \| -1.0 \| 0.7474 \| 0.71 \| 0.769 \| 0.6104 \| 0.7258 \| \| 0.8542 \| 16.0 \| 1440 \| 0.5776 \| 0.6653 \| 0.977 \| 0.8311 \| -1.0 \| -1.0 \| 0.6653 \| 0.5896 \| 0.7402 \| 0.7407 \| -1.0 \| -1.0 \| 0.7407 \| 0.7063 \| 0.7623 \| 0.6244 \| 0.7191 \| \| 0.6835 \| 17.0 \| 1530 \| 0.5971 \| 0.6577 \| 0.9538 \| 0.8235 \| -1.0 \| -1.0 \| 0.6577 \| 0.5731 \| 0.7449 \| 0.7478 \| -1.0 \| -1.0 \| 0.7478 \| 0.6995 \| 0.7619 \| 0.6158 \| 0.7337 \| \| 0.6835 \| 18.0 \| 1620 \| 0.6152 \| 0.6566 \| 0.9211 \| 0.8114 \| -1.0 \| -1.0 \| 0.6566 \| 0.5949 \| 0.7604 \| 0.7615 \| -1.0 \| -1.0 \| 0.7615 \| 0.6964 \| 0.7657 \| 0.6167 \| 0.7573 \| \| 0.6835 \| 19.0 \| 1710 \| 0.6240 \| 0.6666 \| 0.9503 \| 0.8598 \| -1.0 \| 0.0 \| 0.6811 \| 0.5829 \| 0.7345 \| 0.7363 \| -1.0 \| 0.0 \| 0.7532 \| 0.6962 \| 0.7569 \| 0.637 \| 0.7157 \| \| 0.6835 \| 20.0 \| 1800 \| 0.5781 \| 0.6854 \| 0.9693 \| 0.8822 \| -1.0 \| -1.0 \| 0.6856 \| 0.6025 \| 0.7605 \| 0.7605 \| -1.0 \| -1.0 \| 0.7605 \| 0.704 \| 0.7625 \| 0.6668 \| 0.7584 \| \| 0.6835 \| 21.0 \| 1890 \| 0.5097 \| 0.7255 \| 0.9829 \| 0.9169 \| -1.0 \| 0.075 \| 0.7321 \| 0.6342 \| 0.786 \| 0.7862 \| -1.0 \| 0.15 \| 0.7918 \| 0.733 \| 0.787 \| 0.718 \| 0.7854 \| \| 0.6835 \| 22.0 \| 1980 \| 0.5694 \| 0.68 \| 0.963 \| 0.8484 \| -1.0 \| -1.0 \| 0.68 \| 0.6075 \| 0.7613 \| 0.762 \| -1.0 \| -1.0 \| 0.762 \| 0.7047 \| 0.7611 \| 0.6554 \| 0.7629 \| \| 0.5958 \| 23.0 \| 2070 \| 0.5036 \| 0.7132 \| 0.9856 \| 0.8779 \| -1.0 \| 0.0 \| 0.7152 \| 0.6227 \| 0.778 \| 0.7802 \| -1.0 \| 0.0 \| 0.782 \| 0.7326 \| 0.7884 \| 0.6939 \| 0.7719 \| \| 0.5958 \| 24.0 \| 2160 \| 0.5662 \| 0.6767 \| 0.9519 \| 0.8501 \| -1.0 \| 0.0 \| 0.6787 \| 0.6025 \| 0.7635 \| 0.7635 \| -1.0 \| 0.0 \| 0.7653 \| 0.7101 \| 0.7708 \| 0.6434 \| 0.7562 \| \| 0.5958 \| 25.0 \| 2250 \| 0.5620 \| 0.7029 \| 0.9719 \| 0.9007 \| -1.0 \| -1.0 \| 0.703 \| 0.6149 \| 0.7708 \| 0.7713 \| -1.0 \| -1.0 \| 0.7713 \| 0.7112 \| 0.7796 \| 0.6946 \| 0.7629 \| \| 0.5958 \| 26.0 \| 2340 \| 0.5236 \| 0.7173 \| 0.9638 \| 0.9165 \| -1.0 \| 0.016 \| 0.7187 \| 0.6328 \| 0.7883 \| 0.7886 \| -1.0 \| 0.4 \| 0.7895 \| 0.7285 \| 0.7907 \| 0.706 \| 0.7864 \| \| 0.5958 \| 27.0 \| 2430 \| 0.4734 \| 0.7433 \| 0.9886 \| 0.94 \| -1.0 \| -1.0 \| 0.7434 \| 0.6431 \| 0.8014 \| 0.8021 \| -1.0 \| -1.0 \| 0.8021 \| 0.7472 \| 0.8042 \| 0.7395 \| 0.8 \| \| 0.5447 \| 28.0 \| 2520 \| 0.4826 \| 0.7364 \| 0.9868 \| 0.942 \| -1.0 \| -1.0 \| 0.7364 \| 0.6417 \| 0.7931 \| 0.7941 \| -1.0 \| -1.0 \| 0.7941 \| 0.7555 \| 0.8083 \| 0.7172 \| 0.7798 \| \| 0.5447 \| 29.0 \| 2610 \| 0.5002 \| 0.7229 \| 0.9827 \| 0.9309 \| -1.0 \| 0.0 \| 0.7279 \| 0.6386 \| 0.7812 \| 0.7812 \| -1.0 \| 0.0 \| 0.7856 \| 0.7501 \| 0.7995 \| 0.6957 \| 0.7629 \| \| 0.5447 \| 30.0 \| 2700 \| 0.4752 \| 0.7529 \| 0.9831 \| 0.937 \| -1.0 \| -1.0 \| 0.7529 \| 0.6405 \| 0.8089 \| 0.8091 \| -1.0 \| -1.0 \| 0.8091 \| 0.7639 \| 0.8093 \| 0.7419 \| 0.809 \| \| 0.5447 \| 31.0 \| 2790 \| 0.4524 \| 0.7208 \| 0.9756 \| 0.9124 \| -1.0 \| -1.0 \| 0.7209 \| 0.6312 \| 0.7916 \| 0.7933 \| -1.0 \| -1.0 \| 0.7933 \| 0.7589 \| 0.8157 \| 0.6827 \| 0.7708 \| \| 0.5447 \| 32.0 \| 2880 \| 0.4278 \| 0.752 \| 0.9906 \| 0.9451 \| -1.0 \| -1.0 \| 0.752 \| 0.6536 \| 0.8061 \| 0.8066 \| -1.0 \| -1.0 \| 0.8066 \| 0.7781 \| 0.8255 \| 0.7259 \| 0.7876 \| \| 0.5447 \| 33.0 \| 2970 \| 0.4699 \| 0.7509 \| 0.9883 \| 0.9441 \| -1.0 \| -1.0 \| 0.751 \| 0.651 \| 0.8109 \| 0.8114 \| -1.0 \| -1.0 \| 0.8114 \| 0.7505 \| 0.8014 \| 0.7514 \| 0.8213 \| \| 0.5069 \| 34.0 \| 3060 \| 0.5125 \| 0.7178 \| 0.9529 \| 0.9109 \| -1.0 \| 0.0 \| 0.7277 \| 0.6415 \| 0.7884 \| 0.793 \| -1.0 \| 0.0 \| 0.8041 \| 0.7346 \| 0.7917 \| 0.7011 \| 0.7943 \| \| 0.5069 \| 35.0 \| 3150 \| 0.5031 \| 0.7323 \| 0.9687 \| 0.912 \| -1.0 \| 0.0 \| 0.7407 \| 0.643 \| 0.7889 \| 0.7906 \| -1.0 \| 0.0 \| 0.7994 \| 0.764 \| 0.8148 \| 0.7005 \| 0.7663 \| \| 0.5069 \| 36.0 \| 3240 \| 0.4753 \| 0.7423 \| 0.9856 \| 0.9226 \| -1.0 \| 0.0 \| 0.7445 \| 0.6481 \| 0.7989 \| 0.7989 \| -1.0 \| 0.0 \| 0.8007 \| 0.7594 \| 0.8056 \| 0.7252 \| 0.7921 \| \| 0.5069 \| 37.0 \| 3330 \| 0.4264 \| 0.7617 \| 0.9804 \| 0.9631 \| -1.0 \| 0.0 \| 0.767 \| 0.6575 \| 0.815 \| 0.815 \| -1.0 \| 0.0 \| 0.8195 \| 0.79 \| 0.8333 \| 0.7335 \| 0.7966 \| \| 0.5069 \| 38.0 \| 3420 \| 0.3995 \| 0.7762 \| 0.9927 \| 0.9628 \| -1.0 \| -1.0 \| 0.7762 \| 0.6747 \| 0.8279 \| 0.8279 \| -1.0 \| -1.0 \| 0.8279 \| 0.7764 \| 0.8233 \| 0.776 \| 0.8326 \| \| 0.4669 \| 39.0 \| 3510 \| 0.4286 \| 0.7475 \| 0.9772 \| 0.9464 \| -1.0 \| 0.0 \| 0.7553 \| 0.6506 \| 0.8057 \| 0.8057 \| -1.0 \| 0.0 \| 0.812 \| 0.7851 \| 0.8293 \| 0.71 \| 0.782 \| \| 0.4669 \| 40.0 \| 3600 \| 0.3864 \| 0.7726 \| 0.9889 \| 0.9608 \| -1.0 \| 0.0 \| 0.7774 \| 0.6696 \| 0.8235 \| 0.8235 \| -1.0 \| 0.0 \| 0.8281 \| 0.7991 \| 0.8505 \| 0.7461 \| 0.7966 \| \| 0.4669 \| 41.0 \| 3690 \| 0.3316 \| 0.8136 \| 0.9983 \| 0.9729 \| -1.0 \| -1.0 \| 0.8136 \| 0.6932 \| 0.8572 \| 0.8572 \| -1.0 \| -1.0 \| 0.8572 \| 0.8231 \| 0.8639 \| 0.8041 \| 0.8506 \| \| 0.4669 \| 42.0 \| 3780 \| 0.3657 \| 0.7848 \| 0.9762 \| 0.9601 \| -1.0 \| 0.0 \| 0.7936 \| 0.6833 \| 0.8424 \| 0.8424 \| -1.0 \| 0.0 \| 0.8518 \| 0.8105 \| 0.86 \| 0.759 \| 0.8247 \| \| 0.4669 \| 43.0 \| 3870 \| 0.3636 \| 0.7887 \| 0.9952 \| 0.9636 \| -1.0 \| -1.0 \| 0.7888 \| 0.6778 \| 0.8408 \| 0.8408 \| -1.0 \| -1.0 \| 0.8408 \| 0.7958 \| 0.8491 \| 0.7816 \| 0.8326 \| \| 0.4669 \| 44.0 \| 3960 \| 0.3760 \| 0.7813 \| 0.9871 \| 0.9434 \| -1.0 \| -1.0 \| 0.7813 \| 0.6789 \| 0.8321 \| 0.8327 \| -1.0 \| -1.0 \| 0.8327 \| 0.8005 \| 0.844 \| 0.7621 \| 0.8213 \| \| 0.4227 \| 45.0 \| 4050 \| 0.3942 \| 0.7916 \| 0.9925 \| 0.9407 \| -1.0 \| 0.02 \| 0.7937 \| 0.6846 \| 0.8419 \| 0.8421 \| -1.0 \| 0.1 \| 0.8438 \| 0.7844 \| 0.8315 \| 0.7988 \| 0.8528 \| \| 0.4227 \| 46.0 \| 4140 \| 0.3440 \| 0.8051 \| 0.9979 \| 0.9767 \| -1.0 \| -1.0 \| 0.8051 \| 0.6948 \| 0.8578 \| 0.8578 \| -1.0 \| -1.0 \| 0.8578 \| 0.8053 \| 0.8537 \| 0.8049 \| 0.8618 \| \| 0.4227 \| 47.0 \| 4230 \| 0.3353 \| 0.8129 \| 0.9836 \| 0.9605 \| -1.0 \| 0.0 \| 0.8172 \| 0.6903 \| 0.8543 \| 0.8543 \| -1.0 \| 0.0 \| 0.8591 \| 0.8318 \| 0.8727 \| 0.794 \| 0.836 \| \| 0.4227 \| 48.0 \| 4320 \| 0.3638 \| 0.8048 \| 0.9915 \| 0.9616 \| -1.0 \| 0.0 \| 0.8069 \| 0.6896 \| 0.8514 \| 0.8514 \| -1.0 \| 0.0 \| 0.8534 \| 0.8003 \| 0.8444 \| 0.8092 \| 0.8584 \| \| 0.4227 \| 49.0 \| 4410 \| 0.3310 \| 0.8218 \| 0.9982 \| 0.9699 \| -1.0 \| -1.0 \| 0.8218 \| 0.6972 \| 0.865 \| 0.8662 \| -1.0 \| -1.0 \| 0.8662 \| 0.8209 \| 0.8694 \| 0.8226 \| 0.8629 \| \| 0.3807 \| 50.0 \| 4500 \| 0.3393 \| 0.8108 \| 0.9796 \| 0.9528 \| -1.0 \| -1.0 \| 0.8108 \| 0.6952 \| 0.8515 \| 0.8515 \| -1.0 \| -1.0 \| 0.8515 \| 0.8227 \| 0.8648 \| 0.799 \| 0.8382 \| \| 0.3807 \| 51.0 \| 4590 \| 0.3205 \| 0.8225 \| 0.9941 \| 0.9719 \| -1.0 \| -1.0 \| 0.8225 \| 0.7047 \| 0.8631 \| 0.8631 \| -1.0 \| -1.0 \| 0.8631 \| 0.822 \| 0.8644 \| 0.8229 \| 0.8618 \| \| 0.3807 \| 52.0 \| 4680 \| 0.3399 \| 0.8078 \| 0.9886 \| 0.9539 \| -1.0 \| -1.0 \| 0.8078 \| 0.6925 \| 0.8535 \| 0.8535 \| -1.0 \| -1.0 \| 0.8535 \| 0.8261 \| 0.8722 \| 0.7894 \| 0.8348 \| \| 0.3807 \| 53.0 \| 4770 \| 0.3614 \| 0.8174 \| 0.968 \| 0.9492 \| -1.0 \| 0.0333 \| 0.8223 \| 0.6938 \| 0.855 \| 0.855 \| -1.0 \| 0.1 \| 0.8614 \| 0.8171 \| 0.856 \| 0.8177 \| 0.8539 \| \| 0.3807 \| 54.0 \| 4860 \| 0.3446 \| 0.8135 \| 0.9906 \| 0.9588 \| -1.0 \| 0.0 \| 0.816 \| 0.6978 \| 0.8651 \| 0.8651 \| -1.0 \| 0.0 \| 0.8671 \| 0.8183 \| 0.8639 \| 0.8086 \| 0.8663 \| \| 0.3807 \| 55.0 \| 4950 \| 0.3518 \| 0.8203 \| 0.9867 \| 0.9516 \| -1.0 \| -1.0 \| 0.8203 \| 0.6975 \| 0.8615 \| 0.8615 \| -1.0 \| -1.0 \| 0.8615 \| 0.8355 \| 0.8736 \| 0.8051 \| 0.8494 \| \| 0.366 \| 56.0 \| 5040 \| 0.2746 \| 0.8359 \| 0.9998 \| 0.9705 \| -1.0 \| -1.0 \| 0.8359 \| 0.7175 \| 0.882 \| 0.882 \| -1.0 \| -1.0 \| 0.882 \| 0.8422 \| 0.8875 \| 0.8296 \| 0.8764 \| \| 0.366 \| 57.0 \| 5130 \| 0.2882 \| 0.8351 \| 0.9847 \| 0.9698 \| -1.0 \| 0.0 \| 0.8392 \| 0.707 \| 0.8763 \| 0.8763 \| -1.0 \| 0.0 \| 0.8813 \| 0.847 \| 0.8875 \| 0.8232 \| 0.8652 \| \| 0.366 \| 58.0 \| 5220 \| 0.3049 \| 0.8414 \| 0.9945 \| 0.9679 \| -1.0 \| 0.15 \| 0.8426 \| 0.7083 \| 0.8776 \| 0.8776 \| -1.0 \| 0.3 \| 0.879 \| 0.8481 \| 0.8833 \| 0.8348 \| 0.8719 \| \| 0.366 \| 59.0 \| 5310 \| 0.3217 \| 0.821 \| 0.9892 \| 0.9743 \| -1.0 \| -1.0 \| 0.821 \| 0.6989 \| 0.8643 \| 0.8643 \| -1.0 \| -1.0 \| 0.8643 \| 0.8278 \| 0.8713 \| 0.8141 \| 0.8573 \| \| 0.366 \| 60.0 \| 5400 \| 0.2654 \| 0.8564 \| 0.9996 \| 0.9797 \| -1.0 \| -1.0 \| 0.8564 \| 0.7286 \| 0.8941 \| 0.8941 \| -1.0 \| -1.0 \| 0.8941 \| 0.8585 \| 0.8972 \| 0.8544 \| 0.891 \| \| 0.366 \| 61.0 \| 5490 \| 0.2880 \| 0.8637 \| 0.9998 \| 0.9787 \| -1.0 \| 0.1 \| 0.8662 \| 0.7285 \| 0.9 \| 0.9 \| -1.0 \| 0.1 \| 0.9018 \| 0.8521 \| 0.8898 \| 0.8754 \| 0.9101 \| \| 0.3186 \| 62.0 \| 5580 \| 0.2817 \| 0.8638 \| 0.9955 \| 0.9703 \| -1.0 \| -1.0 \| 0.8638 \| 0.7322 \| 0.9086 \| 0.9086 \| -1.0 \| -1.0 \| 0.9086 \| 0.8572 \| 0.8958 \| 0.8704 \| 0.9213 \| \| 0.3186 \| 63.0 \| 5670 \| 0.2560 \| 0.868 \| 0.9951 \| 0.9807 \| -1.0 \| -1.0 \| 0.868 \| 0.7366 \| 0.9099 \| 0.9099 \| -1.0 \| -1.0 \| 0.9099 \| 0.8672 \| 0.9074 \| 0.8688 \| 0.9124 \| \| 0.3186 \| 64.0 \| 5760 \| 0.2785 \| 0.8516 \| 0.9831 \| 0.9709 \| -1.0 \| 0.0 \| 0.8539 \| 0.7251 \| 0.897 \| 0.897 \| -1.0 \| 0.0 \| 0.8991 \| 0.854 \| 0.894 \| 0.8491 \| 0.9 \| \| 0.3186 \| 65.0 \| 5850 \| 0.2623 \| 0.8618 \| 0.9891 \| 0.9501 \| -1.0 \| -1.0 \| 0.8618 \| 0.7315 \| 0.9035 \| 0.9035 \| -1.0 \| -1.0 \| 0.9035 \| 0.8642 \| 0.9069 \| 0.8594 \| 0.9 \| \| 0.3186 \| 66.0 \| 5940 \| 0.2568 \| 0.8695 \| 0.9895 \| 0.9607 \| -1.0 \| -1.0 \| 0.8695 \| 0.7368 \| 0.9079 \| 0.9079 \| -1.0 \| -1.0 \| 0.9079 \| 0.8728 \| 0.9102 \| 0.8663 \| 0.9056 \| \| 0.2959 \| 67.0 \| 6030 \| 0.2528 \| 0.8736 \| 0.9982 \| 0.9643 \| -1.0 \| -1.0 \| 0.8736 \| 0.7344 \| 0.9082 \| 0.9091 \| -1.0 \| -1.0 \| 0.9091 \| 0.8793 \| 0.9148 \| 0.868 \| 0.9034 \| \| 0.2959 \| 68.0 \| 6120 \| 0.2517 \| 0.8736 \| 0.9983 \| 0.976 \| -1.0 \| -1.0 \| 0.8736 \| 0.7375 \| 0.9102 \| 0.9102 \| -1.0 \| -1.0 \| 0.9102 \| 0.8713 \| 0.9093 \| 0.8758 \| 0.9112 \| \| 0.2959 \| 69.0 \| 6210 \| 0.2818 \| 0.8636 \| 0.992 \| 0.9578 \| -1.0 \| 0.2515 \| 0.8717 \| 0.7353 \| 0.9003 \| 0.9003 \| -1.0 \| 0.25 \| 0.9078 \| 0.8607 \| 0.8972 \| 0.8664 \| 0.9034 \| \| 0.2959 \| 70.0 \| 6300 \| 0.2222 \| 0.8915 \| 0.9998 \| 0.98 \| -1.0 \| -1.0 \| 0.8915 \| 0.753 \| 0.9261 \| 0.9261 \| -1.0 \| -1.0 \| 0.9261 \| 0.8826 \| 0.9208 \| 0.9004 \| 0.9315 \| \| 0.2959 \| 71.0 \| 6390 \| 0.2597 \| 0.8699 \| 0.9868 \| 0.9517 \| 0.0 \| 0.25 \| 0.8733 \| 0.7353 \| 0.9094 \| 0.9094 \| 0.0 \| 0.5 \| 0.9124 \| 0.8745 \| 0.9097 \| 0.8653 \| 0.909 \| \| 0.2959 \| 72.0 \| 6480 \| 0.2019 \| 0.9087 \| 0.9992 \| 0.9992 \| -1.0 \| -1.0 \| 0.9087 \| 0.7603 \| 0.9382 \| 0.9382 \| -1.0 \| -1.0 \| 0.9382 \| 0.9001 \| 0.9315 \| 0.9172 \| 0.9449 \| \| 0.2565 \| 73.0 \| 6570 \| 0.2035 \| 0.8993 \| 0.9997 \| 0.9927 \| -1.0 \| -1.0 \| 0.8993 \| 0.7602 \| 0.9331 \| 0.9331 \| -1.0 \| -1.0 \| 0.9331 \| 0.8908 \| 0.9269 \| 0.9078 \| 0.9393 \| \| 0.2565 \| 74.0 \| 6660 \| 0.2539 \| 0.8807 \| 0.9987 \| 0.959 \| -1.0 \| -1.0 \| 0.8808 \| 0.7367 \| 0.9145 \| 0.9145 \| -1.0 \| -1.0 \| 0.9145 \| 0.8803 \| 0.9144 \| 0.8812 \| 0.9146 \| \| 0.2565 \| 75.0 \| 6750 \| 0.1972 \| 0.8966 \| 1.0 \| 0.9729 \| -1.0 \| -1.0 \| 0.8966 \| 0.7539 \| 0.9299 \| 0.9299 \| -1.0 \| -1.0 \| 0.9299 \| 0.9008 \| 0.9306 \| 0.8925 \| 0.9292 \| \| 0.2565 \| 76.0 \| 6840 \| 0.2068 \| 0.9105 \| 0.9995 \| 0.9936 \| -1.0 \| -1.0 \| 0.9105 \| 0.7648 \| 0.9411 \| 0.9411 \| -1.0 \| -1.0 \| 0.9411 \| 0.9042 \| 0.9361 \| 0.9169 \| 0.9461 \| \| 0.2565 \| 77.0 \| 6930 \| 0.2286 \| 0.8966 \| 0.9996 \| 0.964 \| -1.0 \| -1.0 \| 0.8966 \| 0.7543 \| 0.9271 \| 0.9271 \| -1.0 \| -1.0 \| 0.9271 \| 0.8868 \| 0.9227 \| 0.9064 \| 0.9315 \| \| 0.246 \| 78.0 \| 7020 \| 0.2135 \| 0.902 \| 0.9899 \| 0.9561 \| -1.0 \| 0.0 \| 0.9083 \| 0.7514 \| 0.928 \| 0.928 \| -1.0 \| 0.0 \| 0.9332 \| 0.901 \| 0.9324 \| 0.903 \| 0.9236 \| \| 0.246 \| 79.0 \| 7110 \| 0.2176 \| 0.9115 \| 0.9894 \| 0.9795 \| -1.0 \| 0.0 \| 0.9196 \| 0.7649 \| 0.9424 \| 0.9424 \| -1.0 \| 0.0 \| 0.9478 \| 0.8957 \| 0.9319 \| 0.9273 \| 0.9528 \| \| 0.246 \| 80.0 \| 7200 \| 0.1864 \| 0.9307 \| 1.0 \| 0.9827 \| -1.0 \| -1.0 \| 0.9307 \| 0.7734 \| 0.9528 \| 0.9528 \| -1.0 \| -1.0 \| 0.9528 \| 0.9082 \| 0.937 \| 0.9532 \| 0.9685 \| \| 0.246 \| 81.0 \| 7290 \| 0.1990 \| 0.9179 \| 0.9927 \| 0.9802 \| -1.0 \| 0.0 \| 0.9222 \| 0.7646 \| 0.9468 \| 0.9468 \| -1.0 \| 0.0 \| 0.9511 \| 0.897 \| 0.9329 \| 0.9389 \| 0.9607 \| \| 0.246 \| 82.0 \| 7380 \| 0.1954 \| 0.9117 \| 0.9882 \| 0.9734 \| -1.0 \| 0.0375 \| 0.9212 \| 0.7632 \| 0.943 \| 0.943 \| -1.0 \| 0.15 \| 0.9489 \| 0.8915 \| 0.931 \| 0.932 \| 0.9551 \| \| 0.246 \| 83.0 \| 7470 \| 0.1765 \| 0.9221 \| 0.9979 \| 0.9831 \| -1.0 \| -1.0 \| 0.9221 \| 0.7683 \| 0.9483 \| 0.9486 \| -1.0 \| -1.0 \| 0.9486 \| 0.9169 \| 0.9477 \| 0.9272 \| 0.9494 \| \| 0.2126 \| 84.0 \| 7560 \| 0.2004 \| 0.9092 \| 0.995 \| 0.9729 \| -1.0 \| 0.0 \| 0.9131 \| 0.7576 \| 0.9378 \| 0.9378 \| -1.0 \| 0.0 \| 0.9399 \| 0.9078 \| 0.9384 \| 0.9106 \| 0.9371 \| \| 0.2126 \| 85.0 \| 7650 \| 0.1721 \| 0.9352 \| 0.9963 \| 0.9864 \| -1.0 \| -1.0 \| 0.9352 \| 0.7757 \| 0.959 \| 0.959 \| -1.0 \| -1.0 \| 0.959 \| 0.9229 \| 0.9486 \| 0.9475 \| 0.9693 \| \| 0.2126 \| 86.0 \| 7740 \| 0.1835 \| 0.9187 \| 0.9994 \| 0.9699 \| -1.0 \| -1.0 \| 0.9187 \| 0.7621 \| 0.9452 \| 0.9452 \| -1.0 \| -1.0 \| 0.9452 \| 0.9072 \| 0.9398 \| 0.9303 \| 0.9506 \| \| 0.2126 \| 87.0 \| 7830 \| 0.1892 \| 0.9232 \| 0.9947 \| 0.9738 \| -1.0 \| 0.0 \| 0.9257 \| 0.7726 \| 0.9483 \| 0.9483 \| -1.0 \| 0.0 \| 0.9505 \| 0.9071 \| 0.937 \| 0.9393 \| 0.9596 \| \| 0.2126 \| 88.0 \| 7920 \| 0.1753 \| 0.9186 \| 0.9901 \| 0.9851 \| -1.0 \| -1.0 \| 0.9186 \| 0.7667 \| 0.9482 \| 0.9482 \| -1.0 \| -1.0 \| 0.9482 \| 0.912 \| 0.9435 \| 0.9253 \| 0.9528 \| \| 0.2316 \| 89.0 \| 8010 \| 0.1709 \| 0.9268 \| 0.9998 \| 0.9793 \| -1.0 \| -1.0 \| 0.9268 \| 0.7707 \| 0.9528 \| 0.9528 \| -1.0 \| -1.0 \| 0.9528 \| 0.916 \| 0.9472 \| 0.9375 \| 0.9584 \| \| 0.2316 \| 90.0 \| 8100 \| 0.1818 \| 0.9206 \| 0.9987 \| 0.9694 \| -1.0 \| -1.0 \| 0.9206 \| 0.7615 \| 0.9446 \| 0.9446 \| -1.0 \| -1.0 \| 0.9446 \| 0.9197 \| 0.9477 \| 0.9215 \| 0.9416 \| \| 0.2316 \| 91.0 \| 8190 \| 0.1847 \| 0.9161 \| 0.9845 \| 0.9737 \| -1.0 \| 0.0 \| 0.923 \| 0.7618 \| 0.9413 \| 0.9413 \| -1.0 \| 0.0 \| 0.9488 \| 0.9123 \| 0.9398 \| 0.9198 \| 0.9427 \| \| 0.2316 \| 92.0 \| 8280 \| 0.1540 \| 0.9446 \| 0.9992 \| 0.9971 \| -1.0 \| -1.0 \| 0.9446 \| 0.7812 \| 0.9623 \| 0.9623 \| -1.0 \| -1.0 \| 0.9623 \| 0.9377 \| 0.9583 \| 0.9516 \| 0.9663 \| \| 0.2316 \| 93.0 \| 8370 \| 0.1645 \| 0.9343 \| 0.9995 \| 0.9739 \| -1.0 \| -1.0 \| 0.9343 \| 0.7773 \| 0.9571 \| 0.9571 \| -1.0 \| -1.0 \| 0.9571 \| 0.9146 \| 0.9444 \| 0.954 \| 0.9697 \| \| 0.2316 \| 94.0 \| 8460 \| 0.1992 \| 0.9212 \| 0.9881 \| 0.962 \| -1.0 \| 0.6 \| 0.9231 \| 0.7686 \| 0.9473 \| 0.9473 \| -1.0 \| 0.6 \| 0.9493 \| 0.9004 \| 0.9316 \| 0.9419 \| 0.9629 \| \| 0.2112 \| 95.0 \| 8550 \| 0.1736 \| 0.9206 \| 0.9858 \| 0.9745 \| -1.0 \| 0.0112 \| 0.9319 \| 0.7639 \| 0.9449 \| 0.9449 \| -1.0 \| 0.1 \| 0.9545 \| 0.9246 \| 0.9505 \| 0.9165 \| 0.9393 \| \| 0.2112 \| 96.0 \| 8640 \| 0.1558 \| 0.9318 \| 0.9997 \| 0.9793 \| -1.0 \| -1.0 \| 0.9318 \| 0.7795 \| 0.9571 \| 0.9571 \| -1.0 \| -1.0 \| 0.9571 \| 0.9191 \| 0.9491 \| 0.9445 \| 0.9652 \| \| 0.2112 \| 97.0 \| 8730 \| 0.1535 \| 0.9296 \| 0.9901 \| 0.976 \| -1.0 \| 0.05 \| 0.9398 \| 0.7746 \| 0.9573 \| 0.9573 \| -1.0 \| 0.05 \| 0.9647 \| 0.9253 \| 0.9528 \| 0.9339 \| 0.9618 \| \| 0.2112 \| 98.0 \| 8820 \| 0.2102 \| 0.9071 \| 0.9828 \| 0.9617 \| 0.0 \| 0.0 \| 0.9146 \| 0.7557 \| 0.9351 \| 0.9351 \| 0.0 \| 0.0 \| 0.9426 \| 0.9034 \| 0.9332 \| 0.9107 \| 0.9371 \| \| 0.2112 \| 99.0 \| 8910 \| 0.1861 \| 0.9205 \| 0.9969 \| 0.9626 \| -1.0 \| -1.0 \| 0.9205 \| 0.7618 \| 0.943 \| 0.943 \| -1.0 \| -1.0 \| 0.943 \| 0.9134 \| 0.9421 \| 0.9275 \| 0.9438 \| \| 0.2125 \| 100.0 \| 9000 \| 0.1520 \| 0.9392 \| 0.9998 \| 0.9948 \| -1.0 \| -1.0 \| 0.9392 \| 0.7807 \| 0.9616 \| 0.9616 \| -1.0 \| -1.0 \| 0.9616 \| 0.9236 \| 0.9523 \| 0.9547 \| 0.9708 \| ### Framework versions - Transformers 4.42.3 - Pytorch 2.3.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1	[ "nf", "f" ]
glacialfire/detr-finetuned-balloon-v2	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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glacialfire/detr-finetuned	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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glacialfire/detr-finetuned1700	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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glacialfire/detr-finetuned800	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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glacialfire/detr-finetuned3600	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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lucasphilippe14/model_finetuned_VinBigData	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # model_finetuned_VinBigData This model is a fine-tuned version of [mserrasa/detr-resnet-50_finetuned_cppe5](https://huggingface.co/mserrasa/detr-resnet-50_finetuned_cppe5) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.42.4 - Pytorch 2.3.1+cu121 - Datasets 2.12.0 - Tokenizers 0.19.1	[ "aortic enlargement", "cardiomegaly", "nodule/mass", "pleural thickening", "pulmonary fibrosis" ]
mthandazo/detr-resnet-50-hardhat-finetuned	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet-50-hardhat-finetuned This model is a fine-tuned version of [facebook/detr-resnet-50-dc5](https://huggingface.co/facebook/detr-resnet-50-dc5) on the anindya64/hardhat dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.42.4 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1	[ "head", "helmet", "person" ]
tttarun/detr-resnet-50_finetuned_cppe5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet-50_finetuned_cppe5 This model is a fine-tuned version of [tttarun/detr-resnet-50_finetuned_cppe5](https://huggingface.co/tttarun/detr-resnet-50_finetuned_cppe5) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.42.4 - Pytorch 2.3.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1	[ "diseases", "ballooning", "fibrosis", "inflammation", "steatosis" ]
abhishekrn/resnet-stuff	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "n/a", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "n/a", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "n/a", "backpack", "umbrella", "n/a", "n/a", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "n/a", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "n/a", "dining table", "n/a", "n/a", "toilet", "n/a", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "n/a", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush" ]
niujiahui/detr-finetuned-cppe-5-10k-steps	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-finetuned-cppe-5-10k-steps This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset. It achieves the following results on the evaluation set: - Loss: 1.6932 - Map: 0.1289 - Map 50: 0.2606 - Map 75: 0.1098 - Map Small: 0.0355 - Map Medium: 0.1082 - Map Large: 0.1676 - Mar 1: 0.1429 - Mar 10: 0.2628 - Mar 100: 0.2869 - Mar Small: 0.1256 - Mar Medium: 0.2299 - Mar Large: 0.3635 - Map Coverall: 0.399 - Mar 100 Coverall: 0.6383 - Map Face Shield: 0.0257 - Mar 100 Face Shield: 0.1557 - Map Gloves: 0.0535 - Mar 100 Gloves: 0.2772 - Map Goggles: 0.0002 - Mar 100 Goggles: 0.0031 - Map Mask: 0.166 - Mar 100 Mask: 0.36 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Small \| Map Medium \| Map Large \| Mar 1 \| Mar 10 \| Mar 100 \| Mar Small \| Mar Medium \| Mar Large \| Map Coverall \| Mar 100 Coverall \| Map Face Shield \| Mar 100 Face Shield \| Map Gloves \| Mar 100 Gloves \| Map Goggles \| Mar 100 Goggles \| Map Mask \| Mar 100 Mask \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:---------:\|:----------:\|:---------:\|:------------:\|:----------------:\|:---------------:\|:-------------------:\|:----------:\|:--------------:\|:-----------:\|:---------------:\|:--------:\|:------------:\| \| 2.6856 \| 1.0 \| 107 \| 2.4604 \| 0.0152 \| 0.0396 \| 0.0096 \| 0.0051 \| 0.0048 \| 0.0234 \| 0.0535 \| 0.1109 \| 0.1286 \| 0.0401 \| 0.089 \| 0.1752 \| 0.0622 \| 0.3509 \| 0.0 \| 0.0 \| 0.0045 \| 0.1174 \| 0.0 \| 0.0 \| 0.0092 \| 0.1747 \| \| 2.1242 \| 2.0 \| 214 \| 2.1711 \| 0.0464 \| 0.1115 \| 0.033 \| 0.01 \| 0.0466 \| 0.0621 \| 0.0776 \| 0.1595 \| 0.1801 \| 0.0647 \| 0.1406 \| 0.2105 \| 0.1861 \| 0.5176 \| 0.0 \| 0.0 \| 0.0139 \| 0.1437 \| 0.0 \| 0.0 \| 0.0317 \| 0.2391 \| \| 1.9759 \| 3.0 \| 321 \| 2.0671 \| 0.0662 \| 0.1477 \| 0.053 \| 0.0129 \| 0.0709 \| 0.0846 \| 0.0964 \| 0.1814 \| 0.199 \| 0.057 \| 0.1639 \| 0.2279 \| 0.2207 \| 0.5608 \| 0.0 \| 0.0 \| 0.0222 \| 0.1688 \| 0.0 \| 0.0 \| 0.0881 \| 0.2653 \| \| 1.8435 \| 4.0 \| 428 \| 1.9923 \| 0.084 \| 0.1759 \| 0.0717 \| 0.0156 \| 0.0673 \| 0.1068 \| 0.1017 \| 0.1929 \| 0.2119 \| 0.0717 \| 0.1646 \| 0.2621 \| 0.2915 \| 0.5856 \| 0.0 \| 0.0 \| 0.0302 \| 0.1835 \| 0.0 \| 0.0 \| 0.0982 \| 0.2907 \| \| 1.7693 \| 5.0 \| 535 \| 1.9163 \| 0.0851 \| 0.181 \| 0.0718 \| 0.0201 \| 0.0721 \| 0.1072 \| 0.1005 \| 0.1956 \| 0.2137 \| 0.0924 \| 0.1684 \| 0.2556 \| 0.2895 \| 0.5559 \| 0.004 \| 0.0063 \| 0.0256 \| 0.1808 \| 0.0 \| 0.0 \| 0.1064 \| 0.3253 \| \| 1.6961 \| 6.0 \| 642 \| 1.8520 \| 0.1045 \| 0.2193 \| 0.0909 \| 0.0529 \| 0.0938 \| 0.133 \| 0.1183 \| 0.2171 \| 0.2416 \| 0.1111 \| 0.2093 \| 0.2975 \| 0.3181 \| 0.5748 \| 0.0062 \| 0.0329 \| 0.0432 \| 0.2598 \| 0.0 \| 0.0 \| 0.1549 \| 0.3404 \| \| 1.6116 \| 7.0 \| 749 \| 1.7836 \| 0.1118 \| 0.2368 \| 0.089 \| 0.0334 \| 0.0935 \| 0.1543 \| 0.1308 \| 0.2439 \| 0.2684 \| 0.1294 \| 0.2151 \| 0.3409 \| 0.3489 \| 0.6059 \| 0.0081 \| 0.1165 \| 0.0517 \| 0.2888 \| 0.0 \| 0.0 \| 0.1503 \| 0.3307 \| \| 1.5518 \| 8.0 \| 856 \| 1.7223 \| 0.1235 \| 0.2558 \| 0.1096 \| 0.0336 \| 0.1039 \| 0.1553 \| 0.135 \| 0.2555 \| 0.2765 \| 0.1211 \| 0.2311 \| 0.3411 \| 0.3847 \| 0.6203 \| 0.0237 \| 0.1494 \| 0.0556 \| 0.2661 \| 0.0001 \| 0.0015 \| 0.1537 \| 0.3453 \| \| 1.5112 \| 9.0 \| 963 \| 1.6986 \| 0.1268 \| 0.2639 \| 0.1029 \| 0.0318 \| 0.1025 \| 0.1676 \| 0.1444 \| 0.2626 \| 0.2864 \| 0.1204 \| 0.2328 \| 0.3611 \| 0.3928 \| 0.6392 \| 0.0274 \| 0.157 \| 0.0536 \| 0.2741 \| 0.0012 \| 0.0062 \| 0.1591 \| 0.3556 \| \| 1.4924 \| 10.0 \| 1070 \| 1.6932 \| 0.1289 \| 0.2606 \| 0.1098 \| 0.0355 \| 0.1082 \| 0.1676 \| 0.1429 \| 0.2628 \| 0.2869 \| 0.1256 \| 0.2299 \| 0.3635 \| 0.399 \| 0.6383 \| 0.0257 \| 0.1557 \| 0.0535 \| 0.2772 \| 0.0002 \| 0.0031 \| 0.166 \| 0.36 \| ### Framework versions - Transformers 4.42.4 - Pytorch 1.13.1+cu117 - Datasets 2.20.0 - Tokenizers 0.19.1	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
trungphien/table_transfomer-finetuned-v1	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "table", "table rotated" ]
trungphien/phien-table-detection	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "table", "table rotated" ]
Leotrim/detr_finetuned_cppe5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/raspuntinov_ai/huggingface/runs/f0sny8se) [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/raspuntinov_ai/huggingface/runs/f0sny8se) # detr_finetuned_cppe5 This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2223 - Map: 0.3117 - Map 50: 0.6293 - Map 75: 0.2667 - Map Small: 0.106 - Map Medium: 0.2851 - Map Large: 0.4545 - Mar 1: 0.3195 - Mar 10: 0.4877 - Mar 100: 0.5017 - Mar Small: 0.3298 - Mar Medium: 0.4402 - Mar Large: 0.6375 - Map Coverall: 0.5742 - Mar 100 Coverall: 0.7165 - Map Face Shield: 0.2986 - Mar 100 Face Shield: 0.5309 - Map Gloves: 0.2081 - Mar 100 Gloves: 0.3644 - Map Goggles: 0.1561 - Mar 100 Goggles: 0.4509 - Map Mask: 0.3214 - Mar 100 Mask: 0.4458 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Small \| Map Medium \| Map Large \| Mar 1 \| Mar 10 \| Mar 100 \| Mar Small \| Mar Medium \| Mar Large \| Map Coverall \| Mar 100 Coverall \| Map Face Shield \| Mar 100 Face Shield \| Map Gloves \| Mar 100 Gloves \| Map Goggles \| Mar 100 Goggles \| Map Mask \| Mar 100 Mask \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:---------:\|:----------:\|:---------:\|:------------:\|:----------------:\|:---------------:\|:-------------------:\|:----------:\|:--------------:\|:-----------:\|:---------------:\|:--------:\|:------------:\| \| No log \| 1.0 \| 107 \| 2.2632 \| 0.0062 \| 0.0235 \| 0.0011 \| 0.0073 \| 0.0103 \| 0.0113 \| 0.0201 \| 0.0898 \| 0.1405 \| 0.1997 \| 0.1623 \| 0.174 \| 0.0017 \| 0.0824 \| 0.0034 \| 0.0471 \| 0.0033 \| 0.1955 \| 0.0037 \| 0.0727 \| 0.019 \| 0.3047 \| \| No log \| 2.0 \| 214 \| 2.0234 \| 0.0428 \| 0.1012 \| 0.0317 \| 0.021 \| 0.0786 \| 0.0501 \| 0.1375 \| 0.2463 \| 0.2875 \| 0.2315 \| 0.3098 \| 0.3033 \| 0.1382 \| 0.5733 \| 0.0104 \| 0.1529 \| 0.0071 \| 0.2379 \| 0.0034 \| 0.1182 \| 0.0549 \| 0.3553 \| \| No log \| 3.0 \| 321 \| 1.8733 \| 0.0639 \| 0.1631 \| 0.0411 \| 0.0314 \| 0.1147 \| 0.0873 \| 0.1589 \| 0.3112 \| 0.3549 \| 0.2757 \| 0.3445 \| 0.4228 \| 0.168 \| 0.6642 \| 0.0241 \| 0.2309 \| 0.0237 \| 0.2819 \| 0.0083 \| 0.2091 \| 0.0956 \| 0.3884 \| \| No log \| 4.0 \| 428 \| 1.7409 \| 0.115 \| 0.2879 \| 0.0752 \| 0.07 \| 0.1591 \| 0.1481 \| 0.1731 \| 0.3487 \| 0.3892 \| 0.3703 \| 0.3574 \| 0.4902 \| 0.276 \| 0.617 \| 0.0547 \| 0.3471 \| 0.0586 \| 0.3339 \| 0.0157 \| 0.2436 \| 0.17 \| 0.4042 \| \| 2.0328 \| 5.0 \| 535 \| 1.6217 \| 0.1734 \| 0.3876 \| 0.1395 \| 0.1021 \| 0.2176 \| 0.239 \| 0.2162 \| 0.4068 \| 0.4342 \| 0.3228 \| 0.3911 \| 0.5637 \| 0.4173 \| 0.6705 \| 0.1075 \| 0.4059 \| 0.0927 \| 0.3655 \| 0.0222 \| 0.3182 \| 0.2271 \| 0.4111 \| \| 2.0328 \| 6.0 \| 642 \| 1.5774 \| 0.1806 \| 0.4081 \| 0.1359 \| 0.0693 \| 0.1831 \| 0.2549 \| 0.2169 \| 0.3982 \| 0.4191 \| 0.2609 \| 0.3582 \| 0.5482 \| 0.4468 \| 0.6687 \| 0.1109 \| 0.3926 \| 0.0985 \| 0.3316 \| 0.0344 \| 0.3382 \| 0.2123 \| 0.3642 \| \| 2.0328 \| 7.0 \| 749 \| 1.4768 \| 0.1974 \| 0.4263 \| 0.162 \| 0.0814 \| 0.1819 \| 0.3263 \| 0.2326 \| 0.4013 \| 0.4296 \| 0.25 \| 0.3439 \| 0.5935 \| 0.4733 \| 0.6682 \| 0.0929 \| 0.3765 \| 0.1278 \| 0.3458 \| 0.0289 \| 0.3473 \| 0.2641 \| 0.4105 \| \| 2.0328 \| 8.0 \| 856 \| 1.4344 \| 0.2161 \| 0.4708 \| 0.181 \| 0.0699 \| 0.2098 \| 0.333 \| 0.2364 \| 0.4117 \| 0.4391 \| 0.3247 \| 0.391 \| 0.5775 \| 0.5021 \| 0.6795 \| 0.1504 \| 0.4412 \| 0.1385 \| 0.3209 \| 0.0341 \| 0.3745 \| 0.2553 \| 0.3795 \| \| 2.0328 \| 9.0 \| 963 \| 1.4459 \| 0.2189 \| 0.4586 \| 0.1836 \| 0.1123 \| 0.2038 \| 0.3322 \| 0.2571 \| 0.4138 \| 0.4358 \| 0.3249 \| 0.3825 \| 0.5595 \| 0.4938 \| 0.6756 \| 0.1193 \| 0.4191 \| 0.1437 \| 0.3401 \| 0.0531 \| 0.3509 \| 0.2845 \| 0.3932 \| \| 1.4446 \| 10.0 \| 1070 \| 1.3804 \| 0.2384 \| 0.5297 \| 0.1935 \| 0.0951 \| 0.2296 \| 0.3716 \| 0.2606 \| 0.44 \| 0.4664 \| 0.2763 \| 0.4271 \| 0.5924 \| 0.5123 \| 0.6835 \| 0.164 \| 0.4794 \| 0.193 \| 0.3881 \| 0.0584 \| 0.4018 \| 0.2642 \| 0.3789 \| \| 1.4446 \| 11.0 \| 1177 \| 1.3651 \| 0.2451 \| 0.532 \| 0.191 \| 0.144 \| 0.2261 \| 0.3733 \| 0.2756 \| 0.4496 \| 0.4642 \| 0.2983 \| 0.3913 \| 0.6093 \| 0.5158 \| 0.7091 \| 0.1875 \| 0.4956 \| 0.1824 \| 0.3616 \| 0.0623 \| 0.3655 \| 0.2777 \| 0.3895 \| \| 1.4446 \| 12.0 \| 1284 \| 1.3426 \| 0.2526 \| 0.5358 \| 0.208 \| 0.1033 \| 0.2291 \| 0.38 \| 0.285 \| 0.4553 \| 0.4771 \| 0.281 \| 0.4179 \| 0.6092 \| 0.5401 \| 0.692 \| 0.2285 \| 0.5235 \| 0.1492 \| 0.3463 \| 0.0697 \| 0.4164 \| 0.2753 \| 0.4074 \| \| 1.4446 \| 13.0 \| 1391 \| 1.3738 \| 0.2444 \| 0.5204 \| 0.2107 \| 0.09 \| 0.224 \| 0.3802 \| 0.2751 \| 0.4449 \| 0.4625 \| 0.3096 \| 0.398 \| 0.5982 \| 0.52 \| 0.6835 \| 0.1954 \| 0.5132 \| 0.1721 \| 0.3362 \| 0.0596 \| 0.3655 \| 0.2749 \| 0.4142 \| \| 1.4446 \| 14.0 \| 1498 \| 1.3362 \| 0.2562 \| 0.5391 \| 0.2243 \| 0.0838 \| 0.2223 \| 0.4115 \| 0.2789 \| 0.4514 \| 0.4694 \| 0.2657 \| 0.4098 \| 0.6113 \| 0.5536 \| 0.6994 \| 0.1741 \| 0.5 \| 0.1999 \| 0.3593 \| 0.089 \| 0.3982 \| 0.2646 \| 0.39 \| \| 1.2339 \| 15.0 \| 1605 \| 1.2863 \| 0.274 \| 0.5738 \| 0.235 \| 0.1041 \| 0.2467 \| 0.4314 \| 0.2953 \| 0.4712 \| 0.4907 \| 0.3404 \| 0.4268 \| 0.6344 \| 0.5423 \| 0.7063 \| 0.2487 \| 0.5147 \| 0.1963 \| 0.3825 \| 0.1043 \| 0.4382 \| 0.2783 \| 0.4121 \| \| 1.2339 \| 16.0 \| 1712 \| 1.2890 \| 0.2834 \| 0.5828 \| 0.246 \| 0.1031 \| 0.2535 \| 0.4322 \| 0.2994 \| 0.4643 \| 0.4831 \| 0.3513 \| 0.4217 \| 0.6215 \| 0.5515 \| 0.6909 \| 0.2501 \| 0.5103 \| 0.2154 \| 0.3689 \| 0.1029 \| 0.4418 \| 0.2969 \| 0.4037 \| \| 1.2339 \| 17.0 \| 1819 \| 1.3175 \| 0.2706 \| 0.5655 \| 0.2381 \| 0.096 \| 0.2336 \| 0.4086 \| 0.2952 \| 0.4623 \| 0.4779 \| 0.3177 \| 0.4192 \| 0.6109 \| 0.5271 \| 0.6903 \| 0.2482 \| 0.5044 \| 0.1664 \| 0.352 \| 0.1075 \| 0.4309 \| 0.3035 \| 0.4121 \| \| 1.2339 \| 18.0 \| 1926 \| 1.2626 \| 0.2851 \| 0.5718 \| 0.2366 \| 0.0902 \| 0.2654 \| 0.4276 \| 0.3093 \| 0.4791 \| 0.4957 \| 0.2848 \| 0.443 \| 0.6326 \| 0.5663 \| 0.7091 \| 0.2394 \| 0.5279 \| 0.204 \| 0.3701 \| 0.1101 \| 0.4509 \| 0.3058 \| 0.4205 \| \| 1.0914 \| 19.0 \| 2033 \| 1.2619 \| 0.2947 \| 0.6021 \| 0.2419 \| 0.1009 \| 0.2725 \| 0.4294 \| 0.3038 \| 0.4833 \| 0.4971 \| 0.3184 \| 0.4445 \| 0.6232 \| 0.5687 \| 0.7017 \| 0.2691 \| 0.5368 \| 0.2063 \| 0.365 \| 0.1127 \| 0.4455 \| 0.3169 \| 0.4368 \| \| 1.0914 \| 20.0 \| 2140 \| 1.2522 \| 0.3037 \| 0.6086 \| 0.2784 \| 0.1125 \| 0.2678 \| 0.4599 \| 0.3166 \| 0.4787 \| 0.4927 \| 0.3366 \| 0.4307 \| 0.6243 \| 0.5613 \| 0.692 \| 0.2928 \| 0.5279 \| 0.2075 \| 0.3695 \| 0.1509 \| 0.4382 \| 0.306 \| 0.4358 \| \| 1.0914 \| 21.0 \| 2247 \| 1.2523 \| 0.3006 \| 0.6162 \| 0.2592 \| 0.1084 \| 0.263 \| 0.4545 \| 0.3158 \| 0.4778 \| 0.4933 \| 0.3353 \| 0.4233 \| 0.6332 \| 0.5636 \| 0.6989 \| 0.2845 \| 0.5162 \| 0.1984 \| 0.3599 \| 0.1427 \| 0.4582 \| 0.3139 \| 0.4332 \| \| 1.0914 \| 22.0 \| 2354 \| 1.2415 \| 0.3077 \| 0.624 \| 0.2611 \| 0.1393 \| 0.2779 \| 0.4448 \| 0.3182 \| 0.4826 \| 0.4931 \| 0.3503 \| 0.426 \| 0.6303 \| 0.5733 \| 0.7063 \| 0.2865 \| 0.5324 \| 0.2076 \| 0.3559 \| 0.151 \| 0.4418 \| 0.32 \| 0.4289 \| \| 1.0914 \| 23.0 \| 2461 \| 1.2369 \| 0.306 \| 0.6127 \| 0.28 \| 0.1183 \| 0.2778 \| 0.4528 \| 0.3185 \| 0.4812 \| 0.4912 \| 0.3626 \| 0.4312 \| 0.6278 \| 0.5671 \| 0.7017 \| 0.2834 \| 0.5221 \| 0.2051 \| 0.3593 \| 0.1536 \| 0.4309 \| 0.3208 \| 0.4421 \| \| 1.0025 \| 24.0 \| 2568 \| 1.2379 \| 0.3076 \| 0.6168 \| 0.2685 \| 0.1043 \| 0.2796 \| 0.4559 \| 0.3191 \| 0.4815 \| 0.4946 \| 0.3321 \| 0.4344 \| 0.6313 \| 0.5695 \| 0.7091 \| 0.2823 \| 0.5221 \| 0.2061 \| 0.3644 \| 0.1542 \| 0.4327 \| 0.326 \| 0.4447 \| \| 1.0025 \| 25.0 \| 2675 \| 1.2307 \| 0.3139 \| 0.6266 \| 0.2715 \| 0.1157 \| 0.2888 \| 0.4601 \| 0.3206 \| 0.4855 \| 0.5023 \| 0.3389 \| 0.4441 \| 0.6388 \| 0.5695 \| 0.7091 \| 0.2934 \| 0.525 \| 0.2123 \| 0.3678 \| 0.1651 \| 0.4545 \| 0.3293 \| 0.4553 \| \| 1.0025 \| 26.0 \| 2782 \| 1.2233 \| 0.3133 \| 0.6269 \| 0.271 \| 0.109 \| 0.2844 \| 0.4571 \| 0.3171 \| 0.4862 \| 0.5019 \| 0.3415 \| 0.4391 \| 0.6377 \| 0.5774 \| 0.7142 \| 0.2981 \| 0.5279 \| 0.2093 \| 0.3633 \| 0.1619 \| 0.46 \| 0.32 \| 0.4442 \| \| 1.0025 \| 27.0 \| 2889 \| 1.2248 \| 0.313 \| 0.6267 \| 0.2686 \| 0.1104 \| 0.2867 \| 0.4571 \| 0.3185 \| 0.4878 \| 0.5026 \| 0.361 \| 0.44 \| 0.637 \| 0.5724 \| 0.717 \| 0.301 \| 0.5338 \| 0.2043 \| 0.365 \| 0.1653 \| 0.4509 \| 0.322 \| 0.4463 \| \| 1.0025 \| 28.0 \| 2996 \| 1.2249 \| 0.311 \| 0.6268 \| 0.2671 \| 0.1071 \| 0.2842 \| 0.4533 \| 0.3186 \| 0.4887 \| 0.5021 \| 0.3363 \| 0.4395 \| 0.6376 \| 0.5732 \| 0.7176 \| 0.2967 \| 0.5265 \| 0.201 \| 0.3588 \| 0.1609 \| 0.4564 \| 0.3234 \| 0.4511 \| \| 0.9487 \| 29.0 \| 3103 \| 1.2225 \| 0.3125 \| 0.6304 \| 0.2661 \| 0.1045 \| 0.2847 \| 0.4557 \| 0.3199 \| 0.4891 \| 0.503 \| 0.3324 \| 0.4407 \| 0.639 \| 0.5755 \| 0.7182 \| 0.3001 \| 0.5338 \| 0.2089 \| 0.365 \| 0.1574 \| 0.4527 \| 0.3208 \| 0.4453 \| \| 0.9487 \| 30.0 \| 3210 \| 1.2223 \| 0.3117 \| 0.6293 \| 0.2667 \| 0.106 \| 0.2851 \| 0.4545 \| 0.3195 \| 0.4877 \| 0.5017 \| 0.3298 \| 0.4402 \| 0.6375 \| 0.5742 \| 0.7165 \| 0.2986 \| 0.5309 \| 0.2081 \| 0.3644 \| 0.1561 \| 0.4509 \| 0.3214 \| 0.4458 \| ### Framework versions - Transformers 4.42.3 - Pytorch 2.1.2 - Datasets 2.20.0 - Tokenizers 0.19.1	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
yi-zhao-K/detr-resnet-50_cropped_37	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet-50_cropped_37 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 300 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.42.3 - Pytorch 2.3.1+cpu - Datasets 2.20.0 - Tokenizers 0.19.1	[ "blackgum", "chestnut oak", "red maple", "black cherry", "norway spruce", "northern red oak" ]
daksh5656/detr-resnet-50_finetuned_cppe5	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet-50_finetuned_cppe5 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.43.3 - Pytorch 2.4.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1	[ "diseases", "ballooning", "fibrosis", "inflammation", "steatosis" ]
ipd805/digit	open k	[ "digit", "icon" ]
ArrayDice/Vehicle_Detection_Model_Zoom	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Vehicle_Detection_Model_Zoom This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7673 - Map: 0.0853 - Map 50: 0.1613 - Map 75: 0.0814 - Map Small: 0.452 - Map Medium: 0.1098 - Map Large: 0.0 - Mar 1: 0.1187 - Mar 10: 0.1824 - Mar 100: 0.1934 - Mar Small: 0.5946 - Mar Medium: 0.1984 - Mar Large: 0.0 - Map Camping car: 0.0353 - Mar 100 Camping car: 0.35 - Map Car: 0.4678 - Mar 100 Car: 0.6103 - Map Other: 0.0 - Mar 100 Other: 0.0 - Map Pickup: 0.0088 - Mar 100 Pickup: 0.2 - Map Truck: 0.0 - Mar 100 Truck: 0.0 - Map Van: 0.0 - Mar 100 Van: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Small \| Map Medium \| Map Large \| Mar 1 \| Mar 10 \| Mar 100 \| Mar Small \| Mar Medium \| Mar Large \| Map Camping car \| Mar 100 Camping car \| Map Car \| Mar 100 Car \| Map Other \| Mar 100 Other \| Map Pickup \| Mar 100 Pickup \| Map Truck \| Mar 100 Truck \| Map Van \| Mar 100 Van \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:---------:\|:----------:\|:---------:\|:---------------:\|:-------------------:\|:-------:\|:-----------:\|:---------:\|:-------------:\|:----------:\|:--------------:\|:---------:\|:-------------:\|:-------:\|:-----------:\| \| No log \| 1.0 \| 232 \| 1.2888 \| 0.0179 \| 0.0435 \| 0.0095 \| 0.118 \| 0.0187 \| 0.0 \| 0.0175 \| 0.0527 \| 0.0838 \| 0.4665 \| 0.0955 \| 0.0 \| 0.0 \| 0.0 \| 0.1073 \| 0.5028 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| No log \| 2.0 \| 464 \| 1.1028 \| 0.0308 \| 0.0792 \| 0.0172 \| 0.2225 \| 0.0267 \| 0.0 \| 0.0233 \| 0.0637 \| 0.0818 \| 0.4346 \| 0.0998 \| 0.0 \| 0.0 \| 0.0 \| 0.1851 \| 0.4907 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.5836 \| 3.0 \| 696 \| 1.0727 \| 0.0509 \| 0.1216 \| 0.0268 \| 0.3118 \| 0.0531 \| 0.0 \| 0.0278 \| 0.0702 \| 0.0781 \| 0.4362 \| 0.0884 \| 0.0 \| 0.0 \| 0.0 \| 0.3055 \| 0.4683 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.5836 \| 4.0 \| 928 \| 1.1038 \| 0.0467 \| 0.1199 \| 0.0193 \| 0.2617 \| 0.0543 \| 0.0 \| 0.0249 \| 0.0679 \| 0.0772 \| 0.4357 \| 0.0861 \| 0.0 \| 0.0 \| 0.0 \| 0.2803 \| 0.4633 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.1611 \| 5.0 \| 1160 \| 1.0186 \| 0.057 \| 0.13 \| 0.0335 \| 0.3254 \| 0.0643 \| 0.0 \| 0.0281 \| 0.0721 \| 0.0819 \| 0.4735 \| 0.0877 \| 0.0 \| 0.0 \| 0.0 \| 0.3422 \| 0.4915 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.1611 \| 6.0 \| 1392 \| 1.0468 \| 0.0541 \| 0.1255 \| 0.029 \| 0.3094 \| 0.0612 \| 0.0 \| 0.0288 \| 0.0735 \| 0.0816 \| 0.4557 \| 0.0925 \| 0.0 \| 0.0 \| 0.0 \| 0.3249 \| 0.4897 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.1009 \| 7.0 \| 1624 \| 0.9479 \| 0.063 \| 0.1366 \| 0.0467 \| 0.3682 \| 0.0676 \| 0.0 \| 0.0312 \| 0.0789 \| 0.0905 \| 0.507 \| 0.1019 \| 0.0 \| 0.0 \| 0.0 \| 0.3781 \| 0.5427 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.1009 \| 8.0 \| 1856 \| 0.9808 \| 0.0556 \| 0.1305 \| 0.0355 \| 0.3103 \| 0.0652 \| 0.0 \| 0.0295 \| 0.074 \| 0.0858 \| 0.4789 \| 0.0974 \| 0.0 \| 0.0 \| 0.0 \| 0.3338 \| 0.5149 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.043 \| 9.0 \| 2088 \| 1.0168 \| 0.0504 \| 0.1232 \| 0.0248 \| 0.2652 \| 0.0654 \| 0.0 \| 0.0271 \| 0.0716 \| 0.0815 \| 0.4443 \| 0.0958 \| 0.0 \| 0.0 \| 0.0 \| 0.3022 \| 0.489 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.043 \| 10.0 \| 2320 \| 0.9497 \| 0.0536 \| 0.1207 \| 0.0312 \| 0.2924 \| 0.0715 \| 0.0 \| 0.0299 \| 0.0755 \| 0.0888 \| 0.4968 \| 0.1003 \| 0.0 \| 0.0 \| 0.0 \| 0.3218 \| 0.5327 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.026 \| 11.0 \| 2552 \| 0.9826 \| 0.0844 \| 0.1826 \| 0.0374 \| 0.3582 \| 0.0936 \| 0.0 \| 0.0727 \| 0.1434 \| 0.1534 \| 0.4795 \| 0.1667 \| 0.0 \| 0.127 \| 0.4 \| 0.3792 \| 0.5206 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 1.026 \| 12.0 \| 2784 \| 0.9282 \| 0.0615 \| 0.1396 \| 0.0422 \| 0.3292 \| 0.0758 \| 0.0 \| 0.0809 \| 0.1281 \| 0.1357 \| 0.4784 \| 0.1472 \| 0.0 \| 0.0202 \| 0.3 \| 0.3487 \| 0.5142 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.9794 \| 13.0 \| 3016 \| 0.8976 \| 0.0663 \| 0.1368 \| 0.0541 \| 0.3854 \| 0.0748 \| 0.0 \| 0.0317 \| 0.0821 \| 0.0942 \| 0.5389 \| 0.1026 \| 0.0 \| 0.0 \| 0.0 \| 0.3977 \| 0.5651 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.9794 \| 14.0 \| 3248 \| 0.8970 \| 0.0633 \| 0.1358 \| 0.0474 \| 0.3519 \| 0.0754 \| 0.0 \| 0.0315 \| 0.0799 \| 0.0929 \| 0.5151 \| 0.1066 \| 0.0 \| 0.0 \| 0.0 \| 0.3798 \| 0.5577 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.9794 \| 15.0 \| 3480 \| 0.8418 \| 0.0677 \| 0.1368 \| 0.052 \| 0.3737 \| 0.0807 \| 0.0 \| 0.0328 \| 0.0837 \| 0.0935 \| 0.5265 \| 0.1045 \| 0.0 \| 0.0 \| 0.0 \| 0.4063 \| 0.5609 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.9189 \| 16.0 \| 3712 \| 0.8709 \| 0.0677 \| 0.1414 \| 0.055 \| 0.3733 \| 0.0808 \| 0.0 \| 0.066 \| 0.1151 \| 0.1261 \| 0.5308 \| 0.1344 \| 0.0 \| 0.0033 \| 0.2 \| 0.4031 \| 0.5566 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.9189 \| 17.0 \| 3944 \| 0.9095 \| 0.0657 \| 0.1386 \| 0.048 \| 0.3734 \| 0.0746 \| 0.0 \| 0.0444 \| 0.0925 \| 0.1004 \| 0.4935 \| 0.1139 \| 0.0 \| 0.0 \| 0.0 \| 0.3918 \| 0.5356 \| 0.0 \| 0.0 \| 0.0024 \| 0.0667 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.8933 \| 18.0 \| 4176 \| 0.8832 \| 0.0651 \| 0.1416 \| 0.046 \| 0.3467 \| 0.0809 \| 0.0 \| 0.1021 \| 0.1488 \| 0.1594 \| 0.5076 \| 0.1698 \| 0.0 \| 0.0151 \| 0.3 \| 0.3708 \| 0.5399 \| 0.0 \| 0.0 \| 0.0045 \| 0.1167 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.8933 \| 19.0 \| 4408 \| 0.8459 \| 0.0707 \| 0.1444 \| 0.0527 \| 0.4093 \| 0.0782 \| 0.0 \| 0.043 \| 0.0914 \| 0.1017 \| 0.5405 \| 0.1082 \| 0.0 \| 0.0 \| 0.0 \| 0.4231 \| 0.5605 \| 0.0 \| 0.0 \| 0.0013 \| 0.05 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.8732 \| 20.0 \| 4640 \| 0.8352 \| 0.0719 \| 0.151 \| 0.0581 \| 0.3987 \| 0.0853 \| 0.0 \| 0.0699 \| 0.1257 \| 0.1366 \| 0.5459 \| 0.1443 \| 0.0 \| 0.001 \| 0.05 \| 0.4198 \| 0.5698 \| 0.0 \| 0.0 \| 0.0106 \| 0.2 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.8732 \| 21.0 \| 4872 \| 0.8110 \| 0.077 \| 0.1574 \| 0.0657 \| 0.407 \| 0.0938 \| 0.0 \| 0.1345 \| 0.1896 \| 0.2005 \| 0.5638 \| 0.2078 \| 0.0 \| 0.0104 \| 0.3 \| 0.4317 \| 0.5865 \| 0.0 \| 0.0 \| 0.0201 \| 0.3167 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.8261 \| 22.0 \| 5104 \| 0.8235 \| 0.074 \| 0.1564 \| 0.0561 \| 0.4045 \| 0.0867 \| 0.0 \| 0.0842 \| 0.1682 \| 0.1776 \| 0.5422 \| 0.1851 \| 0.0 \| 0.0066 \| 0.15 \| 0.4229 \| 0.5655 \| 0.0 \| 0.0 \| 0.0144 \| 0.35 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.8261 \| 23.0 \| 5336 \| 0.8116 \| 0.0817 \| 0.1636 \| 0.0731 \| 0.4237 \| 0.0965 \| 0.0 \| 0.1115 \| 0.1724 \| 0.1822 \| 0.5557 \| 0.1889 \| 0.0 \| 0.0433 \| 0.3 \| 0.4392 \| 0.5765 \| 0.0 \| 0.0 \| 0.0074 \| 0.2167 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.8213 \| 24.0 \| 5568 \| 0.7748 \| 0.0841 \| 0.1619 \| 0.0746 \| 0.4393 \| 0.1182 \| 0.0 \| 0.1244 \| 0.1874 \| 0.197 \| 0.58 \| 0.2031 \| 0.0 \| 0.0393 \| 0.35 \| 0.457 \| 0.5989 \| 0.0 \| 0.0 \| 0.0085 \| 0.2333 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.8213 \| 25.0 \| 5800 \| 0.7782 \| 0.0893 \| 0.1678 \| 0.0802 \| 0.4418 \| 0.1555 \| 0.0 \| 0.1376 \| 0.2022 \| 0.2134 \| 0.5795 \| 0.2191 \| 0.0 \| 0.0683 \| 0.45 \| 0.4583 \| 0.5972 \| 0.0 \| 0.0 \| 0.009 \| 0.2333 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.794 \| 26.0 \| 6032 \| 0.7822 \| 0.082 \| 0.1585 \| 0.0738 \| 0.4368 \| 0.1026 \| 0.0 \| 0.118 \| 0.191 \| 0.2021 \| 0.5773 \| 0.208 \| 0.0 \| 0.0324 \| 0.4 \| 0.4519 \| 0.5957 \| 0.0 \| 0.0 \| 0.0074 \| 0.2167 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.794 \| 27.0 \| 6264 \| 0.7735 \| 0.0888 \| 0.1683 \| 0.0879 \| 0.4533 \| 0.1188 \| 0.0 \| 0.1404 \| 0.1981 \| 0.2087 \| 0.5865 \| 0.2137 \| 0.0 \| 0.0505 \| 0.35 \| 0.4672 \| 0.6021 \| 0.0 \| 0.0 \| 0.0153 \| 0.3 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.794 \| 28.0 \| 6496 \| 0.7680 \| 0.0852 \| 0.1608 \| 0.0842 \| 0.4557 \| 0.109 \| 0.0 \| 0.1186 \| 0.1821 \| 0.1924 \| 0.5892 \| 0.1974 \| 0.0 \| 0.0321 \| 0.35 \| 0.4705 \| 0.6046 \| 0.0 \| 0.0 \| 0.0088 \| 0.2 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.7843 \| 29.0 \| 6728 \| 0.7678 \| 0.0852 \| 0.1614 \| 0.0817 \| 0.4516 \| 0.1095 \| 0.0 \| 0.1185 \| 0.1821 \| 0.1931 \| 0.593 \| 0.1981 \| 0.0 \| 0.0353 \| 0.35 \| 0.4673 \| 0.6085 \| 0.0 \| 0.0 \| 0.0088 \| 0.2 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 0.7843 \| 30.0 \| 6960 \| 0.7673 \| 0.0853 \| 0.1613 \| 0.0814 \| 0.452 \| 0.1098 \| 0.0 \| 0.1187 \| 0.1824 \| 0.1934 \| 0.5946 \| 0.1984 \| 0.0 \| 0.0353 \| 0.35 \| 0.4678 \| 0.6103 \| 0.0 \| 0.0 \| 0.0088 \| 0.2 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| ### Framework versions - Transformers 4.42.4 - Pytorch 2.3.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1	[ "cars-trucks-vans", "boat", "camping car", "car", "motorcycle", "other", "pickup", "plane", "tractor", "truck", "van" ]
NabilaLM/DETR_25	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "label_0", "label_1", "label_2" ]
namnguyen059/detr-finetuned-cppe-5-10k-steps	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-finetuned-cppe-5-10k-steps This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the namnguyen059/PDFextract dataset. It achieves the following results on the evaluation set: - Loss: 3.3774 - Map: 0.0076 - Map 50: 0.0237 - Map 75: 0.0004 - Map Small: 0.0 - Map Medium: 0.0117 - Map Large: 0.0 - Mar 1: 0.0014 - Mar 10: 0.0459 - Mar 100: 0.0622 - Mar Small: 0.0 - Mar Medium: 0.0958 - Mar Large: 0.0 - Map Table: 0.0 - Mar 100 Table: 0.0 - Map Text: 0.0152 - Mar 100 Text: 0.1243 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Small \| Map Medium \| Map Large \| Mar 1 \| Mar 10 \| Mar 100 \| Mar Small \| Mar Medium \| Mar Large \| Map Table \| Mar 100 Table \| Map Text \| Mar 100 Text \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:---------:\|:----------:\|:---------:\|:---------:\|:-------------:\|:--------:\|:------------:\| \| 6.2935 \| 1.0 \| 5 \| 5.7125 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 5.6711 \| 2.0 \| 10 \| 5.4708 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 5.3937 \| 3.0 \| 15 \| 5.2153 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 5.001 \| 4.0 \| 20 \| 4.8025 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 4.8611 \| 5.0 \| 25 \| 4.6390 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| \| 4.6947 \| 6.0 \| 30 \| 4.2093 \| 0.0 \| 0.0002 \| 0.0 \| 0.0 \| 0.0 \| 0.0021 \| 0.0 \| 0.0 \| 0.0027 \| 0.0 \| 0.0 \| 0.1 \| 0.0 \| 0.0 \| 0.0001 \| 0.0054 \| \| 4.1881 \| 7.0 \| 35 \| 3.8453 \| 0.0003 \| 0.0031 \| 0.0 \| 0.0 \| 0.0006 \| 0.0012 \| 0.0 \| 0.0014 \| 0.0054 \| 0.0 \| 0.0021 \| 0.15 \| 0.0 \| 0.0 \| 0.0007 \| 0.0108 \| \| 3.9652 \| 8.0 \| 40 \| 4.0073 \| 0.003 \| 0.0182 \| 0.0 \| 0.0 \| 0.0052 \| 0.0017 \| 0.0014 \| 0.0081 \| 0.0122 \| 0.0 \| 0.0125 \| 0.15 \| 0.0 \| 0.0 \| 0.0059 \| 0.0243 \| \| 3.9875 \| 9.0 \| 45 \| 4.0535 \| 0.0001 \| 0.0005 \| 0.0001 \| 0.0 \| 0.0001 \| 0.0019 \| 0.0 \| 0.0 \| 0.0122 \| 0.0 \| 0.0063 \| 0.3 \| 0.0 \| 0.0 \| 0.0002 \| 0.0243 \| \| 4.041 \| 10.0 \| 50 \| 4.0097 \| 0.0002 \| 0.0017 \| 0.0 \| 0.0 \| 0.0004 \| 0.0009 \| 0.0 \| 0.0 \| 0.0081 \| 0.0 \| 0.0083 \| 0.1 \| 0.0 \| 0.0 \| 0.0003 \| 0.0162 \| \| 3.959 \| 11.0 \| 55 \| 3.8365 \| 0.0005 \| 0.0021 \| 0.0 \| 0.0 \| 0.0012 \| 0.0003 \| 0.0 \| 0.0068 \| 0.0122 \| 0.0 \| 0.0167 \| 0.05 \| 0.0 \| 0.0 \| 0.0009 \| 0.0243 \| \| 3.8681 \| 12.0 \| 60 \| 3.6800 \| 0.0007 \| 0.0041 \| 0.0 \| 0.0 \| 0.0014 \| 0.001 \| 0.0 \| 0.0068 \| 0.0149 \| 0.0 \| 0.0167 \| 0.15 \| 0.0 \| 0.0 \| 0.0013 \| 0.0297 \| \| 3.7309 \| 13.0 \| 65 \| 3.6922 \| 0.0029 \| 0.0249 \| 0.0 \| 0.0 \| 0.0068 \| 0.0008 \| 0.0014 \| 0.0081 \| 0.0189 \| 0.0 \| 0.025 \| 0.1 \| 0.0 \| 0.0 \| 0.0058 \| 0.0378 \| \| 3.647 \| 14.0 \| 70 \| 3.7221 \| 0.0024 \| 0.0158 \| 0.0 \| 0.0 \| 0.0045 \| 0.0016 \| 0.0014 \| 0.0108 \| 0.0162 \| 0.0 \| 0.0167 \| 0.2 \| 0.0 \| 0.0 \| 0.0048 \| 0.0324 \| \| 3.6564 \| 15.0 \| 75 \| 3.6637 \| 0.0007 \| 0.0048 \| 0.0 \| 0.0 \| 0.0012 \| 0.0025 \| 0.0014 \| 0.0054 \| 0.0189 \| 0.0 \| 0.0229 \| 0.15 \| 0.0 \| 0.0 \| 0.0014 \| 0.0378 \| \| 3.5905 \| 16.0 \| 80 \| 3.6443 \| 0.0078 \| 0.0179 \| 0.0 \| 0.0 \| 0.0116 \| 0.0074 \| 0.0135 \| 0.0203 \| 0.023 \| 0.0 \| 0.0312 \| 0.1 \| 0.0 \| 0.0 \| 0.0157 \| 0.0459 \| \| 3.8091 \| 17.0 \| 85 \| 3.8066 \| 0.0018 \| 0.0122 \| 0.0 \| 0.0 \| 0.003 \| 0.0 \| 0.0041 \| 0.0108 \| 0.0108 \| 0.0 \| 0.0167 \| 0.0 \| 0.0 \| 0.0 \| 0.0035 \| 0.0216 \| \| 3.8171 \| 18.0 \| 90 \| 3.6044 \| 0.0006 \| 0.0032 \| 0.0 \| 0.0 \| 0.0012 \| 0.0 \| 0.0 \| 0.0054 \| 0.0095 \| 0.0 \| 0.0146 \| 0.0 \| 0.0 \| 0.0 \| 0.0013 \| 0.0189 \| \| 3.7048 \| 19.0 \| 95 \| 3.6161 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0018 \| 0.0 \| 0.0 \| 0.0068 \| 0.0 \| 0.0 \| 0.25 \| 0.0 \| 0.0 \| 0.0 \| 0.0135 \| \| 3.8635 \| 20.0 \| 100 \| 3.6542 \| 0.0004 \| 0.0015 \| 0.0 \| 0.0 \| 0.0009 \| 0.0 \| 0.0 \| 0.0054 \| 0.0095 \| 0.0 \| 0.0146 \| 0.0 \| 0.0 \| 0.0 \| 0.0009 \| 0.0189 \| \| 3.7021 \| 21.0 \| 105 \| 4.0802 \| 0.0032 \| 0.016 \| 0.0 \| 0.0 \| 0.0053 \| 0.0 \| 0.0027 \| 0.0068 \| 0.0108 \| 0.0 \| 0.0167 \| 0.0 \| 0.0 \| 0.0 \| 0.0064 \| 0.0216 \| \| 3.7035 \| 22.0 \| 110 \| 4.0508 \| 0.0006 \| 0.0041 \| 0.0 \| 0.0 \| 0.001 \| 0.0 \| 0.0 \| 0.0014 \| 0.0162 \| 0.0 \| 0.025 \| 0.0 \| 0.0 \| 0.0 \| 0.0012 \| 0.0324 \| \| 3.6404 \| 23.0 \| 115 \| 4.1771 \| 0.0013 \| 0.0057 \| 0.0005 \| 0.0 \| 0.0022 \| 0.0 \| 0.0068 \| 0.0162 \| 0.0189 \| 0.0 \| 0.0292 \| 0.0 \| 0.0 \| 0.0 \| 0.0027 \| 0.0378 \| \| 3.7922 \| 24.0 \| 120 \| 4.4617 \| 0.006 \| 0.0149 \| 0.0 \| 0.0 \| 0.0099 \| 0.0 \| 0.0054 \| 0.0054 \| 0.0095 \| 0.0 \| 0.0146 \| 0.0 \| 0.0 \| 0.0 \| 0.0119 \| 0.0189 \| \| 3.7619 \| 25.0 \| 125 \| 3.8533 \| 0.001 \| 0.0026 \| 0.0 \| 0.0 \| 0.0053 \| 0.0008 \| 0.0 \| 0.0108 \| 0.0135 \| 0.0 \| 0.0167 \| 0.1 \| 0.0 \| 0.0 \| 0.0019 \| 0.027 \| \| 3.804 \| 26.0 \| 130 \| 3.7589 \| 0.001 \| 0.0073 \| 0.0 \| 0.0 \| 0.0036 \| 0.0 \| 0.0 \| 0.0081 \| 0.0122 \| 0.0 \| 0.0188 \| 0.0 \| 0.0 \| 0.0 \| 0.0021 \| 0.0243 \| \| 3.7785 \| 27.0 \| 135 \| 3.6378 \| 0.003 \| 0.0197 \| 0.0 \| 0.0 \| 0.01 \| 0.0003 \| 0.0027 \| 0.0162 \| 0.0189 \| 0.0 \| 0.0271 \| 0.05 \| 0.0 \| 0.0 \| 0.006 \| 0.0378 \| \| 3.726 \| 28.0 \| 140 \| 3.7696 \| 0.0003 \| 0.0021 \| 0.0001 \| 0.0 \| 0.0009 \| 0.0 \| 0.0 \| 0.0041 \| 0.0149 \| 0.0 \| 0.0229 \| 0.0 \| 0.0 \| 0.0 \| 0.0006 \| 0.0297 \| \| 3.682 \| 29.0 \| 145 \| 3.8782 \| 0.0003 \| 0.0006 \| 0.0003 \| 0.0 \| 0.001 \| 0.0 \| 0.0 \| 0.0081 \| 0.0108 \| 0.0 \| 0.0167 \| 0.0 \| 0.0 \| 0.0 \| 0.0006 \| 0.0216 \| \| 3.7331 \| 30.0 \| 150 \| 4.3841 \| 0.0005 \| 0.0027 \| 0.0 \| 0.0 \| 0.002 \| 0.0009 \| 0.0 \| 0.0081 \| 0.0189 \| 0.0 \| 0.025 \| 0.1 \| 0.0 \| 0.0 \| 0.001 \| 0.0378 \| \| 3.7627 \| 31.0 \| 155 \| 4.7688 \| 0.0007 \| 0.0054 \| 0.0 \| 0.0 \| 0.003 \| 0.0 \| 0.0 \| 0.0081 \| 0.0189 \| 0.0 \| 0.0292 \| 0.0 \| 0.0 \| 0.0 \| 0.0015 \| 0.0378 \| \| 3.8656 \| 32.0 \| 160 \| 5.0427 \| 0.0009 \| 0.0046 \| 0.0 \| 0.0 \| 0.0039 \| 0.0007 \| 0.0 \| 0.0027 \| 0.0324 \| 0.0 \| 0.0417 \| 0.2 \| 0.0 \| 0.0 \| 0.0018 \| 0.0649 \| \| 3.8845 \| 33.0 \| 165 \| 4.1952 \| 0.0012 \| 0.0056 \| 0.0 \| 0.0 \| 0.0027 \| 0.0 \| 0.0 \| 0.0108 \| 0.0243 \| 0.0 \| 0.0375 \| 0.0 \| 0.0 \| 0.0 \| 0.0024 \| 0.0486 \| \| 3.8416 \| 34.0 \| 170 \| 4.1403 \| 0.0048 \| 0.0128 \| 0.0006 \| 0.0 \| 0.0118 \| 0.0048 \| 0.0 \| 0.0311 \| 0.0703 \| 0.0 \| 0.1021 \| 0.15 \| 0.0 \| 0.0 \| 0.0096 \| 0.1405 \| \| 3.807 \| 35.0 \| 175 \| 4.6646 \| 0.0031 \| 0.0218 \| 0.0002 \| 0.0 \| 0.0103 \| 0.0125 \| 0.0014 \| 0.0176 \| 0.05 \| 0.0 \| 0.075 \| 0.05 \| 0.0 \| 0.0 \| 0.0062 \| 0.1 \| \| 3.8728 \| 36.0 \| 180 \| 4.5890 \| 0.0009 \| 0.0039 \| 0.0004 \| 0.0 \| 0.002 \| 0.0 \| 0.0 \| 0.0014 \| 0.0432 \| 0.0 \| 0.0667 \| 0.0 \| 0.0 \| 0.0 \| 0.0018 \| 0.0865 \| \| 3.7952 \| 37.0 \| 185 \| 4.0300 \| 0.0013 \| 0.0048 \| 0.0007 \| 0.0 \| 0.0023 \| 0.0 \| 0.0 \| 0.0176 \| 0.023 \| 0.0 \| 0.0354 \| 0.0 \| 0.0 \| 0.0 \| 0.0025 \| 0.0459 \| \| 3.725 \| 38.0 \| 190 \| 3.9829 \| 0.0003 \| 0.0025 \| 0.0001 \| 0.0 \| 0.0005 \| 0.0 \| 0.0 \| 0.0014 \| 0.0135 \| 0.0 \| 0.0208 \| 0.0 \| 0.0 \| 0.0 \| 0.0006 \| 0.027 \| \| 3.6884 \| 39.0 \| 195 \| 3.7402 \| 0.0008 \| 0.0036 \| 0.0 \| 0.0 \| 0.0009 \| 0.0119 \| 0.0 \| 0.0014 \| 0.027 \| 0.0 \| 0.0333 \| 0.2 \| 0.0 \| 0.0 \| 0.0016 \| 0.0541 \| \| 3.6963 \| 40.0 \| 200 \| 3.4723 \| 0.0002 \| 0.0015 \| 0.0 \| 0.0 \| 0.0004 \| 0.0 \| 0.0 \| 0.0 \| 0.0081 \| 0.0 \| 0.0125 \| 0.0 \| 0.0 \| 0.0 \| 0.0004 \| 0.0162 \| \| 3.7368 \| 41.0 \| 205 \| 3.6067 \| 0.0 \| 0.0004 \| 0.0 \| 0.0 \| 0.0001 \| 0.0 \| 0.0 \| 0.0 \| 0.0027 \| 0.0 \| 0.0042 \| 0.0 \| 0.0 \| 0.0 \| 0.0001 \| 0.0054 \| \| 3.7349 \| 42.0 \| 210 \| 3.6135 \| 0.0 \| 0.0002 \| 0.0 \| 0.0 \| 0.0 \| 0.0012 \| 0.0 \| 0.0014 \| 0.0014 \| 0.0 \| 0.0 \| 0.05 \| 0.0 \| 0.0 \| 0.0 \| 0.0027 \| \| 3.6353 \| 43.0 \| 215 \| 3.5835 \| 0.0018 \| 0.0062 \| 0.0014 \| 0.0 \| 0.0074 \| 0.0 \| 0.0 \| 0.0122 \| 0.027 \| 0.0 \| 0.0417 \| 0.0 \| 0.0 \| 0.0 \| 0.0036 \| 0.0541 \| \| 3.6948 \| 44.0 \| 220 \| 3.7714 \| 0.0023 \| 0.0077 \| 0.0001 \| 0.0 \| 0.0045 \| 0.0 \| 0.0 \| 0.0162 \| 0.0324 \| 0.0 \| 0.05 \| 0.0 \| 0.0 \| 0.0 \| 0.0046 \| 0.0649 \| \| 3.7546 \| 45.0 \| 225 \| 3.9600 \| 0.0074 \| 0.0207 \| 0.0 \| 0.0 \| 0.0126 \| 0.0 \| 0.0054 \| 0.0189 \| 0.0243 \| 0.0 \| 0.0375 \| 0.0 \| 0.0 \| 0.0 \| 0.0149 \| 0.0486 \| \| 3.6691 \| 46.0 \| 230 \| 3.8211 \| 0.0018 \| 0.0094 \| 0.0 \| 0.0 \| 0.0037 \| 0.0 \| 0.0 \| 0.0095 \| 0.0297 \| 0.0 \| 0.0458 \| 0.0 \| 0.0 \| 0.0 \| 0.0037 \| 0.0595 \| \| 3.5355 \| 47.0 \| 235 \| 3.6537 \| 0.0006 \| 0.0036 \| 0.0001 \| 0.0 \| 0.0017 \| 0.0 \| 0.0 \| 0.0068 \| 0.0216 \| 0.0 \| 0.0333 \| 0.0 \| 0.0 \| 0.0 \| 0.0012 \| 0.0432 \| \| 3.5232 \| 48.0 \| 240 \| 3.5552 \| 0.0004 \| 0.002 \| 0.0 \| 0.0 \| 0.0018 \| 0.0 \| 0.0 \| 0.0027 \| 0.0149 \| 0.0 \| 0.0229 \| 0.0 \| 0.0 \| 0.0 \| 0.0008 \| 0.0297 \| \| 3.48 \| 49.0 \| 245 \| 3.4924 \| 0.0014 \| 0.0126 \| 0.0 \| 0.0 \| 0.0043 \| 0.0017 \| 0.0014 \| 0.0108 \| 0.0122 \| 0.0 \| 0.0167 \| 0.05 \| 0.0 \| 0.0 \| 0.0028 \| 0.0243 \| \| 3.6056 \| 50.0 \| 250 \| 3.4697 \| 0.0007 \| 0.0024 \| 0.0 \| 0.0 \| 0.0019 \| 0.0002 \| 0.0 \| 0.0095 \| 0.0122 \| 0.0 \| 0.0167 \| 0.05 \| 0.0 \| 0.0 \| 0.0014 \| 0.0243 \| \| 3.4806 \| 51.0 \| 255 \| 3.5357 \| 0.0045 \| 0.0339 \| 0.0 \| 0.0 \| 0.0129 \| 0.0006 \| 0.0014 \| 0.023 \| 0.0284 \| 0.0 \| 0.0417 \| 0.05 \| 0.0 \| 0.0 \| 0.0091 \| 0.0568 \| \| 3.5547 \| 52.0 \| 260 \| 3.5216 \| 0.0016 \| 0.0141 \| 0.0 \| 0.0 \| 0.004 \| 0.0021 \| 0.0 \| 0.0108 \| 0.0257 \| 0.0 \| 0.0292 \| 0.25 \| 0.0 \| 0.0 \| 0.0032 \| 0.0514 \| \| 3.483 \| 53.0 \| 265 \| 3.4858 \| 0.0048 \| 0.028 \| 0.0005 \| 0.0 \| 0.0077 \| 0.0 \| 0.0 \| 0.0311 \| 0.0405 \| 0.0 \| 0.0625 \| 0.0 \| 0.0 \| 0.0 \| 0.0095 \| 0.0811 \| \| 3.6952 \| 54.0 \| 270 \| 3.4944 \| 0.0083 \| 0.0661 \| 0.0 \| 0.0 \| 0.0131 \| 0.0 \| 0.0041 \| 0.027 \| 0.0284 \| 0.0 \| 0.0437 \| 0.0 \| 0.0 \| 0.0 \| 0.0165 \| 0.0568 \| \| 3.5447 \| 55.0 \| 275 \| 3.5247 \| 0.0049 \| 0.0251 \| 0.0 \| 0.0 \| 0.0079 \| 0.0 \| 0.0027 \| 0.0176 \| 0.0257 \| 0.0 \| 0.0396 \| 0.0 \| 0.0 \| 0.0 \| 0.0099 \| 0.0514 \| \| 3.6204 \| 56.0 \| 280 \| 3.5457 \| 0.0069 \| 0.0452 \| 0.0 \| 0.0 \| 0.0168 \| 0.0 \| 0.0068 \| 0.0135 \| 0.0257 \| 0.0 \| 0.0396 \| 0.0 \| 0.0 \| 0.0 \| 0.0137 \| 0.0514 \| \| 3.3873 \| 57.0 \| 285 \| 3.4963 \| 0.0134 \| 0.0576 \| 0.0 \| 0.0 \| 0.0218 \| 0.0 \| 0.0081 \| 0.0365 \| 0.0473 \| 0.0 \| 0.0729 \| 0.0 \| 0.0 \| 0.0 \| 0.0268 \| 0.0946 \| \| 3.5384 \| 58.0 \| 290 \| 3.5618 \| 0.0016 \| 0.0083 \| 0.0 \| 0.0 \| 0.0025 \| 0.0 \| 0.0 \| 0.0122 \| 0.0338 \| 0.0 \| 0.0521 \| 0.0 \| 0.0 \| 0.0 \| 0.0032 \| 0.0676 \| \| 3.6034 \| 59.0 \| 295 \| 3.4600 \| 0.0016 \| 0.0102 \| 0.0 \| 0.0 \| 0.0025 \| 0.0 \| 0.0 \| 0.0081 \| 0.0324 \| 0.0 \| 0.05 \| 0.0 \| 0.0 \| 0.0 \| 0.0032 \| 0.0649 \| \| 3.5524 \| 60.0 \| 300 \| 3.4434 \| 0.0012 \| 0.0063 \| 0.0004 \| 0.0 \| 0.0019 \| 0.0 \| 0.0 \| 0.0041 \| 0.0324 \| 0.0 \| 0.05 \| 0.0 \| 0.0 \| 0.0 \| 0.0024 \| 0.0649 \| \| 3.4222 \| 61.0 \| 305 \| 3.5144 \| 0.0043 \| 0.0113 \| 0.0002 \| 0.0 \| 0.0066 \| 0.0 \| 0.0 \| 0.027 \| 0.0419 \| 0.0 \| 0.0646 \| 0.0 \| 0.0 \| 0.0 \| 0.0086 \| 0.0838 \| \| 3.451 \| 62.0 \| 310 \| 3.5189 \| 0.0198 \| 0.0582 \| 0.0002 \| 0.0 \| 0.0304 \| 0.0007 \| 0.0189 \| 0.0351 \| 0.0446 \| 0.0 \| 0.0646 \| 0.1 \| 0.0 \| 0.0 \| 0.0396 \| 0.0892 \| \| 3.4485 \| 63.0 \| 315 \| 3.4585 \| 0.0125 \| 0.0428 \| 0.0 \| 0.0 \| 0.0202 \| 0.0 \| 0.0135 \| 0.0297 \| 0.0405 \| 0.0 \| 0.0625 \| 0.0 \| 0.0 \| 0.0 \| 0.0249 \| 0.0811 \| \| 3.475 \| 64.0 \| 320 \| 3.3541 \| 0.0063 \| 0.0297 \| 0.0012 \| 0.0 \| 0.0111 \| 0.0016 \| 0.0027 \| 0.0216 \| 0.0405 \| 0.0 \| 0.0583 \| 0.1 \| 0.0 \| 0.0 \| 0.0126 \| 0.0811 \| \| 3.4189 \| 65.0 \| 325 \| 3.3517 \| 0.0051 \| 0.0315 \| 0.0 \| 0.0 \| 0.0094 \| 0.0 \| 0.0014 \| 0.0257 \| 0.0378 \| 0.0 \| 0.0583 \| 0.0 \| 0.0 \| 0.0 \| 0.0102 \| 0.0757 \| \| 3.324 \| 66.0 \| 330 \| 3.2557 \| 0.0025 \| 0.0134 \| 0.0 \| 0.0 \| 0.0045 \| 0.0 \| 0.0 \| 0.0216 \| 0.0243 \| 0.0 \| 0.0375 \| 0.0 \| 0.0 \| 0.0 \| 0.005 \| 0.0486 \| \| 3.5049 \| 67.0 \| 335 \| 3.3093 \| 0.0031 \| 0.0108 \| 0.0 \| 0.0 \| 0.0051 \| 0.0 \| 0.0 \| 0.027 \| 0.027 \| 0.0 \| 0.0417 \| 0.0 \| 0.0 \| 0.0 \| 0.0061 \| 0.0541 \| \| 3.3219 \| 68.0 \| 340 \| 3.3455 \| 0.0119 \| 0.0596 \| 0.0008 \| 0.0 \| 0.0189 \| 0.0 \| 0.0095 \| 0.0284 \| 0.0432 \| 0.0 \| 0.0667 \| 0.0 \| 0.0 \| 0.0 \| 0.0239 \| 0.0865 \| \| 3.3633 \| 69.0 \| 345 \| 3.4110 \| 0.0268 \| 0.0747 \| 0.0034 \| 0.0 \| 0.0414 \| 0.0 \| 0.0203 \| 0.0662 \| 0.0662 \| 0.0 \| 0.1021 \| 0.0 \| 0.0 \| 0.0 \| 0.0536 \| 0.1324 \| \| 3.4628 \| 70.0 \| 350 \| 3.3594 \| 0.0024 \| 0.0151 \| 0.0002 \| 0.0 \| 0.0037 \| 0.0 \| 0.0014 \| 0.0216 \| 0.0405 \| 0.0 \| 0.0625 \| 0.0 \| 0.0 \| 0.0 \| 0.0048 \| 0.0811 \| \| 3.272 \| 71.0 \| 355 \| 3.3655 \| 0.0039 \| 0.0174 \| 0.0008 \| 0.0 \| 0.0061 \| 0.0 \| 0.0014 \| 0.0149 \| 0.0473 \| 0.0 \| 0.0729 \| 0.0 \| 0.0 \| 0.0 \| 0.0079 \| 0.0946 \| \| 3.3713 \| 72.0 \| 360 \| 3.3455 \| 0.0097 \| 0.0609 \| 0.0018 \| 0.0 \| 0.015 \| 0.0 \| 0.0108 \| 0.0122 \| 0.0473 \| 0.0 \| 0.0729 \| 0.0 \| 0.0 \| 0.0 \| 0.0195 \| 0.0946 \| \| 3.258 \| 73.0 \| 365 \| 3.3679 \| 0.0006 \| 0.0025 \| 0.0 \| 0.0 \| 0.0011 \| 0.0 \| 0.0 \| 0.0068 \| 0.023 \| 0.0 \| 0.0354 \| 0.0 \| 0.0 \| 0.0 \| 0.0013 \| 0.0459 \| \| 3.3666 \| 74.0 \| 370 \| 3.3173 \| 0.0004 \| 0.0024 \| 0.0 \| 0.0 \| 0.0008 \| 0.0 \| 0.0 \| 0.0014 \| 0.0216 \| 0.0 \| 0.0333 \| 0.0 \| 0.0 \| 0.0 \| 0.0009 \| 0.0432 \| \| 3.2339 \| 75.0 \| 375 \| 3.3463 \| 0.002 \| 0.0103 \| 0.0 \| 0.0 \| 0.0033 \| 0.0 \| 0.0 \| 0.0081 \| 0.0405 \| 0.0 \| 0.0625 \| 0.0 \| 0.0 \| 0.0 \| 0.004 \| 0.0811 \| \| 3.2601 \| 76.0 \| 380 \| 3.3633 \| 0.002 \| 0.0111 \| 0.0 \| 0.0 \| 0.0032 \| 0.0 \| 0.0 \| 0.0081 \| 0.0392 \| 0.0 \| 0.0604 \| 0.0 \| 0.0 \| 0.0 \| 0.0039 \| 0.0784 \| \| 3.3584 \| 77.0 \| 385 \| 3.3331 \| 0.0025 \| 0.0108 \| 0.0 \| 0.0 \| 0.004 \| 0.0 \| 0.0 \| 0.0081 \| 0.0486 \| 0.0 \| 0.075 \| 0.0 \| 0.0 \| 0.0 \| 0.0051 \| 0.0973 \| \| 3.2928 \| 78.0 \| 390 \| 3.3012 \| 0.0043 \| 0.0139 \| 0.0002 \| 0.0 \| 0.0066 \| 0.0 \| 0.0 \| 0.0162 \| 0.0635 \| 0.0 \| 0.0979 \| 0.0 \| 0.0 \| 0.0 \| 0.0085 \| 0.127 \| \| 3.2633 \| 79.0 \| 395 \| 3.2913 \| 0.0076 \| 0.0226 \| 0.0003 \| 0.0 \| 0.0117 \| 0.0 \| 0.0027 \| 0.0392 \| 0.0432 \| 0.0 \| 0.0667 \| 0.0 \| 0.0 \| 0.0 \| 0.0153 \| 0.0865 \| \| 3.3965 \| 80.0 \| 400 \| 3.3141 \| 0.008 \| 0.0224 \| 0.0051 \| 0.0 \| 0.0126 \| 0.0 \| 0.0014 \| 0.0405 \| 0.0419 \| 0.0 \| 0.0646 \| 0.0 \| 0.0 \| 0.0 \| 0.016 \| 0.0838 \| \| 3.2753 \| 81.0 \| 405 \| 3.4127 \| 0.0077 \| 0.0231 \| 0.0036 \| 0.0 \| 0.012 \| 0.0 \| 0.0 \| 0.0405 \| 0.0432 \| 0.0 \| 0.0667 \| 0.0 \| 0.0 \| 0.0 \| 0.0155 \| 0.0865 \| \| 3.3362 \| 82.0 \| 410 \| 3.4194 \| 0.0099 \| 0.0365 \| 0.0032 \| 0.0 \| 0.0152 \| 0.0 \| 0.0054 \| 0.0473 \| 0.0568 \| 0.0 \| 0.0875 \| 0.0 \| 0.0 \| 0.0 \| 0.0197 \| 0.1135 \| \| 3.3752 \| 83.0 \| 415 \| 3.3421 \| 0.0086 \| 0.0411 \| 0.0 \| 0.0 \| 0.0134 \| 0.0 \| 0.0014 \| 0.0351 \| 0.0432 \| 0.0 \| 0.0667 \| 0.0 \| 0.0 \| 0.0 \| 0.0173 \| 0.0865 \| \| 3.3222 \| 84.0 \| 420 \| 3.2956 \| 0.004 \| 0.0234 \| 0.0 \| 0.0 \| 0.0063 \| 0.0 \| 0.0014 \| 0.0243 \| 0.0243 \| 0.0 \| 0.0375 \| 0.0 \| 0.0 \| 0.0 \| 0.008 \| 0.0486 \| \| 3.3732 \| 85.0 \| 425 \| 3.2515 \| 0.0281 \| 0.0614 \| 0.0 \| 0.0 \| 0.0434 \| 0.0 \| 0.0257 \| 0.0338 \| 0.0459 \| 0.0 \| 0.0708 \| 0.0 \| 0.0 \| 0.0 \| 0.0562 \| 0.0919 \| \| 3.1932 \| 86.0 \| 430 \| 3.2197 \| 0.0263 \| 0.0603 \| 0.0 \| 0.0 \| 0.0409 \| 0.0 \| 0.0243 \| 0.0297 \| 0.0459 \| 0.0 \| 0.0708 \| 0.0 \| 0.0 \| 0.0 \| 0.0527 \| 0.0919 \| \| 3.3181 \| 87.0 \| 435 \| 3.3373 \| 0.0027 \| 0.0142 \| 0.0 \| 0.0 \| 0.0042 \| 0.0 \| 0.0 \| 0.023 \| 0.0297 \| 0.0 \| 0.0458 \| 0.0 \| 0.0 \| 0.0 \| 0.0055 \| 0.0595 \| \| 3.3382 \| 88.0 \| 440 \| 3.3707 \| 0.0019 \| 0.0069 \| 0.0 \| 0.0 \| 0.003 \| 0.0 \| 0.0 \| 0.0243 \| 0.0297 \| 0.0 \| 0.0458 \| 0.0 \| 0.0 \| 0.0 \| 0.0039 \| 0.0595 \| \| 3.1525 \| 89.0 \| 445 \| 3.4728 \| 0.0025 \| 0.0056 \| 0.0 \| 0.0 \| 0.0038 \| 0.0 \| 0.0 \| 0.0324 \| 0.0324 \| 0.0 \| 0.05 \| 0.0 \| 0.0 \| 0.0 \| 0.005 \| 0.0649 \| \| 3.1836 \| 90.0 \| 450 \| 3.4726 \| 0.0103 \| 0.0357 \| 0.0 \| 0.0 \| 0.0163 \| 0.0 \| 0.0041 \| 0.0486 \| 0.05 \| 0.0 \| 0.0771 \| 0.0 \| 0.0 \| 0.0 \| 0.0206 \| 0.1 \| \| 3.2294 \| 91.0 \| 455 \| 3.4077 \| 0.0116 \| 0.0309 \| 0.0 \| 0.0 \| 0.0183 \| 0.0 \| 0.0041 \| 0.0581 \| 0.0595 \| 0.0 \| 0.0917 \| 0.0 \| 0.0 \| 0.0 \| 0.0233 \| 0.1189 \| \| 3.2468 \| 92.0 \| 460 \| 3.3905 \| 0.0101 \| 0.0324 \| 0.0004 \| 0.0 \| 0.0158 \| 0.0 \| 0.0027 \| 0.0581 \| 0.0581 \| 0.0 \| 0.0896 \| 0.0 \| 0.0 \| 0.0 \| 0.0202 \| 0.1162 \| \| 3.2433 \| 93.0 \| 465 \| 3.3989 \| 0.0099 \| 0.0309 \| 0.0004 \| 0.0 \| 0.0155 \| 0.0 \| 0.0027 \| 0.0581 \| 0.0581 \| 0.0 \| 0.0896 \| 0.0 \| 0.0 \| 0.0 \| 0.0198 \| 0.1162 \| \| 3.152 \| 94.0 \| 470 \| 3.3957 \| 0.0113 \| 0.0352 \| 0.0003 \| 0.0 \| 0.0177 \| 0.0 \| 0.0027 \| 0.0568 \| 0.0568 \| 0.0 \| 0.0875 \| 0.0 \| 0.0 \| 0.0 \| 0.0226 \| 0.1135 \| \| 3.2372 \| 95.0 \| 475 \| 3.3941 \| 0.0105 \| 0.0345 \| 0.0003 \| 0.0 \| 0.0163 \| 0.0 \| 0.0027 \| 0.0527 \| 0.0527 \| 0.0 \| 0.0812 \| 0.0 \| 0.0 \| 0.0 \| 0.0209 \| 0.1054 \| \| 3.2734 \| 96.0 \| 480 \| 3.3851 \| 0.0103 \| 0.0256 \| 0.0003 \| 0.0 \| 0.016 \| 0.0 \| 0.0041 \| 0.0568 \| 0.0568 \| 0.0 \| 0.0875 \| 0.0 \| 0.0 \| 0.0 \| 0.0205 \| 0.1135 \| \| 3.2559 \| 97.0 \| 485 \| 3.3807 \| 0.0113 \| 0.0308 \| 0.0003 \| 0.0 \| 0.0174 \| 0.0 \| 0.0041 \| 0.0595 \| 0.0608 \| 0.0 \| 0.0938 \| 0.0 \| 0.0 \| 0.0 \| 0.0226 \| 0.1216 \| \| 3.3223 \| 98.0 \| 490 \| 3.3773 \| 0.0094 \| 0.03 \| 0.0003 \| 0.0 \| 0.0146 \| 0.0 \| 0.0027 \| 0.0527 \| 0.0608 \| 0.0 \| 0.0938 \| 0.0 \| 0.0 \| 0.0 \| 0.0189 \| 0.1216 \| \| 3.1813 \| 99.0 \| 495 \| 3.3789 \| 0.0084 \| 0.025 \| 0.0004 \| 0.0 \| 0.0129 \| 0.0 \| 0.0027 \| 0.0486 \| 0.0622 \| 0.0 \| 0.0958 \| 0.0 \| 0.0 \| 0.0 \| 0.0168 \| 0.1243 \| \| 3.1959 \| 100.0 \| 500 \| 3.3774 \| 0.0076 \| 0.0237 \| 0.0004 \| 0.0 \| 0.0117 \| 0.0 \| 0.0014 \| 0.0459 \| 0.0622 \| 0.0 \| 0.0958 \| 0.0 \| 0.0 \| 0.0 \| 0.0152 \| 0.1243 \| ### Framework versions - Transformers 4.44.0.dev0 - Pytorch 2.3.1 - Datasets 2.20.0 - Tokenizers 0.19.1	[ "table", "text" ]
Mudassir41/silkworm_rt-detr	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "can", "heathly", "sick" ]
anastasispk/law-game-evidence-replacement-finetune-v1	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # law-game-evidence-replacement-finetune This model is a fine-tuned version of [PekingU/rtdetr_r50vd_coco_o365](https://huggingface.co/PekingU/rtdetr_r50vd_coco_o365) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 3.4253 - eval_map: 0.8264 - eval_map_50: 0.8488 - eval_map_75: 0.8441 - eval_map_small: 0.5688 - eval_map_medium: 0.9527 - eval_map_large: 0.8547 - eval_mar_1: 0.7043 - eval_mar_10: 0.9575 - eval_mar_100: 0.9727 - eval_mar_small: 0.5949 - eval_mar_medium: 0.9738 - eval_mar_large: 0.9894 - eval_map_evidence: -1.0 - eval_mar_100_evidence: -1.0 - eval_map_ambulance: 0.9802 - eval_mar_100_ambulance: 0.9899 - eval_map_artificial_target: 0.9267 - eval_mar_100_artificial_target: 0.9572 - eval_map_cartridge: 0.9742 - eval_mar_100_cartridge: 0.9949 - eval_map_gun: 0.9165 - eval_mar_100_gun: 0.9403 - eval_map_knife: 0.8599 - eval_mar_100_knife: 0.931 - eval_map_police: 0.9935 - eval_mar_100_police: 0.9959 - eval_map_traffic: 0.9586 - eval_mar_100_traffic: 0.9726 - eval_map_traffic_cone: 0.0013 - eval_mar_100_traffic_cone: 1.0 - eval_runtime: 50.5267 - eval_samples_per_second: 16.467 - eval_steps_per_second: 2.058 - epoch: 28.0 - step: 5152 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 300 - num_epochs: 50 ### Framework versions - Transformers 4.44.0.dev0 - Pytorch 2.3.1+cu121 - Tokenizers 0.19.1	[ "evidence", "ambulance", "artificial_target", "cartridge", "gun", "knife", "police", "traffic", "traffic_cone" ]
psyche/document-layout-detector	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "text", "title", "list", "table", "figure" ]
anastasispk/law-game-evidence-replacement-finetune	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # law-game-evidence-replacement-finetune This model is a fine-tuned version of [PekingU/rtdetr_r50vd_coco_o365](https://huggingface.co/PekingU/rtdetr_r50vd_coco_o365) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.5533 - Map: 0.9339 - Map 50: 0.9616 - Map 75: 0.9575 - Map Small: 0.5574 - Map Medium: 0.9423 - Map Large: 0.9699 - Mar 1: 0.6597 - Mar 10: 0.9522 - Mar 100: 0.9722 - Mar Small: 0.7411 - Mar Medium: 0.9806 - Mar Large: 0.9908 - Map Evidence: -1.0 - Mar 100 Evidence: -1.0 - Map Ambulance: 0.9802 - Mar 100 Ambulance: 0.9899 - Map Artificial Target: 0.9245 - Mar 100 Artificial Target: 0.9611 - Map Cartridge: 0.9759 - Mar 100 Cartridge: 0.9937 - Map Gun: 0.9225 - Mar 100 Gun: 0.9542 - Map Knife: 0.8562 - Mar 100 Knife: 0.9404 - Map Police: 0.9495 - Mar 100 Police: 0.999 - Map Traffic Cone: 0.9285 - Mar 100 Traffic Cone: 0.9673 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 300 - num_epochs: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Small \| Map Medium \| Map Large \| Mar 1 \| Mar 10 \| Mar 100 \| Mar Small \| Mar Medium \| Mar Large \| Map Evidence \| Mar 100 Evidence \| Map Ambulance \| Mar 100 Ambulance \| Map Artificial Target \| Mar 100 Artificial Target \| Map Cartridge \| Mar 100 Cartridge \| Map Gun \| Mar 100 Gun \| Map Knife \| Mar 100 Knife \| Map Police \| Mar 100 Police \| Map Traffic Cone \| Mar 100 Traffic Cone \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:---------:\|:----------:\|:---------:\|:------------:\|:----------------:\|:-------------:\|:-----------------:\|:---------------------:\|:-------------------------:\|:-------------:\|:-----------------:\|:-------:\|:-----------:\|:---------:\|:-------------:\|:----------:\|:--------------:\|:----------------:\|:--------------------:\| \| No log \| 1.0 \| 183 \| 17.1925 \| 0.553 \| 0.61 \| 0.584 \| 0.1918 \| 0.3235 \| 0.6555 \| 0.5467 \| 0.8763 \| 0.8964 \| 0.3142 \| 0.8206 \| 0.9705 \| -1.0 \| -1.0 \| 0.9057 \| 0.9848 \| 0.5233 \| 0.7299 \| 0.9125 \| 0.9647 \| 0.1841 \| 0.9194 \| 0.6003 \| 0.8687 \| 0.518 \| 0.9286 \| 0.2268 \| 0.8789 \| \| No log \| 2.0 \| 366 \| 7.1301 \| 0.7763 \| 0.8536 \| 0.8146 \| 0.2855 \| 0.6006 \| 0.875 \| 0.6198 \| 0.9116 \| 0.9359 \| 0.62 \| 0.876 \| 0.9781 \| -1.0 \| -1.0 \| 0.9418 \| 0.9707 \| 0.7052 \| 0.8648 \| 0.9529 \| 0.9733 \| 0.5436 \| 0.9667 \| 0.7831 \| 0.9172 \| 0.8516 \| 0.9398 \| 0.656 \| 0.9191 \| \| 37.3669 \| 3.0 \| 549 \| 5.7075 \| 0.848 \| 0.9115 \| 0.8936 \| 0.3256 \| 0.7543 \| 0.9317 \| 0.6317 \| 0.9274 \| 0.9486 \| 0.6783 \| 0.9289 \| 0.9849 \| -1.0 \| -1.0 \| 0.9687 \| 0.9879 \| 0.7575 \| 0.8761 \| 0.9619 \| 0.9822 \| 0.8187 \| 0.9653 \| 0.8076 \| 0.9172 \| 0.9181 \| 0.9827 \| 0.7032 \| 0.9287 \| \| 37.3669 \| 4.0 \| 732 \| 5.8395 \| 0.8232 \| 0.8809 \| 0.8653 \| 0.3221 \| 0.7104 \| 0.8994 \| 0.642 \| 0.9362 \| 0.9536 \| 0.688 \| 0.9333 \| 0.9884 \| -1.0 \| -1.0 \| 0.9718 \| 0.9899 \| 0.8061 \| 0.8878 \| 0.9676 \| 0.9854 \| 0.8731 \| 0.9778 \| 0.7678 \| 0.9162 \| 0.6454 \| 0.9867 \| 0.7303 \| 0.9317 \| \| 37.3669 \| 5.0 \| 915 \| 5.2081 \| 0.8722 \| 0.924 \| 0.9156 \| 0.3818 \| 0.7789 \| 0.951 \| 0.6457 \| 0.9406 \| 0.9593 \| 0.6963 \| 0.9663 \| 0.9887 \| -1.0 \| -1.0 \| 0.976 \| 0.9899 \| 0.8077 \| 0.9071 \| 0.973 \| 0.9869 \| 0.7967 \| 0.9611 \| 0.8391 \| 0.9313 \| 0.8822 \| 0.9908 \| 0.8309 \| 0.9482 \| \| 4.4127 \| 6.0 \| 1098 \| 5.4515 \| 0.8848 \| 0.9339 \| 0.9262 \| 0.5118 \| 0.8295 \| 0.9572 \| 0.6538 \| 0.9446 \| 0.9624 \| 0.6997 \| 0.9621 \| 0.9903 \| -1.0 \| -1.0 \| 0.9686 \| 0.9889 \| 0.7937 \| 0.9057 \| 0.9784 \| 0.9886 \| 0.8982 \| 0.9722 \| 0.8491 \| 0.9434 \| 0.8521 \| 0.9888 \| 0.8534 \| 0.9495 \| \| 4.4127 \| 7.0 \| 1281 \| 4.9756 \| 0.9019 \| 0.9468 \| 0.9396 \| 0.5037 \| 0.8805 \| 0.9631 \| 0.6476 \| 0.9443 \| 0.9666 \| 0.703 \| 0.9692 \| 0.9932 \| -1.0 \| -1.0 \| 0.9754 \| 0.9889 \| 0.821 \| 0.9129 \| 0.9753 \| 0.9907 \| 0.9017 \| 0.9833 \| 0.8054 \| 0.9414 \| 0.9414 \| 0.9949 \| 0.8933 \| 0.9541 \| \| 4.4127 \| 8.0 \| 1464 \| 4.4998 \| 0.9119 \| 0.9554 \| 0.9432 \| 0.5098 \| 0.9047 \| 0.9619 \| 0.6482 \| 0.9436 \| 0.9641 \| 0.7091 \| 0.9786 \| 0.9876 \| -1.0 \| -1.0 \| 0.9726 \| 0.9899 \| 0.869 \| 0.9248 \| 0.9738 \| 0.9902 \| 0.8528 \| 0.9556 \| 0.8667 \| 0.9384 \| 0.9631 \| 0.9939 \| 0.8853 \| 0.9561 \| \| 3.3287 \| 9.0 \| 1647 \| 4.5378 \| 0.9107 \| 0.9472 \| 0.9424 \| 0.5347 \| 0.91 \| 0.9625 \| 0.6551 \| 0.9498 \| 0.9694 \| 0.7243 \| 0.984 \| 0.9908 \| -1.0 \| -1.0 \| 0.9802 \| 0.9899 \| 0.8691 \| 0.9281 \| 0.9785 \| 0.9939 \| 0.9128 \| 0.9736 \| 0.8646 \| 0.9424 \| 0.8889 \| 0.9929 \| 0.8805 \| 0.965 \| \| 3.3287 \| 10.0 \| 1830 \| 5.0033 \| 0.8831 \| 0.9264 \| 0.9202 \| 0.5206 \| 0.8887 \| 0.9369 \| 0.6497 \| 0.9456 \| 0.9661 \| 0.7104 \| 0.9714 \| 0.9895 \| -1.0 \| -1.0 \| 0.9404 \| 0.9899 \| 0.8686 \| 0.929 \| 0.9741 \| 0.9917 \| 0.92 \| 0.9722 \| 0.8131 \| 0.9323 \| 0.7768 \| 0.9908 \| 0.889 \| 0.9568 \| \| 2.8465 \| 11.0 \| 2013 \| 4.1896 \| 0.9183 \| 0.9522 \| 0.9491 \| 0.4507 \| 0.8926 \| 0.9704 \| 0.6595 \| 0.9497 \| 0.9676 \| 0.7033 \| 0.9677 \| 0.9884 \| -1.0 \| -1.0 \| 0.9786 \| 0.9899 \| 0.8902 \| 0.9333 \| 0.9745 \| 0.993 \| 0.9182 \| 0.9583 \| 0.8633 \| 0.9424 \| 0.9004 \| 0.9929 \| 0.9031 \| 0.963 \| \| 2.8465 \| 12.0 \| 2196 \| 4.3806 \| 0.9118 \| 0.9486 \| 0.9445 \| 0.5313 \| 0.8959 \| 0.9574 \| 0.6545 \| 0.9487 \| 0.9701 \| 0.688 \| 0.9741 \| 0.9929 \| -1.0 \| -1.0 \| 0.9791 \| 0.9899 \| 0.8856 \| 0.935 \| 0.9736 \| 0.9924 \| 0.9151 \| 0.975 \| 0.8429 \| 0.9384 \| 0.8852 \| 0.998 \| 0.9008 \| 0.9617 \| \| 2.8465 \| 13.0 \| 2379 \| 4.3575 \| 0.9131 \| 0.9471 \| 0.9419 \| 0.5419 \| 0.9126 \| 0.9643 \| 0.6576 \| 0.9531 \| 0.9717 \| 0.7239 \| 0.9875 \| 0.9909 \| -1.0 \| -1.0 \| 0.9677 \| 0.9899 \| 0.8731 \| 0.9358 \| 0.9774 \| 0.9951 \| 0.9226 \| 0.9708 \| 0.8794 \| 0.9545 \| 0.8601 \| 0.9939 \| 0.9114 \| 0.9617 \| \| 2.5085 \| 14.0 \| 2562 \| 4.0609 \| 0.9277 \| 0.9619 \| 0.9539 \| 0.5802 \| 0.9195 \| 0.9659 \| 0.6566 \| 0.9518 \| 0.9703 \| 0.7168 \| 0.9791 \| 0.9913 \| -1.0 \| -1.0 \| 0.9697 \| 0.9899 \| 0.902 \| 0.9451 \| 0.9819 \| 0.9958 \| 0.9273 \| 0.9667 \| 0.8392 \| 0.9374 \| 0.954 \| 0.9939 \| 0.9199 \| 0.9634 \| \| 2.5085 \| 15.0 \| 2745 \| 4.2034 \| 0.9284 \| 0.961 \| 0.9559 \| 0.541 \| 0.9483 \| 0.9743 \| 0.6606 \| 0.9502 \| 0.9695 \| 0.7169 \| 0.9792 \| 0.9902 \| -1.0 \| -1.0 \| 0.979 \| 0.9899 \| 0.9022 \| 0.9469 \| 0.9781 \| 0.9947 \| 0.9251 \| 0.9611 \| 0.8495 \| 0.9404 \| 0.9481 \| 0.9918 \| 0.9167 \| 0.9614 \| \| 2.5085 \| 16.0 \| 2928 \| 4.1849 \| 0.9283 \| 0.9599 \| 0.9559 \| 0.5591 \| 0.9323 \| 0.9644 \| 0.6575 \| 0.9493 \| 0.9697 \| 0.7267 \| 0.9795 \| 0.988 \| -1.0 \| -1.0 \| 0.9716 \| 0.9899 \| 0.9033 \| 0.948 \| 0.9754 \| 0.9949 \| 0.9108 \| 0.9583 \| 0.8469 \| 0.9404 \| 0.9675 \| 0.9929 \| 0.9222 \| 0.9634 \| \| 2.2183 \| 17.0 \| 3111 \| 4.0696 \| 0.9222 \| 0.9556 \| 0.9503 \| 0.5517 \| 0.9288 \| 0.9634 \| 0.6572 \| 0.9523 \| 0.9726 \| 0.7348 \| 0.9863 \| 0.992 \| -1.0 \| -1.0 \| 0.9707 \| 0.9899 \| 0.9052 \| 0.9496 \| 0.9784 \| 0.9949 \| 0.9309 \| 0.9694 \| 0.8074 \| 0.9465 \| 0.9525 \| 0.9959 \| 0.91 \| 0.962 \| \| 2.2183 \| 18.0 \| 3294 \| 4.3283 \| 0.9126 \| 0.9461 \| 0.9414 \| 0.5422 \| 0.9246 \| 0.9498 \| 0.6564 \| 0.9502 \| 0.9698 \| 0.7138 \| 0.9805 \| 0.9896 \| -1.0 \| -1.0 \| 0.9723 \| 0.9899 \| 0.901 \| 0.9483 \| 0.9815 \| 0.9952 \| 0.9204 \| 0.9528 \| 0.7964 \| 0.9394 \| 0.9043 \| 0.9969 \| 0.9125 \| 0.966 \| \| 2.2183 \| 19.0 \| 3477 \| 3.7839 \| 0.9209 \| 0.9518 \| 0.9477 \| 0.5608 \| 0.9475 \| 0.9583 \| 0.6562 \| 0.9512 \| 0.9701 \| 0.7414 \| 0.9806 \| 0.9885 \| -1.0 \| -1.0 \| 0.9566 \| 0.9899 \| 0.9131 \| 0.9531 \| 0.9779 \| 0.9949 \| 0.9132 \| 0.9486 \| 0.833 \| 0.9404 \| 0.9407 \| 0.9959 \| 0.9117 \| 0.9677 \| \| 2.009 \| 20.0 \| 3660 \| 3.7275 \| 0.9287 \| 0.958 \| 0.9542 \| 0.5558 \| 0.9078 \| 0.9681 \| 0.6586 \| 0.951 \| 0.9709 \| 0.7422 \| 0.979 \| 0.9892 \| -1.0 \| -1.0 \| 0.9802 \| 0.9899 \| 0.9239 \| 0.96 \| 0.9761 \| 0.9944 \| 0.9222 \| 0.9514 \| 0.8345 \| 0.9364 \| 0.9389 \| 0.998 \| 0.9248 \| 0.966 \| \| 2.009 \| 21.0 \| 3843 \| 3.8496 \| 0.93 \| 0.9592 \| 0.9554 \| 0.5552 \| 0.9187 \| 0.9664 \| 0.6581 \| 0.9508 \| 0.9708 \| 0.7373 \| 0.9788 \| 0.9904 \| -1.0 \| -1.0 \| 0.9802 \| 0.9899 \| 0.9148 \| 0.9565 \| 0.9778 \| 0.9949 \| 0.9227 \| 0.9556 \| 0.853 \| 0.9364 \| 0.9443 \| 1.0 \| 0.9167 \| 0.9624 \| \| 1.8494 \| 22.0 \| 4026 \| 3.6452 \| 0.9309 \| 0.9592 \| 0.9551 \| 0.5561 \| 0.929 \| 0.9664 \| 0.6595 \| 0.9495 \| 0.9709 \| 0.723 \| 0.9801 \| 0.9902 \| -1.0 \| -1.0 \| 0.9802 \| 0.9899 \| 0.9176 \| 0.9593 \| 0.9764 \| 0.9935 \| 0.9212 \| 0.95 \| 0.8459 \| 0.9374 \| 0.9471 \| 0.999 \| 0.928 \| 0.9673 \| \| 1.8494 \| 23.0 \| 4209 \| 3.6352 \| 0.9299 \| 0.9587 \| 0.9546 \| 0.5524 \| 0.9155 \| 0.9681 \| 0.659 \| 0.9509 \| 0.9708 \| 0.7217 \| 0.98 \| 0.9902 \| -1.0 \| -1.0 \| 0.9802 \| 0.9899 \| 0.9175 \| 0.9589 \| 0.9756 \| 0.9935 \| 0.9217 \| 0.9514 \| 0.8458 \| 0.9364 \| 0.9448 \| 0.999 \| 0.9236 \| 0.9667 \| \| 1.8494 \| 24.0 \| 4392 \| 3.6526 \| 0.9298 \| 0.9577 \| 0.9535 \| 0.5572 \| 0.9119 \| 0.9666 \| 0.6593 \| 0.9518 \| 0.972 \| 0.725 \| 0.9794 \| 0.9911 \| -1.0 \| -1.0 \| 0.9802 \| 0.9899 \| 0.9229 \| 0.9609 \| 0.976 \| 0.9934 \| 0.9217 \| 0.9542 \| 0.8493 \| 0.9394 \| 0.936 \| 1.0 \| 0.9226 \| 0.9663 \| \| 1.7025 \| 25.0 \| 4575 \| 3.5533 \| 0.9339 \| 0.9616 \| 0.9575 \| 0.5574 \| 0.9423 \| 0.9699 \| 0.6597 \| 0.9522 \| 0.9722 \| 0.7411 \| 0.9806 \| 0.9908 \| -1.0 \| -1.0 \| 0.9802 \| 0.9899 \| 0.9245 \| 0.9611 \| 0.9759 \| 0.9937 \| 0.9225 \| 0.9542 \| 0.8562 \| 0.9404 \| 0.9495 \| 0.999 \| 0.9285 \| 0.9673 \| ### Framework versions - Transformers 4.44.0.dev0 - Pytorch 2.3.1+cu121 - Tokenizers 0.19.1	[ "evidence", "ambulance", "artificial_target", "cartridge", "gun", "knife", "police", "traffic_cone" ]
NabilaLM/detr-weapons-detection	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Shrusti1/detr_shrusti	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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cmarkea/detr-layout-detection	# DETR-layout-detection We present the model cmarkea/detr-layout-detection, which allows extracting different layouts (Text, Picture, Caption, Footnote, etc.) from an image of a document. This is a fine-tuning of the model [detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the [DocLayNet](https://huggingface.co/datasets/ds4sd/DocLayNet) dataset. This model can jointly predict masks and bounding boxes for documentary objects. It is ideal for processing documentary corpora to be ingested into an ODQA system. This model allows extracting 11 entities, which are: Caption, Footnote, Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, and Title. ## Performance In this section, we will assess the model's performance by separately considering semantic segmentation and object detection. In both cases, no post-processing was applied after estimation. For semantic segmentation, we will use the F1-score to evaluate the classification of each pixel. For object detection, we will assess performance based on the Generalized Intersection over Union (GIoU) and the accuracy of the predicted bounding box class. The evaluation is conducted on 500 pages from the PDF evaluation dataset of DocLayNet. \| Class \| f1-score (x100) \| GIoU (x100) \| accuracy (x100) \| \|:--------------:\|:---------------:\|:-----------:\|:---------------:\| \| Background \| 95.82 \| NA \| NA \| \| Caption \| 82.68 \| 74.71 \| 69.05 \| \| Footnote \| 78.19 \| 74.71 \| 74.19 \| \| Formula \| 87.25 \| 76.31 \| 97.79 \| \| List-item \| 81.43 \| 77.0 \| 90.62 \| \| Page-footer \| 82.01 \| 69.86 \| 96.64 \| \| Page-header \| 68.32 \| 77.68 \| 88.3 \| \| Picture \| 81.04 \| 81.84 \| 90.88 \| \| Section-header \| 73.52 \| 73.46 \| 85.96 \| \| Table \| 78.59 \| 85.45 \| 90.58 \| \| Text \| 91.93 \| 83.16 \| 91.8 \| \| Title \| 70.38 \| 74.13 \| 63.33 \| ## Benchmark Now, let's compare the performance of this model with other models. \| Model \| f1-score (x100) \| GIoU (x100) \| accuracy (x100) \| \|:---------------------------------------------------------------------------------------------:\|:---------------:\|:-----------:\|:---------------:\| \| cmarkea/detr-layout-detection \| 91.27 \| 80.66 \| 90.46 \| \| [cmarkea/dit-base-layout-detection](https://huggingface.co/cmarkea/dit-base-layout-detection) \| 90.77 \| 56.29 \| 85.26 \| ## Direct Use ```python from transformers import AutoImageProcessor from transformers.models.detr import DetrForSegmentation img_proc = AutoImageProcessor.from_pretrained( "cmarkea/detr-layout-detection" ) model = DetrForSegmentation.from_pretrained( "cmarkea/detr-layout-detection" ) img: PIL.Image with torch.inference_mode(): input_ids = img_proc(img, return_tensors='pt') output = model(**input_ids) threshold=0.4 segmentation_mask = img_proc.post_process_segmentation( output, threshold=threshold, target_sizes=[img.size[::-1]] ) bbox_pred = img_proc.post_process_object_detection( output, threshold=threshold, target_sizes=[img.size[::-1]] ) ``` ### Example ![example](https://i.postimg.cc/1X6zr216/detr.png) ### Citation ``` @online{DeDetrLay, AUTHOR = {Cyrile Delestre}, URL = {https://huggingface.co/cmarkea/detr-layout-detection}, YEAR = {2024}, KEYWORDS = {Image Processing ; Transformers ; Layout}, } ```	[ "caption", "footnote", "formula", "list-item", "page-footer", "page-header", "picture", "section-header", "table", "text", "title" ]
NabilaLM/detr-weapons-detection_30ep	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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NabilaLM/detr-weapons-detection_40ep	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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deepaksen12/detr-resnet-50_finetuned_cppe5-1	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet-50_finetuned_cppe5-1 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.43.3 - Pytorch 2.4.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
NabilaLM/detr-weapons-detection_50ep	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Shrusti1/detr_shrusti_100	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Overseer66/cervix_and_blurry_cervix_detector	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # cervix_and_blurry_cervix_detector This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on the webdataset dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.43.3 - Pytorch 1.14.0a0+410ce96 - Datasets 2.20.0 - Tokenizers 0.19.1	[ "cervix", "blurrycervix" ]
Shrusti1/detr_shrusti_60	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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deepaksen12/detr-resnet-50_finetuned_cppe5-3	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-resnet-50_finetuned_cppe5-3 This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.43.3 - Pytorch 2.4.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
Shrusti1/detr_shrusti_100N	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Shrusti1/detr_shrusti_50_conditional	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "bone-fracture", "angle", "fracture", "line", "messed_up_angle" ]
farhanishraq/table_tr-finetuned-bs	# Bank Statement Structure Detection A table structure detection model capable of parsing bank statements. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of bank statement table structure detection model, finetuned on a custom dataset collected from personal bank statements. It is capable of detecting rows and columns from Chase and Bank of America. - Developed by: Ahmad Farhan Ishraq - Finetuned from model [optional]: microsoft/table-transformer-detection ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary	[ "no object", "table row", "table column" ]
bellayu/transformer-scorebug	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7" ]
anastasispk/law-game-replace-finetune	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # law-game-replace-finetune This model is a fine-tuned version of [PekingU/rtdetr_r50vd_coco_o365](https://huggingface.co/PekingU/rtdetr_r50vd_coco_o365) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 5.6852 - Map: 0.8547 - Map 50: 0.9219 - Map 75: 0.8855 - Map Small: 0.6003 - Map Medium: 0.6465 - Map Large: 0.9074 - Mar 1: 0.652 - Mar 10: 0.9364 - Mar 100: 0.9452 - Mar Small: 0.779 - Mar Medium: 0.924 - Mar Large: 0.9818 - Map Evidence: -1.0 - Mar 100 Evidence: -1.0 - Map Ambulance: 1.0 - Mar 100 Ambulance: 1.0 - Map Artificial Target: 0.8084 - Mar 100 Artificial Target: 0.8727 - Map Cartridge: 0.9022 - Mar 100 Cartridge: 0.9681 - Map Gun: 0.7068 - Mar 100 Gun: 0.9667 - Map Knife: 0.7732 - Mar 100 Knife: 0.9111 - Map Police: 0.9573 - Mar 100 Police: 0.9818 - Map Traffic: 0.835 - Mar 100 Traffic: 0.9162 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 300 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Small \| Map Medium \| Map Large \| Mar 1 \| Mar 10 \| Mar 100 \| Mar Small \| Mar Medium \| Mar Large \| Map Evidence \| Mar 100 Evidence \| Map Ambulance \| Mar 100 Ambulance \| Map Artificial Target \| Mar 100 Artificial Target \| Map Cartridge \| Mar 100 Cartridge \| Map Gun \| Mar 100 Gun \| Map Knife \| Mar 100 Knife \| Map Police \| Mar 100 Police \| Map Traffic \| Mar 100 Traffic \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:---------:\|:----------:\|:---------:\|:------------:\|:----------------:\|:-------------:\|:-----------------:\|:---------------------:\|:-------------------------:\|:-------------:\|:-----------------:\|:-------:\|:-----------:\|:---------:\|:-------------:\|:----------:\|:--------------:\|:-----------:\|:---------------:\| \| No log \| 1.0 \| 35 \| 134.6829 \| 0.0007 \| 0.0015 \| 0.0001 \| 0.0 \| 0.0001 \| 0.0025 \| 0.0014 \| 0.0168 \| 0.0462 \| 0.0 \| 0.0686 \| 0.0457 \| -1.0 \| -1.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0023 \| 0.0033 \| 0.0203 \| 0.0 \| 0.0407 \| 0.0001 \| 0.1667 \| 0.0009 \| 0.0909 \| 0.0004 \| 0.0027 \| \| No log \| 2.0 \| 70 \| 65.3809 \| 0.0021 \| 0.0028 \| 0.0022 \| 0.0001 \| 0.0003 \| 0.0074 \| 0.0229 \| 0.0958 \| 0.167 \| 0.095 \| 0.3103 \| 0.1895 \| -1.0 \| -1.0 \| 0.0 \| 0.0 \| 0.0 \| 0.0114 \| 0.0 \| 0.0203 \| 0.0002 \| 0.2444 \| 0.0007 \| 0.4185 \| 0.007 \| 0.3364 \| 0.0064 \| 0.1378 \| \| No log \| 3.0 \| 105 \| 35.8052 \| 0.0174 \| 0.0218 \| 0.0181 \| 0.0018 \| 0.0208 \| 0.0403 \| 0.1026 \| 0.3488 \| 0.4766 \| 0.2068 \| 0.3434 \| 0.5788 \| -1.0 \| -1.0 \| 0.002 \| 0.6 \| 0.0299 \| 0.5614 \| 0.0465 \| 0.4087 \| 0.0025 \| 0.4889 \| 0.0087 \| 0.6963 \| 0.0043 \| 0.3727 \| 0.0275 \| 0.2081 \| \| No log \| 4.0 \| 140 \| 25.0458 \| 0.1381 \| 0.1523 \| 0.1431 \| 0.0303 \| 0.1563 \| 0.182 \| 0.3056 \| 0.7303 \| 0.8222 \| 0.3066 \| 0.7579 \| 0.9116 \| -1.0 \| -1.0 \| 0.0486 \| 0.99 \| 0.1396 \| 0.8114 \| 0.5183 \| 0.9478 \| 0.0069 \| 0.6519 \| 0.1362 \| 0.8556 \| 0.0466 \| 0.9909 \| 0.0704 \| 0.5081 \| \| No log \| 5.0 \| 175 \| 17.7820 \| 0.3449 \| 0.3761 \| 0.3616 \| 0.1626 \| 0.3378 \| 0.4356 \| 0.4732 \| 0.8209 \| 0.8887 \| 0.5946 \| 0.834 \| 0.9595 \| -1.0 \| -1.0 \| 0.049 \| 0.99 \| 0.5783 \| 0.8307 \| 0.7758 \| 0.9507 \| 0.0242 \| 0.8519 \| 0.3644 \| 0.8889 \| 0.4966 \| 0.9909 \| 0.1259 \| 0.7176 \| \| No log \| 6.0 \| 210 \| 12.9410 \| 0.5642 \| 0.6087 \| 0.5938 \| 0.2533 \| 0.4341 \| 0.6233 \| 0.5662 \| 0.8824 \| 0.9137 \| 0.6323 \| 0.8828 \| 0.9676 \| -1.0 \| -1.0 \| 0.522 \| 0.99 \| 0.5584 \| 0.8205 \| 0.8377 \| 0.9754 \| 0.2365 \| 0.8889 \| 0.6276 \| 0.9 \| 0.907 \| 0.9818 \| 0.2599 \| 0.8392 \| \| No log \| 7.0 \| 245 \| 10.9532 \| 0.621 \| 0.6648 \| 0.6501 \| 0.2326 \| 0.5144 \| 0.7328 \| 0.557 \| 0.8817 \| 0.9294 \| 0.7018 \| 0.9104 \| 0.9762 \| -1.0 \| -1.0 \| 0.8543 \| 0.99 \| 0.7325 \| 0.8534 \| 0.8209 \| 0.9783 \| 0.1195 \| 0.9148 \| 0.4129 \| 0.9222 \| 0.8795 \| 0.9727 \| 0.527 \| 0.8743 \| \| No log \| 8.0 \| 280 \| 10.0540 \| 0.5983 \| 0.6382 \| 0.6263 \| 0.2675 \| 0.4634 \| 0.7109 \| 0.5338 \| 0.8619 \| 0.8908 \| 0.7047 \| 0.8926 \| 0.9389 \| -1.0 \| -1.0 \| 0.6275 \| 0.99 \| 0.7514 \| 0.8477 \| 0.8526 \| 0.9739 \| 0.3547 \| 0.937 \| 0.5777 \| 0.9185 \| 0.5453 \| 0.7091 \| 0.4788 \| 0.8595 \| \| No log \| 9.0 \| 315 \| 9.1369 \| 0.7135 \| 0.7584 \| 0.7387 \| 0.3085 \| 0.5685 \| 0.8728 \| 0.602 \| 0.9018 \| 0.9292 \| 0.708 \| 0.7784 \| 0.9823 \| -1.0 \| -1.0 \| 0.9209 \| 0.99 \| 0.7616 \| 0.833 \| 0.8367 \| 0.9681 \| 0.258 \| 0.9037 \| 0.7433 \| 0.9296 \| 0.9341 \| 0.9909 \| 0.54 \| 0.8892 \| \| No log \| 10.0 \| 350 \| 8.3330 \| 0.7455 \| 0.7925 \| 0.7791 \| 0.3892 \| 0.5906 \| 0.8393 \| 0.6105 \| 0.9109 \| 0.9418 \| 0.7521 \| 0.8131 \| 0.9867 \| -1.0 \| -1.0 \| 0.8772 \| 0.99 \| 0.7901 \| 0.8602 \| 0.8873 \| 0.9797 \| 0.4403 \| 0.9407 \| 0.6274 \| 0.9259 \| 0.884 \| 0.9909 \| 0.7124 \| 0.9054 \| \| No log \| 11.0 \| 385 \| 7.8064 \| 0.7386 \| 0.7829 \| 0.7659 \| 0.386 \| 0.5855 \| 0.8299 \| 0.598 \| 0.9136 \| 0.9447 \| 0.7716 \| 0.8992 \| 0.9875 \| -1.0 \| -1.0 \| 0.9646 \| 0.99 \| 0.797 \| 0.8773 \| 0.8289 \| 0.9739 \| 0.2469 \| 0.9593 \| 0.6911 \| 0.9148 \| 0.9411 \| 0.9909 \| 0.7008 \| 0.9068 \| \| No log \| 12.0 \| 420 \| 7.3854 \| 0.7433 \| 0.7883 \| 0.7675 \| 0.4603 \| 0.5849 \| 0.8471 \| 0.6029 \| 0.9147 \| 0.9389 \| 0.7745 \| 0.8155 \| 0.9831 \| -1.0 \| -1.0 \| 0.9646 \| 0.99 \| 0.8199 \| 0.8602 \| 0.7835 \| 0.9754 \| 0.2672 \| 0.9222 \| 0.6356 \| 0.9296 \| 0.9685 \| 0.9909 \| 0.7639 \| 0.9041 \| \| No log \| 13.0 \| 455 \| 7.1834 \| 0.7809 \| 0.8401 \| 0.7988 \| 0.4502 \| 0.6114 \| 0.8633 \| 0.6232 \| 0.9163 \| 0.9319 \| 0.7806 \| 0.814 \| 0.9751 \| -1.0 \| -1.0 \| 0.9495 \| 0.99 \| 0.7535 \| 0.8625 \| 0.8276 \| 0.9725 \| 0.5165 \| 0.8963 \| 0.7709 \| 0.9259 \| 0.8929 \| 0.9818 \| 0.7556 \| 0.8946 \| \| No log \| 14.0 \| 490 \| 6.9868 \| 0.7827 \| 0.8428 \| 0.8038 \| 0.4806 \| 0.6204 \| 0.8808 \| 0.6146 \| 0.918 \| 0.9335 \| 0.7759 \| 0.7932 \| 0.9763 \| -1.0 \| -1.0 \| 0.8188 \| 0.99 \| 0.8202 \| 0.8727 \| 0.8257 \| 0.9551 \| 0.5485 \| 0.9074 \| 0.7324 \| 0.9222 \| 0.9589 \| 0.9909 \| 0.7743 \| 0.8959 \| \| 32.4583 \| 15.0 \| 525 \| 6.9190 \| 0.7805 \| 0.8338 \| 0.8066 \| 0.4936 \| 0.595 \| 0.8772 \| 0.6148 \| 0.9272 \| 0.9467 \| 0.7756 \| 0.9095 \| 0.9864 \| -1.0 \| -1.0 \| 0.912 \| 0.99 \| 0.7913 \| 0.8557 \| 0.8613 \| 0.9855 \| 0.5283 \| 0.963 \| 0.6932 \| 0.9296 \| 0.8962 \| 0.9909 \| 0.7809 \| 0.9122 \| \| 32.4583 \| 16.0 \| 560 \| 7.0608 \| 0.7653 \| 0.8279 \| 0.7837 \| 0.5397 \| 0.5975 \| 0.838 \| 0.6004 \| 0.9049 \| 0.9226 \| 0.7743 \| 0.8751 \| 0.9682 \| -1.0 \| -1.0 \| 0.9569 \| 0.99 \| 0.7921 \| 0.8455 \| 0.7774 \| 0.9174 \| 0.4303 \| 0.9 \| 0.6478 \| 0.9185 \| 0.9515 \| 0.9909 \| 0.8008 \| 0.8959 \| \| 32.4583 \| 17.0 \| 595 \| 6.5305 \| 0.7648 \| 0.8275 \| 0.7858 \| 0.4759 \| 0.6282 \| 0.866 \| 0.6017 \| 0.919 \| 0.9327 \| 0.7467 \| 0.8837 \| 0.977 \| -1.0 \| -1.0 \| 0.982 \| 0.99 \| 0.8007 \| 0.8648 \| 0.8692 \| 0.971 \| 0.2549 \| 0.9296 \| 0.7184 \| 0.9 \| 0.9133 \| 0.9818 \| 0.8148 \| 0.8919 \| \| 32.4583 \| 18.0 \| 630 \| 6.3597 \| 0.7757 \| 0.837 \| 0.8076 \| 0.5064 \| 0.6482 \| 0.863 \| 0.5974 \| 0.9229 \| 0.9362 \| 0.7693 \| 0.8368 \| 0.9779 \| -1.0 \| -1.0 \| 0.8771 \| 1.0 \| 0.8099 \| 0.867 \| 0.9032 \| 0.9696 \| 0.2876 \| 0.9222 \| 0.7655 \| 0.9074 \| 0.9522 \| 0.9818 \| 0.8346 \| 0.9054 \| \| 32.4583 \| 19.0 \| 665 \| 6.2977 \| 0.8016 \| 0.8679 \| 0.8317 \| 0.5793 \| 0.6589 \| 0.8646 \| 0.6006 \| 0.9256 \| 0.9403 \| 0.7879 \| 0.9265 \| 0.9749 \| -1.0 \| -1.0 \| 0.9534 \| 0.99 \| 0.8131 \| 0.8739 \| 0.9071 \| 0.9725 \| 0.4297 \| 0.9407 \| 0.7494 \| 0.9185 \| 0.9567 \| 0.9909 \| 0.8015 \| 0.8959 \| \| 32.4583 \| 20.0 \| 700 \| 6.2690 \| 0.7843 \| 0.8443 \| 0.8135 \| 0.5068 \| 0.6505 \| 0.887 \| 0.6166 \| 0.9212 \| 0.9362 \| 0.7759 \| 0.8952 \| 0.9767 \| -1.0 \| -1.0 \| 0.9725 \| 0.99 \| 0.8173 \| 0.8739 \| 0.8936 \| 0.9812 \| 0.3364 \| 0.9296 \| 0.7669 \| 0.9037 \| 0.9347 \| 0.9818 \| 0.7686 \| 0.8932 \| \| 32.4583 \| 21.0 \| 735 \| 6.3902 \| 0.8137 \| 0.8806 \| 0.8417 \| 0.7055 \| 0.6578 \| 0.8776 \| 0.6087 \| 0.9276 \| 0.9445 \| 0.7868 \| 0.9097 \| 0.9828 \| -1.0 \| -1.0 \| 0.9807 \| 0.99 \| 0.8096 \| 0.8739 \| 0.9099 \| 0.9696 \| 0.5614 \| 0.9741 \| 0.6762 \| 0.9074 \| 0.9398 \| 0.9909 \| 0.8186 \| 0.9054 \| \| 32.4583 \| 22.0 \| 770 \| 6.5328 \| 0.8196 \| 0.8927 \| 0.8492 \| 0.5932 \| 0.626 \| 0.876 \| 0.6229 \| 0.9181 \| 0.9309 \| 0.785 \| 0.8935 \| 0.9673 \| -1.0 \| -1.0 \| 0.9881 \| 0.99 \| 0.8162 \| 0.8705 \| 0.8924 \| 0.9638 \| 0.6492 \| 0.9333 \| 0.7485 \| 0.8704 \| 0.8867 \| 0.9818 \| 0.7563 \| 0.9068 \| \| 32.4583 \| 23.0 \| 805 \| 6.2433 \| 0.8291 \| 0.8944 \| 0.8509 \| 0.5805 \| 0.6343 \| 0.9025 \| 0.64 \| 0.9191 \| 0.9392 \| 0.8098 \| 0.8845 \| 0.981 \| -1.0 \| -1.0 \| 1.0 \| 1.0 \| 0.8052 \| 0.8705 \| 0.9029 \| 0.9623 \| 0.5827 \| 0.9333 \| 0.7635 \| 0.9222 \| 0.9357 \| 0.9818 \| 0.8135 \| 0.9041 \| \| 32.4583 \| 24.0 \| 840 \| 6.1554 \| 0.8171 \| 0.885 \| 0.8449 \| 0.6088 \| 0.6328 \| 0.8923 \| 0.6174 \| 0.9203 \| 0.9316 \| 0.7746 \| 0.782 \| 0.976 \| -1.0 \| -1.0 \| 1.0 \| 1.0 \| 0.8049 \| 0.8727 \| 0.878 \| 0.9667 \| 0.5197 \| 0.9148 \| 0.75 \| 0.8889 \| 0.9508 \| 0.9818 \| 0.8159 \| 0.8959 \| \| 32.4583 \| 25.0 \| 875 \| 5.9349 \| 0.8258 \| 0.8844 \| 0.8498 \| 0.6153 \| 0.6481 \| 0.8945 \| 0.6112 \| 0.9268 \| 0.9361 \| 0.7883 \| 0.7972 \| 0.9813 \| -1.0 \| -1.0 \| 1.0 \| 1.0 \| 0.8164 \| 0.8795 \| 0.9086 \| 0.9565 \| 0.4921 \| 0.9185 \| 0.7741 \| 0.9185 \| 0.9465 \| 0.9727 \| 0.8429 \| 0.9068 \| \| 32.4583 \| 26.0 \| 910 \| 6.3160 \| 0.7895 \| 0.8497 \| 0.8183 \| 0.5915 \| 0.6343 \| 0.8686 \| 0.6264 \| 0.9223 \| 0.9318 \| 0.7793 \| 0.8842 \| 0.9716 \| -1.0 \| -1.0 \| 0.9497 \| 0.99 \| 0.7948 \| 0.867 \| 0.859 \| 0.9493 \| 0.3445 \| 0.9333 \| 0.7677 \| 0.9 \| 0.968 \| 0.9818 \| 0.8428 \| 0.9014 \| \| 32.4583 \| 27.0 \| 945 \| 6.4135 \| 0.8095 \| 0.8772 \| 0.838 \| 0.5667 \| 0.6294 \| 0.876 \| 0.6148 \| 0.9118 \| 0.9303 \| 0.7603 \| 0.876 \| 0.9693 \| -1.0 \| -1.0 \| 0.9317 \| 0.98 \| 0.8026 \| 0.8648 \| 0.8146 \| 0.9246 \| 0.5762 \| 0.9481 \| 0.7626 \| 0.9111 \| 0.9675 \| 0.9818 \| 0.8115 \| 0.9014 \| \| 32.4583 \| 28.0 \| 980 \| 5.8669 \| 0.8351 \| 0.8972 \| 0.8582 \| 0.6038 \| 0.6358 \| 0.8918 \| 0.6464 \| 0.928 \| 0.9365 \| 0.795 \| 0.8915 \| 0.9783 \| -1.0 \| -1.0 \| 0.9752 \| 0.99 \| 0.821 \| 0.8739 \| 0.8749 \| 0.9754 \| 0.6426 \| 0.9444 \| 0.7537 \| 0.8741 \| 0.9634 \| 0.9909 \| 0.8152 \| 0.9068 \| \| 5.1898 \| 29.0 \| 1015 \| 5.9247 \| 0.86 \| 0.9246 \| 0.882 \| 0.6164 \| 0.6296 \| 0.9322 \| 0.6408 \| 0.9206 \| 0.9326 \| 0.7759 \| 0.8898 \| 0.9781 \| -1.0 \| -1.0 \| 0.991 \| 1.0 \| 0.8174 \| 0.8636 \| 0.8344 \| 0.9174 \| 0.7097 \| 0.9333 \| 0.8459 \| 0.9259 \| 0.9812 \| 0.9909 \| 0.8404 \| 0.8973 \| \| 5.1898 \| 30.0 \| 1050 \| 5.9808 \| 0.8348 \| 0.9006 \| 0.8552 \| 0.5928 \| 0.6299 \| 0.8899 \| 0.6409 \| 0.9203 \| 0.928 \| 0.7853 \| 0.7907 \| 0.9711 \| -1.0 \| -1.0 \| 0.9901 \| 0.99 \| 0.8064 \| 0.8693 \| 0.8471 \| 0.9449 \| 0.7043 \| 0.9222 \| 0.7611 \| 0.9 \| 0.9621 \| 0.9818 \| 0.7727 \| 0.8878 \| \| 5.1898 \| 31.0 \| 1085 \| 5.9738 \| 0.8511 \| 0.9076 \| 0.8774 \| 0.5968 \| 0.6397 \| 0.9095 \| 0.644 \| 0.9194 \| 0.9335 \| 0.7835 \| 0.8017 \| 0.9779 \| -1.0 \| -1.0 \| 0.9835 \| 1.0 \| 0.8134 \| 0.8739 \| 0.8661 \| 0.9362 \| 0.744 \| 0.9222 \| 0.7586 \| 0.9296 \| 0.9647 \| 0.9727 \| 0.8277 \| 0.9 \| \| 5.1898 \| 32.0 \| 1120 \| 6.0519 \| 0.8567 \| 0.9245 \| 0.8833 \| 0.6084 \| 0.6568 \| 0.9083 \| 0.6565 \| 0.9215 \| 0.9332 \| 0.7877 \| 0.8841 \| 0.9733 \| -1.0 \| -1.0 \| 0.982 \| 0.99 \| 0.8148 \| 0.8682 \| 0.8808 \| 0.9681 \| 0.7649 \| 0.9333 \| 0.7794 \| 0.8926 \| 0.9557 \| 0.9818 \| 0.8196 \| 0.8986 \| \| 5.1898 \| 33.0 \| 1155 \| 5.8607 \| 0.8556 \| 0.9226 \| 0.8835 \| 0.6277 \| 0.6905 \| 0.9001 \| 0.6521 \| 0.926 \| 0.9366 \| 0.7708 \| 0.9035 \| 0.9755 \| -1.0 \| -1.0 \| 1.0 \| 1.0 \| 0.7983 \| 0.8693 \| 0.8764 \| 0.9609 \| 0.7527 \| 0.9444 \| 0.7659 \| 0.8889 \| 0.9675 \| 0.9818 \| 0.8285 \| 0.9108 \| \| 5.1898 \| 34.0 \| 1190 \| 5.7650 \| 0.851 \| 0.9163 \| 0.8812 \| 0.5894 \| 0.6515 \| 0.9045 \| 0.6564 \| 0.9253 \| 0.9367 \| 0.7932 \| 0.8781 \| 0.9762 \| -1.0 \| -1.0 \| 1.0 \| 1.0 \| 0.7983 \| 0.875 \| 0.8831 \| 0.9609 \| 0.667 \| 0.9444 \| 0.7993 \| 0.8852 \| 0.9675 \| 0.9818 \| 0.842 \| 0.9095 \| \| 5.1898 \| 35.0 \| 1225 \| 5.6566 \| 0.8525 \| 0.9147 \| 0.8784 \| 0.5964 \| 0.6244 \| 0.905 \| 0.6486 \| 0.9316 \| 0.9414 \| 0.7726 \| 0.9147 \| 0.9757 \| -1.0 \| -1.0 \| 0.9772 \| 1.0 \| 0.8076 \| 0.8682 \| 0.9067 \| 0.9667 \| 0.6843 \| 0.9407 \| 0.7884 \| 0.9074 \| 0.9812 \| 0.9909 \| 0.8225 \| 0.9162 \| \| 5.1898 \| 36.0 \| 1260 \| 5.7578 \| 0.8626 \| 0.9252 \| 0.89 \| 0.6101 \| 0.6392 \| 0.9241 \| 0.6555 \| 0.9323 \| 0.9426 \| 0.785 \| 0.9145 \| 0.9808 \| -1.0 \| -1.0 \| 0.9835 \| 1.0 \| 0.8056 \| 0.8636 \| 0.8808 \| 0.9696 \| 0.7216 \| 0.963 \| 0.8373 \| 0.9074 \| 0.9812 \| 0.9909 \| 0.8284 \| 0.9041 \| \| 5.1898 \| 37.0 \| 1295 \| 5.7752 \| 0.8529 \| 0.9132 \| 0.8854 \| 0.6004 \| 0.6418 \| 0.9072 \| 0.6458 \| 0.9251 \| 0.9412 \| 0.7632 \| 0.9092 \| 0.9834 \| -1.0 \| -1.0 \| 0.9772 \| 1.0 \| 0.8062 \| 0.8693 \| 0.8795 \| 0.9594 \| 0.7218 \| 0.9407 \| 0.7752 \| 0.9222 \| 0.9652 \| 0.9818 \| 0.8453 \| 0.9149 \| \| 5.1898 \| 38.0 \| 1330 \| 5.8527 \| 0.854 \| 0.9169 \| 0.8842 \| 0.6171 \| 0.6414 \| 0.9136 \| 0.6539 \| 0.9288 \| 0.9456 \| 0.7921 \| 0.9069 \| 0.9876 \| -1.0 \| -1.0 \| 0.991 \| 1.0 \| 0.8115 \| 0.8773 \| 0.8773 \| 0.9652 \| 0.6804 \| 0.9667 \| 0.8167 \| 0.9185 \| 0.9651 \| 0.9818 \| 0.8358 \| 0.9095 \| \| 5.1898 \| 39.0 \| 1365 \| 5.6796 \| 0.864 \| 0.9314 \| 0.8903 \| 0.6112 \| 0.6373 \| 0.9218 \| 0.6476 \| 0.9308 \| 0.9413 \| 0.7787 \| 0.9145 \| 0.9782 \| -1.0 \| -1.0 \| 1.0 \| 1.0 \| 0.8099 \| 0.8705 \| 0.9009 \| 0.9652 \| 0.706 \| 0.9556 \| 0.8127 \| 0.8963 \| 0.9812 \| 0.9909 \| 0.8375 \| 0.9108 \| \| 5.1898 \| 40.0 \| 1400 \| 5.6425 \| 0.8536 \| 0.9205 \| 0.8835 \| 0.6029 \| 0.6507 \| 0.9146 \| 0.6521 \| 0.9329 \| 0.9418 \| 0.7882 \| 0.9185 \| 0.9792 \| -1.0 \| -1.0 \| 0.9772 \| 1.0 \| 0.8058 \| 0.8716 \| 0.909 \| 0.9696 \| 0.6723 \| 0.9593 \| 0.7884 \| 0.9074 \| 0.9647 \| 0.9727 \| 0.8578 \| 0.9122 \| \| 5.1898 \| 41.0 \| 1435 \| 5.6851 \| 0.8526 \| 0.9164 \| 0.8808 \| 0.6038 \| 0.6471 \| 0.916 \| 0.6569 \| 0.9325 \| 0.9399 \| 0.7945 \| 0.9122 \| 0.9793 \| -1.0 \| -1.0 \| 1.0 \| 1.0 \| 0.8085 \| 0.875 \| 0.923 \| 0.9754 \| 0.6399 \| 0.9407 \| 0.7996 \| 0.9037 \| 0.9738 \| 0.9818 \| 0.8236 \| 0.9027 \| \| 5.1898 \| 42.0 \| 1470 \| 5.6092 \| 0.864 \| 0.9291 \| 0.893 \| 0.6087 \| 0.6473 \| 0.9309 \| 0.652 \| 0.9334 \| 0.9418 \| 0.7832 \| 0.9139 \| 0.9805 \| -1.0 \| -1.0 \| 1.0 \| 1.0 \| 0.8058 \| 0.8705 \| 0.9203 \| 0.9681 \| 0.6728 \| 0.9667 \| 0.8363 \| 0.9 \| 0.9647 \| 0.9727 \| 0.8484 \| 0.9149 \| \| 4.0823 \| 43.0 \| 1505 \| 5.4824 \| 0.8709 \| 0.9372 \| 0.8972 \| 0.6011 \| 0.6472 \| 0.937 \| 0.6562 \| 0.9342 \| 0.9434 \| 0.7777 \| 0.9143 \| 0.9847 \| -1.0 \| -1.0 \| 1.0 \| 1.0 \| 0.8119 \| 0.8705 \| 0.9112 \| 0.9652 \| 0.721 \| 0.963 \| 0.8411 \| 0.9222 \| 0.9652 \| 0.9818 \| 0.846 \| 0.9014 \| \| 4.0823 \| 44.0 \| 1540 \| 5.5914 \| 0.8642 \| 0.9316 \| 0.8912 \| 0.6061 \| 0.6487 \| 0.9302 \| 0.6586 \| 0.9353 \| 0.9424 \| 0.7779 \| 0.9139 \| 0.9826 \| -1.0 \| -1.0 \| 0.991 \| 1.0 \| 0.8078 \| 0.8693 \| 0.9284 \| 0.971 \| 0.6971 \| 0.9667 \| 0.8299 \| 0.9037 \| 0.9654 \| 0.9818 \| 0.8299 \| 0.9041 \| \| 4.0823 \| 45.0 \| 1575 \| 5.6331 \| 0.86 \| 0.9296 \| 0.8902 \| 0.6114 \| 0.6418 \| 0.9152 \| 0.6456 \| 0.9336 \| 0.9419 \| 0.7856 \| 0.9124 \| 0.9809 \| -1.0 \| -1.0 \| 1.0 \| 1.0 \| 0.8105 \| 0.8773 \| 0.9199 \| 0.9696 \| 0.7093 \| 0.9593 \| 0.7953 \| 0.9 \| 0.9653 \| 0.9818 \| 0.82 \| 0.9054 \| \| 4.0823 \| 46.0 \| 1610 \| 5.6049 \| 0.8607 \| 0.9295 \| 0.889 \| 0.6164 \| 0.6419 \| 0.9181 \| 0.6484 \| 0.9316 \| 0.9404 \| 0.7568 \| 0.9149 \| 0.9799 \| -1.0 \| -1.0 \| 0.991 \| 1.0 \| 0.8076 \| 0.8716 \| 0.9103 \| 0.971 \| 0.7084 \| 0.9556 \| 0.8046 \| 0.9 \| 0.9647 \| 0.9727 \| 0.838 \| 0.9122 \| \| 4.0823 \| 47.0 \| 1645 \| 5.6549 \| 0.8492 \| 0.9148 \| 0.8762 \| 0.6114 \| 0.6449 \| 0.9051 \| 0.6431 \| 0.9325 \| 0.9427 \| 0.7934 \| 0.9172 \| 0.9801 \| -1.0 \| -1.0 \| 0.9835 \| 1.0 \| 0.8064 \| 0.8773 \| 0.9207 \| 0.9667 \| 0.6572 \| 0.963 \| 0.7851 \| 0.9074 \| 0.9647 \| 0.9727 \| 0.8268 \| 0.9122 \| \| 4.0823 \| 48.0 \| 1680 \| 5.5983 \| 0.858 \| 0.926 \| 0.8891 \| 0.6136 \| 0.6419 \| 0.9136 \| 0.6499 \| 0.9324 \| 0.9438 \| 0.7845 \| 0.9192 \| 0.9812 \| -1.0 \| -1.0 \| 0.9835 \| 1.0 \| 0.8059 \| 0.8739 \| 0.9212 \| 0.971 \| 0.7215 \| 0.963 \| 0.773 \| 0.9037 \| 0.9589 \| 0.9818 \| 0.8419 \| 0.9135 \| \| 4.0823 \| 49.0 \| 1715 \| 5.6368 \| 0.8556 \| 0.9223 \| 0.8844 \| 0.6091 \| 0.6486 \| 0.9069 \| 0.6488 \| 0.9349 \| 0.9438 \| 0.7895 \| 0.9236 \| 0.979 \| -1.0 \| -1.0 \| 0.991 \| 1.0 \| 0.81 \| 0.8739 \| 0.9161 \| 0.9696 \| 0.7073 \| 0.963 \| 0.7632 \| 0.9074 \| 0.9577 \| 0.9727 \| 0.8439 \| 0.9203 \| \| 4.0823 \| 50.0 \| 1750 \| 5.6852 \| 0.8547 \| 0.9219 \| 0.8855 \| 0.6003 \| 0.6465 \| 0.9074 \| 0.652 \| 0.9364 \| 0.9452 \| 0.779 \| 0.924 \| 0.9818 \| -1.0 \| -1.0 \| 1.0 \| 1.0 \| 0.8084 \| 0.8727 \| 0.9022 \| 0.9681 \| 0.7068 \| 0.9667 \| 0.7732 \| 0.9111 \| 0.9573 \| 0.9818 \| 0.835 \| 0.9162 \| ### Framework versions - Transformers 4.44.0.dev0 - Pytorch 2.3.1+cu121 - Tokenizers 0.19.1	[ "evidence", "ambulance", "artificial_target", "cartridge", "gun", "knife", "police", "traffic" ]
trungphien/phien-table-straighten-detection	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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trungphien/phien-table-straighten-detection-130	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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trungphien/phien-table-straighten-detection-fixID	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mostafasmart/detr-resnet-50_finetuned_11	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "ca", "uv", "pt", "st", "t.nor" ]
MedicalVision/detr_nih_1ep	## Original result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.001 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.003 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.008 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.008 ``` ## After training result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.006 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.015 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.017 ``` ## Config - dataset: NIH - original model: facebook/detr-resnet-50 - lr: 0.0001 - max_epochs: 1 ## Logging ### Training process ``` {'training_loss': tensor(2.9624, device='cuda:0'), 'train_loss_ce': tensor(0.4469, device='cuda:0'), 'train_loss_bbox': tensor(0.2411, device='cuda:0'), 'train_loss_giou': tensor(0.6551, device='cuda:0'), 'train_cardinality_error': tensor(1.1250, device='cuda:0'), 'validation_loss': tensor(2.4818, device='cuda:0'), 'validation_loss_ce': tensor(0.5116, device='cuda:0'), 'validation_loss_bbox': tensor(0.1740, device='cuda:0'), 'validation_loss_giou': tensor(0.5502, device='cuda:0'), 'validation_cardinality_error': tensor(1.0955, device='cuda:0')} ``` ### Validation process ``` {'validation_loss': tensor(5.8176, device='cuda:0'), 'validation_loss_ce': tensor(2.3980, device='cuda:0'), 'validation_loss_bbox': tensor(0.4030, device='cuda:0'), 'validation_loss_giou': tensor(0.7024, device='cuda:0'), 'validation_cardinality_error': tensor(98.5312, device='cuda:0')} {'training_loss': tensor(2.9624, device='cuda:0'), 'train_loss_ce': tensor(0.4469, device='cuda:0'), 'train_loss_bbox': tensor(0.2411, device='cuda:0'), 'train_loss_giou': tensor(0.6551, device='cuda:0'), 'train_cardinality_error': tensor(1.1250, device='cuda:0'), 'validation_loss': tensor(2.4818, device='cuda:0'), 'validation_loss_ce': tensor(0.5116, device='cuda:0'), 'validation_loss_bbox': tensor(0.1740, device='cuda:0'), 'validation_loss_giou': tensor(0.5502, device='cuda:0'), 'validation_cardinality_error': tensor(1.0955, device='cuda:0')} ```	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7" ]
MedicalVision/detr_nih_10ep	## Original result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.001 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.002 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.004 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.004 ``` ## After training result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.003 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.011 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.001 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.003 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.037 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.074 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.084 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.006 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.085 ``` ## Config - dataset: NIH - original model: facebook/detr-resnet-50 - lr: 5e-05 - max_epochs: 10 ## Logging ### Training process ``` {'validation_loss': tensor(6.4663, device='cuda:0'), 'validation_loss_ce': tensor(2.5639, device='cuda:0'), 'validation_loss_bbox': tensor(0.4662, device='cuda:0'), 'validation_loss_giou': tensor(0.7858, device='cuda:0'), 'validation_cardinality_error': tensor(98.8125, device='cuda:0')} {'training_loss': tensor(4.3831, device='cuda:0'), 'train_loss_ce': tensor(1.5780, device='cuda:0'), 'train_loss_bbox': tensor(0.2546, device='cuda:0'), 'train_loss_giou': tensor(0.7661, device='cuda:0'), 'train_cardinality_error': tensor(3.7500, device='cuda:0'), 'validation_loss': tensor(4.1328, device='cuda:0'), 'validation_loss_ce': tensor(1.5373, device='cuda:0'), 'validation_loss_bbox': tensor(0.2405, device='cuda:0'), 'validation_loss_giou': tensor(0.6965, device='cuda:0'), 'validation_cardinality_error': tensor(1.4364, device='cuda:0')} {'training_loss': tensor(2.8787, device='cuda:0'), 'train_loss_ce': tensor(0.7759, device='cuda:0'), 'train_loss_bbox': tensor(0.1841, device='cuda:0'), 'train_loss_giou': tensor(0.5911, device='cuda:0'), 'train_cardinality_error': tensor(1.0625, device='cuda:0'), 'validation_loss': tensor(2.8971, device='cuda:0'), 'validation_loss_ce': tensor(0.7507, device='cuda:0'), 'validation_loss_bbox': tensor(0.1935, device='cuda:0'), 'validation_loss_giou': tensor(0.5893, device='cuda:0'), 'validation_cardinality_error': tensor(1.1159, device='cuda:0')} {'training_loss': tensor(2.8165, device='cuda:0'), 'train_loss_ce': tensor(0.5296, device='cuda:0'), 'train_loss_bbox': tensor(0.2043, device='cuda:0'), 'train_loss_giou': tensor(0.6328, device='cuda:0'), 'train_cardinality_error': tensor(1.0625, device='cuda:0'), 'validation_loss': tensor(2.6196, device='cuda:0'), 'validation_loss_ce': tensor(0.5727, device='cuda:0'), 'validation_loss_bbox': tensor(0.1831, device='cuda:0'), 'validation_loss_giou': tensor(0.5656, device='cuda:0'), 'validation_cardinality_error': tensor(1.1159, device='cuda:0')} {'training_loss': tensor(2.6336, device='cuda:0'), 'train_loss_ce': tensor(0.5603, device='cuda:0'), 'train_loss_bbox': tensor(0.1886, device='cuda:0'), 'train_loss_giou': tensor(0.5651, device='cuda:0'), 'train_cardinality_error': tensor(1.1875, device='cuda:0'), 'validation_loss': tensor(2.4110, device='cuda:0'), 'validation_loss_ce': tensor(0.5158, device='cuda:0'), 'validation_loss_bbox': tensor(0.1616, device='cuda:0'), 'validation_loss_giou': tensor(0.5437, device='cuda:0'), 'validation_cardinality_error': tensor(1.1159, device='cuda:0')} {'training_loss': tensor(2.3404, device='cuda:0'), 'train_loss_ce': tensor(0.4770, device='cuda:0'), 'train_loss_bbox': tensor(0.1684, device='cuda:0'), 'train_loss_giou': tensor(0.5106, device='cuda:0'), 'train_cardinality_error': tensor(1.2500, device='cuda:0'), 'validation_loss': tensor(2.3158, device='cuda:0'), 'validation_loss_ce': tensor(0.5015, device='cuda:0'), 'validation_loss_bbox': tensor(0.1579, device='cuda:0'), 'validation_loss_giou': tensor(0.5123, device='cuda:0'), 'validation_cardinality_error': tensor(1.1159, device='cuda:0')} {'training_loss': tensor(2.4181, device='cuda:0'), 'train_loss_ce': tensor(0.4818, device='cuda:0'), 'train_loss_bbox': tensor(0.1594, device='cuda:0'), 'train_loss_giou': tensor(0.5697, device='cuda:0'), 'train_cardinality_error': tensor(1.0625, device='cuda:0'), 'validation_loss': tensor(2.2529, device='cuda:0'), 'validation_loss_ce': tensor(0.4929, device='cuda:0'), 'validation_loss_bbox': tensor(0.1507, device='cuda:0'), 'validation_loss_giou': tensor(0.5034, device='cuda:0'), 'validation_cardinality_error': tensor(1.1159, device='cuda:0')} {'training_loss': tensor(2.3413, device='cuda:0'), 'train_loss_ce': tensor(0.6037, device='cuda:0'), 'train_loss_bbox': tensor(0.1422, device='cuda:0'), 'train_loss_giou': tensor(0.5133, device='cuda:0'), 'train_cardinality_error': tensor(1.3750, device='cuda:0'), 'validation_loss': tensor(2.2137, device='cuda:0'), 'validation_loss_ce': tensor(0.4902, device='cuda:0'), 'validation_loss_bbox': tensor(0.1481, device='cuda:0'), 'validation_loss_giou': tensor(0.4914, device='cuda:0'), 'validation_cardinality_error': tensor(1.1159, device='cuda:0')} {'training_loss': tensor(2.3693, device='cuda:0'), 'train_loss_ce': tensor(0.4641, device='cuda:0'), 'train_loss_bbox': tensor(0.1578, device='cuda:0'), 'train_loss_giou': tensor(0.5582, device='cuda:0'), 'train_cardinality_error': tensor(1.1250, device='cuda:0'), 'validation_loss': tensor(2.1577, device='cuda:0'), 'validation_loss_ce': tensor(0.4842, device='cuda:0'), 'validation_loss_bbox': tensor(0.1380, device='cuda:0'), 'validation_loss_giou': tensor(0.4917, device='cuda:0'), 'validation_cardinality_error': tensor(1.1159, device='cuda:0')} {'training_loss': tensor(2.0663, device='cuda:0'), 'train_loss_ce': tensor(0.4912, device='cuda:0'), 'train_loss_bbox': tensor(0.1322, device='cuda:0'), 'train_loss_giou': tensor(0.4571, device='cuda:0'), 'train_cardinality_error': tensor(1.1250, device='cuda:0'), 'validation_loss': tensor(2.1651, device='cuda:0'), 'validation_loss_ce': tensor(0.4833, device='cuda:0'), 'validation_loss_bbox': tensor(0.1443, device='cuda:0'), 'validation_loss_giou': tensor(0.4801, device='cuda:0'), 'validation_cardinality_error': tensor(1.1159, device='cuda:0')} {'training_loss': tensor(2.2925, device='cuda:0'), 'train_loss_ce': tensor(0.3871, device='cuda:0'), 'train_loss_bbox': tensor(0.1779, device='cuda:0'), 'train_loss_giou': tensor(0.5079, device='cuda:0'), 'train_cardinality_error': tensor(1.0625, device='cuda:0'), 'validation_loss': tensor(2.2612, device='cuda:0'), 'validation_loss_ce': tensor(0.4804, device='cuda:0'), 'validation_loss_bbox': tensor(0.1531, device='cuda:0'), 'validation_loss_giou': tensor(0.5077, device='cuda:0'), 'validation_cardinality_error': tensor(1.1159, device='cuda:0')} ```	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7" ]
MedicalVision/detr_nih_20ep	## Original result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.006 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.025 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.025 ``` ## After training result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.005 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.015 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.003 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.005 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.037 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.105 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.110 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.111 ``` ## Config - dataset: NIH - original model: facebook/detr-resnet-50 - lr: 0.0001 - max_epochs: 20 ## Logging ### Training process ``` {'validation_loss': tensor(6.3146, device='cuda:0'), 'validation_loss_ce': tensor(2.1177, device='cuda:0'), 'validation_loss_bbox': tensor(0.4698, device='cuda:0'), 'validation_loss_giou': tensor(0.9240, device='cuda:0'), 'validation_cardinality_error': tensor(92.7500, device='cuda:0')} {'training_loss': tensor(2.3670, device='cuda:0'), 'train_loss_ce': tensor(0.4749, device='cuda:0'), 'train_loss_bbox': tensor(0.1771, device='cuda:0'), 'train_loss_giou': tensor(0.5034, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.5731, device='cuda:0'), 'validation_loss_ce': tensor(0.4578, device='cuda:0'), 'validation_loss_bbox': tensor(0.1954, device='cuda:0'), 'validation_loss_giou': tensor(0.5691, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.8187, device='cuda:0'), 'train_loss_ce': tensor(0.4641, device='cuda:0'), 'train_loss_bbox': tensor(0.1463, device='cuda:0'), 'train_loss_giou': tensor(0.3115, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.3214, device='cuda:0'), 'validation_loss_ce': tensor(0.4453, device='cuda:0'), 'validation_loss_bbox': tensor(0.1656, device='cuda:0'), 'validation_loss_giou': tensor(0.5240, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.0658, device='cuda:0'), 'train_loss_ce': tensor(0.4764, device='cuda:0'), 'train_loss_bbox': tensor(0.1381, device='cuda:0'), 'train_loss_giou': tensor(0.4494, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.5311, device='cuda:0'), 'validation_loss_ce': tensor(0.4494, device='cuda:0'), 'validation_loss_bbox': tensor(0.1959, device='cuda:0'), 'validation_loss_giou': tensor(0.5512, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.2448, device='cuda:0'), 'train_loss_ce': tensor(0.4614, device='cuda:0'), 'train_loss_bbox': tensor(0.1400, device='cuda:0'), 'train_loss_giou': tensor(0.5416, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.4153, device='cuda:0'), 'validation_loss_ce': tensor(0.4393, device='cuda:0'), 'validation_loss_bbox': tensor(0.1699, device='cuda:0'), 'validation_loss_giou': tensor(0.5632, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.1852, device='cuda:0'), 'train_loss_ce': tensor(0.3962, device='cuda:0'), 'train_loss_bbox': tensor(0.1427, device='cuda:0'), 'train_loss_giou': tensor(0.5378, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.5502, device='cuda:0'), 'validation_loss_ce': tensor(0.4350, device='cuda:0'), 'validation_loss_bbox': tensor(0.1922, device='cuda:0'), 'validation_loss_giou': tensor(0.5771, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.3992, device='cuda:0'), 'train_loss_ce': tensor(0.4620, device='cuda:0'), 'train_loss_bbox': tensor(0.1689, device='cuda:0'), 'train_loss_giou': tensor(0.5463, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.3235, device='cuda:0'), 'validation_loss_ce': tensor(0.4311, device='cuda:0'), 'validation_loss_bbox': tensor(0.1645, device='cuda:0'), 'validation_loss_giou': tensor(0.5349, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(3.3016, device='cuda:0'), 'train_loss_ce': tensor(0.4607, device='cuda:0'), 'train_loss_bbox': tensor(0.2840, device='cuda:0'), 'train_loss_giou': tensor(0.7105, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(3.1183, device='cuda:0'), 'validation_loss_ce': tensor(0.4855, device='cuda:0'), 'validation_loss_bbox': tensor(0.2575, device='cuda:0'), 'validation_loss_giou': tensor(0.6727, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.2759, device='cuda:0'), 'train_loss_ce': tensor(0.4727, device='cuda:0'), 'train_loss_bbox': tensor(0.1564, device='cuda:0'), 'train_loss_giou': tensor(0.5106, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.4930, device='cuda:0'), 'validation_loss_ce': tensor(0.4559, device='cuda:0'), 'validation_loss_bbox': tensor(0.1806, device='cuda:0'), 'validation_loss_giou': tensor(0.5671, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.1693, device='cuda:0'), 'train_loss_ce': tensor(0.4572, device='cuda:0'), 'train_loss_bbox': tensor(0.1668, device='cuda:0'), 'train_loss_giou': tensor(0.4391, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.4470, device='cuda:0'), 'validation_loss_ce': tensor(0.4276, device='cuda:0'), 'validation_loss_bbox': tensor(0.1828, device='cuda:0'), 'validation_loss_giou': tensor(0.5526, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.1987, device='cuda:0'), 'train_loss_ce': tensor(0.4733, device='cuda:0'), 'train_loss_bbox': tensor(0.1611, device='cuda:0'), 'train_loss_giou': tensor(0.4599, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.3597, device='cuda:0'), 'validation_loss_ce': tensor(0.4294, device='cuda:0'), 'validation_loss_bbox': tensor(0.1693, device='cuda:0'), 'validation_loss_giou': tensor(0.5418, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.5232, device='cuda:0'), 'train_loss_ce': tensor(0.4011, device='cuda:0'), 'train_loss_bbox': tensor(0.1782, device='cuda:0'), 'train_loss_giou': tensor(0.6154, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.2632, device='cuda:0'), 'validation_loss_ce': tensor(0.4187, device='cuda:0'), 'validation_loss_bbox': tensor(0.1576, device='cuda:0'), 'validation_loss_giou': tensor(0.5281, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.6633, device='cuda:0'), 'train_loss_ce': tensor(0.4540, device='cuda:0'), 'train_loss_bbox': tensor(0.1967, device='cuda:0'), 'train_loss_giou': tensor(0.6129, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.3457, device='cuda:0'), 'validation_loss_ce': tensor(0.4227, device='cuda:0'), 'validation_loss_bbox': tensor(0.1699, device='cuda:0'), 'validation_loss_giou': tensor(0.5369, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.4428, device='cuda:0'), 'train_loss_ce': tensor(0.4721, device='cuda:0'), 'train_loss_bbox': tensor(0.1833, device='cuda:0'), 'train_loss_giou': tensor(0.5272, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.5712, device='cuda:0'), 'validation_loss_ce': tensor(0.4430, device='cuda:0'), 'validation_loss_bbox': tensor(0.1854, device='cuda:0'), 'validation_loss_giou': tensor(0.6006, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.2497, device='cuda:0'), 'train_loss_ce': tensor(0.4085, device='cuda:0'), 'train_loss_bbox': tensor(0.1480, device='cuda:0'), 'train_loss_giou': tensor(0.5506, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.2828, device='cuda:0'), 'validation_loss_ce': tensor(0.4168, device='cuda:0'), 'validation_loss_bbox': tensor(0.1639, device='cuda:0'), 'validation_loss_giou': tensor(0.5232, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.3250, device='cuda:0'), 'train_loss_ce': tensor(0.4841, device='cuda:0'), 'train_loss_bbox': tensor(0.1521, device='cuda:0'), 'train_loss_giou': tensor(0.5402, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.2129, device='cuda:0'), 'validation_loss_ce': tensor(0.4072, device='cuda:0'), 'validation_loss_bbox': tensor(0.1600, device='cuda:0'), 'validation_loss_giou': tensor(0.5028, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.6468, device='cuda:0'), 'train_loss_ce': tensor(0.4126, device='cuda:0'), 'train_loss_bbox': tensor(0.1863, device='cuda:0'), 'train_loss_giou': tensor(0.6512, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.1853, device='cuda:0'), 'validation_loss_ce': tensor(0.4065, device='cuda:0'), 'validation_loss_bbox': tensor(0.1541, device='cuda:0'), 'validation_loss_giou': tensor(0.5041, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(3.1960, device='cuda:0'), 'train_loss_ce': tensor(0.3919, device='cuda:0'), 'train_loss_bbox': tensor(0.2759, device='cuda:0'), 'train_loss_giou': tensor(0.7123, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(4.5836, device='cuda:0'), 'validation_loss_ce': tensor(0.4463, device='cuda:0'), 'validation_loss_bbox': tensor(0.4188, device='cuda:0'), 'validation_loss_giou': tensor(1.0216, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.6624, device='cuda:0'), 'train_loss_ce': tensor(0.4196, device='cuda:0'), 'train_loss_bbox': tensor(0.1069, device='cuda:0'), 'train_loss_giou': tensor(0.3542, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.3143, device='cuda:0'), 'validation_loss_ce': tensor(0.4098, device='cuda:0'), 'validation_loss_bbox': tensor(0.1679, device='cuda:0'), 'validation_loss_giou': tensor(0.5325, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.6003, device='cuda:0'), 'train_loss_ce': tensor(0.3036, device='cuda:0'), 'train_loss_bbox': tensor(0.1298, device='cuda:0'), 'train_loss_giou': tensor(0.3238, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.1774, device='cuda:0'), 'validation_loss_ce': tensor(0.4031, device='cuda:0'), 'validation_loss_bbox': tensor(0.1508, device='cuda:0'), 'validation_loss_giou': tensor(0.5101, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.9200, device='cuda:0'), 'train_loss_ce': tensor(0.4339, device='cuda:0'), 'train_loss_bbox': tensor(0.1293, device='cuda:0'), 'train_loss_giou': tensor(0.4198, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.2109, device='cuda:0'), 'validation_loss_ce': tensor(0.4018, device='cuda:0'), 'validation_loss_bbox': tensor(0.1551, device='cuda:0'), 'validation_loss_giou': tensor(0.5167, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} ```	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7" ]
MedicalVision/detr_nih_50ep	## Original result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.000 ``` ## After training result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.004 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.014 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.001 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.004 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.048 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.123 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.138 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.139 ``` ## Config - dataset: NIH - original model: facebook/detr-resnet-50 - lr: 0.0001 - max_epochs: 50 ## Logging ### Training process ``` {'validation_loss': tensor(6.7355, device='cuda:0'), 'validation_loss_ce': tensor(2.3414, device='cuda:0'), 'validation_loss_bbox': tensor(0.5120, device='cuda:0'), 'validation_loss_giou': tensor(0.9169, device='cuda:0'), 'validation_cardinality_error': tensor(99., device='cuda:0')} {'training_loss': tensor(4.2989, device='cuda:0'), 'train_loss_ce': tensor(0.9732, device='cuda:0'), 'train_loss_bbox': tensor(0.3333, device='cuda:0'), 'train_loss_giou': tensor(0.8296, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(3.9485, device='cuda:0'), 'validation_loss_ce': tensor(0.9262, device='cuda:0'), 'validation_loss_bbox': tensor(0.3221, device='cuda:0'), 'validation_loss_giou': tensor(0.7058, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.7515, device='cuda:0'), 'train_loss_ce': 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'validation_loss_giou': tensor(0.5236, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.8662, device='cuda:0'), 'train_loss_ce': tensor(0.3441, device='cuda:0'), 'train_loss_bbox': tensor(0.1282, device='cuda:0'), 'train_loss_giou': tensor(0.4406, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.1352, device='cuda:0'), 'validation_loss_ce': tensor(0.3726, device='cuda:0'), 'validation_loss_bbox': tensor(0.1454, device='cuda:0'), 'validation_loss_giou': tensor(0.5179, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} ```	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7" ]
MedicalVision/detr_nih_100ep	## Original result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.000 ``` ## After training result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.007 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.019 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.003 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.008 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.060 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.147 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.160 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.167 ``` ## Config - dataset: NIH - original model: facebook/detr-resnet-50 - lr: 0.0001 - max_epochs: 100 ## Logging ### Training process ``` {'validation_loss': tensor(6.3390, device='cuda:0'), 'validation_loss_ce': tensor(1.9257, device='cuda:0'), 'validation_loss_bbox': tensor(0.5244, device='cuda:0'), 'validation_loss_giou': tensor(0.8958, device='cuda:0'), 'validation_cardinality_error': tensor(60.5938, device='cuda:0')} {'training_loss': tensor(3.2533, device='cuda:0'), 'train_loss_ce': tensor(0.6592, device='cuda:0'), 'train_loss_bbox': tensor(0.2086, device='cuda:0'), 'train_loss_giou': tensor(0.7757, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(3.0453, device='cuda:0'), 'validation_loss_ce': tensor(0.5371, device='cuda:0'), 'validation_loss_bbox': tensor(0.2398, device='cuda:0'), 'validation_loss_giou': tensor(0.6546, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.6515, device='cuda:0'), 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device='cuda:0'), 'validation_loss_ce': tensor(0.3682, device='cuda:0'), 'validation_loss_bbox': tensor(0.1379, device='cuda:0'), 'validation_loss_giou': tensor(0.5021, device='cuda:0'), 'validation_cardinality_error': tensor(0.8485, device='cuda:0')} {'training_loss': tensor(1.1113, device='cuda:0'), 'train_loss_ce': tensor(0.3081, device='cuda:0'), 'train_loss_bbox': tensor(0.0766, device='cuda:0'), 'train_loss_giou': tensor(0.2101, device='cuda:0'), 'train_cardinality_error': tensor(0.6000, device='cuda:0'), 'validation_loss': tensor(2.0995, device='cuda:0'), 'validation_loss_ce': tensor(0.3756, device='cuda:0'), 'validation_loss_bbox': tensor(0.1425, device='cuda:0'), 'validation_loss_giou': tensor(0.5057, device='cuda:0'), 'validation_cardinality_error': tensor(0.7879, device='cuda:0')} {'training_loss': tensor(1.2509, device='cuda:0'), 'train_loss_ce': tensor(0.2919, device='cuda:0'), 'train_loss_bbox': tensor(0.0893, device='cuda:0'), 'train_loss_giou': tensor(0.2564, device='cuda:0'), 'train_cardinality_error': tensor(0.8000, device='cuda:0'), 'validation_loss': tensor(2.0875, device='cuda:0'), 'validation_loss_ce': tensor(0.3768, device='cuda:0'), 'validation_loss_bbox': tensor(0.1419, device='cuda:0'), 'validation_loss_giou': tensor(0.5006, device='cuda:0'), 'validation_cardinality_error': tensor(0.8889, device='cuda:0')} {'training_loss': tensor(2.0985, device='cuda:0'), 'train_loss_ce': tensor(0.4567, device='cuda:0'), 'train_loss_bbox': tensor(0.1506, device='cuda:0'), 'train_loss_giou': tensor(0.4444, device='cuda:0'), 'train_cardinality_error': tensor(0.8000, device='cuda:0'), 'validation_loss': tensor(2.0427, device='cuda:0'), 'validation_loss_ce': tensor(0.3717, device='cuda:0'), 'validation_loss_bbox': tensor(0.1338, device='cuda:0'), 'validation_loss_giou': tensor(0.5009, device='cuda:0'), 'validation_cardinality_error': tensor(0.8586, device='cuda:0')} {'training_loss': tensor(1.4819, device='cuda:0'), 'train_loss_ce': tensor(0.3455, device='cuda:0'), 'train_loss_bbox': tensor(0.0939, device='cuda:0'), 'train_loss_giou': tensor(0.3335, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.0359, device='cuda:0'), 'validation_loss_ce': tensor(0.3772, device='cuda:0'), 'validation_loss_bbox': tensor(0.1353, device='cuda:0'), 'validation_loss_giou': tensor(0.4910, device='cuda:0'), 'validation_cardinality_error': tensor(0.8081, device='cuda:0')} {'training_loss': tensor(1.5143, device='cuda:0'), 'train_loss_ce': tensor(0.3716, device='cuda:0'), 'train_loss_bbox': tensor(0.0961, device='cuda:0'), 'train_loss_giou': tensor(0.3310, device='cuda:0'), 'train_cardinality_error': tensor(0.6000, device='cuda:0'), 'validation_loss': tensor(2.0415, device='cuda:0'), 'validation_loss_ce': tensor(0.3759, device='cuda:0'), 'validation_loss_bbox': tensor(0.1340, device='cuda:0'), 'validation_loss_giou': tensor(0.4977, device='cuda:0'), 'validation_cardinality_error': tensor(0.7172, device='cuda:0')} {'training_loss': tensor(1.5073, device='cuda:0'), 'train_loss_ce': tensor(0.4135, device='cuda:0'), 'train_loss_bbox': tensor(0.0790, device='cuda:0'), 'train_loss_giou': tensor(0.3494, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.0644, device='cuda:0'), 'validation_loss_ce': tensor(0.3671, device='cuda:0'), 'validation_loss_bbox': tensor(0.1376, device='cuda:0'), 'validation_loss_giou': tensor(0.5045, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.6490, device='cuda:0'), 'train_loss_ce': tensor(0.3305, device='cuda:0'), 'train_loss_bbox': tensor(0.1019, device='cuda:0'), 'train_loss_giou': tensor(0.4045, device='cuda:0'), 'train_cardinality_error': tensor(0.8000, device='cuda:0'), 'validation_loss': tensor(2.0022, device='cuda:0'), 'validation_loss_ce': tensor(0.3722, device='cuda:0'), 'validation_loss_bbox': tensor(0.1319, device='cuda:0'), 'validation_loss_giou': tensor(0.4852, device='cuda:0'), 'validation_cardinality_error': tensor(0.7677, device='cuda:0')} {'training_loss': tensor(1.5561, device='cuda:0'), 'train_loss_ce': tensor(0.3952, device='cuda:0'), 'train_loss_bbox': tensor(0.0957, device='cuda:0'), 'train_loss_giou': tensor(0.3413, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.0142, device='cuda:0'), 'validation_loss_ce': tensor(0.3756, device='cuda:0'), 'validation_loss_bbox': tensor(0.1325, device='cuda:0'), 'validation_loss_giou': tensor(0.4880, device='cuda:0'), 'validation_cardinality_error': tensor(0.8788, device='cuda:0')} {'training_loss': tensor(1.7862, device='cuda:0'), 'train_loss_ce': tensor(0.4257, device='cuda:0'), 'train_loss_bbox': tensor(0.0938, device='cuda:0'), 'train_loss_giou': tensor(0.4458, device='cuda:0'), 'train_cardinality_error': tensor(0.8000, device='cuda:0'), 'validation_loss': tensor(2.0516, device='cuda:0'), 'validation_loss_ce': tensor(0.3752, device='cuda:0'), 'validation_loss_bbox': tensor(0.1374, device='cuda:0'), 'validation_loss_giou': tensor(0.4947, device='cuda:0'), 'validation_cardinality_error': tensor(0.7273, device='cuda:0')} {'training_loss': tensor(1.2506, device='cuda:0'), 'train_loss_ce': tensor(0.2011, device='cuda:0'), 'train_loss_bbox': tensor(0.0865, device='cuda:0'), 'train_loss_giou': tensor(0.3084, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.0788, device='cuda:0'), 'validation_loss_ce': tensor(0.3698, device='cuda:0'), 'validation_loss_bbox': tensor(0.1379, device='cuda:0'), 'validation_loss_giou': tensor(0.5097, device='cuda:0'), 'validation_cardinality_error': tensor(0.9899, device='cuda:0')} {'training_loss': tensor(1.0275, device='cuda:0'), 'train_loss_ce': tensor(0.3081, device='cuda:0'), 'train_loss_bbox': tensor(0.0678, device='cuda:0'), 'train_loss_giou': tensor(0.1902, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.0275, device='cuda:0'), 'validation_loss_ce': tensor(0.3691, device='cuda:0'), 'validation_loss_bbox': tensor(0.1308, device='cuda:0'), 'validation_loss_giou': tensor(0.5021, device='cuda:0'), 'validation_cardinality_error': tensor(0.8485, device='cuda:0')} {'training_loss': tensor(1.1726, device='cuda:0'), 'train_loss_ce': tensor(0.3240, device='cuda:0'), 'train_loss_bbox': tensor(0.0523, device='cuda:0'), 'train_loss_giou': tensor(0.2935, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.0288, device='cuda:0'), 'validation_loss_ce': tensor(0.3669, device='cuda:0'), 'validation_loss_bbox': tensor(0.1348, device='cuda:0'), 'validation_loss_giou': tensor(0.4941, device='cuda:0'), 'validation_cardinality_error': tensor(0.9899, device='cuda:0')} {'training_loss': tensor(1.5491, device='cuda:0'), 'train_loss_ce': tensor(0.3478, device='cuda:0'), 'train_loss_bbox': tensor(0.0993, device='cuda:0'), 'train_loss_giou': tensor(0.3525, device='cuda:0'), 'train_cardinality_error': tensor(0.8000, device='cuda:0'), 'validation_loss': tensor(2.0170, device='cuda:0'), 'validation_loss_ce': tensor(0.3702, device='cuda:0'), 'validation_loss_bbox': tensor(0.1325, device='cuda:0'), 'validation_loss_giou': tensor(0.4921, device='cuda:0'), 'validation_cardinality_error': tensor(0.9091, device='cuda:0')} {'training_loss': tensor(1.3482, device='cuda:0'), 'train_loss_ce': tensor(0.2806, device='cuda:0'), 'train_loss_bbox': tensor(0.0887, device='cuda:0'), 'train_loss_giou': tensor(0.3121, device='cuda:0'), 'train_cardinality_error': tensor(0.6000, device='cuda:0'), 'validation_loss': tensor(2.0218, device='cuda:0'), 'validation_loss_ce': tensor(0.3643, device='cuda:0'), 'validation_loss_bbox': tensor(0.1347, device='cuda:0'), 'validation_loss_giou': tensor(0.4919, device='cuda:0'), 'validation_cardinality_error': tensor(0.5859, device='cuda:0')} {'training_loss': tensor(1.4447, device='cuda:0'), 'train_loss_ce': tensor(0.4458, device='cuda:0'), 'train_loss_bbox': tensor(0.0673, device='cuda:0'), 'train_loss_giou': tensor(0.3311, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.0012, device='cuda:0'), 'validation_loss_ce': tensor(0.3722, device='cuda:0'), 'validation_loss_bbox': tensor(0.1287, device='cuda:0'), 'validation_loss_giou': tensor(0.4928, device='cuda:0'), 'validation_cardinality_error': tensor(0.7778, device='cuda:0')} {'training_loss': tensor(1.0222, device='cuda:0'), 'train_loss_ce': tensor(0.2589, device='cuda:0'), 'train_loss_bbox': tensor(0.0659, device='cuda:0'), 'train_loss_giou': tensor(0.2170, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.0226, device='cuda:0'), 'validation_loss_ce': tensor(0.3678, device='cuda:0'), 'validation_loss_bbox': tensor(0.1364, device='cuda:0'), 'validation_loss_giou': tensor(0.4865, device='cuda:0'), 'validation_cardinality_error': tensor(0.8586, device='cuda:0')} ```	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7" ]
mrdbourke/detr_finetuned_trashify_box_detector	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr_finetuned_trashify_box_detector This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1302 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 25 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 101.8783 \| 1.0 \| 50 \| 7.5132 \| \| 4.1455 \| 2.0 \| 100 \| 3.0556 \| \| 2.5964 \| 3.0 \| 150 \| 2.2737 \| \| 2.2773 \| 4.0 \| 200 \| 2.0691 \| \| 2.0818 \| 5.0 \| 250 \| 1.8494 \| \| 1.9253 \| 6.0 \| 300 \| 1.6872 \| \| 1.7802 \| 7.0 \| 350 \| 1.6033 \| \| 1.675 \| 8.0 \| 400 \| 1.4511 \| \| 1.5263 \| 9.0 \| 450 \| 1.4097 \| \| 1.4322 \| 10.0 \| 500 \| 1.3397 \| \| 1.386 \| 11.0 \| 550 \| 1.2897 \| \| 1.3098 \| 12.0 \| 600 \| 1.2813 \| \| 1.248 \| 13.0 \| 650 \| 1.2096 \| \| 1.209 \| 14.0 \| 700 \| 1.2200 \| \| 1.1757 \| 15.0 \| 750 \| 1.1987 \| \| 1.144 \| 16.0 \| 800 \| 1.1757 \| \| 1.0732 \| 17.0 \| 850 \| 1.1935 \| \| 1.0501 \| 18.0 \| 900 \| 1.1531 \| \| 0.9864 \| 19.0 \| 950 \| 1.1576 \| \| 0.9941 \| 20.0 \| 1000 \| 1.1513 \| \| 0.9589 \| 21.0 \| 1050 \| 1.1450 \| \| 0.9279 \| 22.0 \| 1100 \| 1.1355 \| \| 0.9071 \| 23.0 \| 1150 \| 1.1233 \| \| 0.8851 \| 24.0 \| 1200 \| 1.1338 \| \| 0.8709 \| 25.0 \| 1250 \| 1.1302 \| ### Framework versions - Transformers 4.45.0.dev0 - Pytorch 2.4.0+cu124 - Datasets 2.21.0 - Tokenizers 0.19.1	[ "bin", "hand", "not_bin", "not_hand", "not_trash", "trash", "trash_arm" ]
MedicalVision/detr_nih_3ep	## Original result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.001 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.008 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.017 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.017 ``` ## After training result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.003 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.007 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.002 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.003 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.024 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.066 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.070 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.071 ``` ## Config - dataset: NIH - original model: facebook/detr-resnet-50 - lr: 0.0001 - max_epochs: 3 ## Logging ### Training process ``` {'training_loss': tensor(2.0822, device='cuda:0'), 'train_loss_ce': tensor(0.5032, device='cuda:0'), 'train_loss_bbox': tensor(0.1356, device='cuda:0'), 'train_loss_giou': tensor(0.4505, device='cuda:0'), 'train_cardinality_error': tensor(1.0625, device='cuda:0'), 'validation_loss': tensor(2.3906, device='cuda:0'), 'validation_loss_ce': tensor(0.5131, device='cuda:0'), 'validation_loss_bbox': tensor(0.1641, device='cuda:0'), 'validation_loss_giou': tensor(0.5285, device='cuda:0'), 'validation_cardinality_error': tensor(1.1227, device='cuda:0')} {'training_loss': tensor(2.5546, device='cuda:0'), 'train_loss_ce': tensor(0.5681, device='cuda:0'), 'train_loss_bbox': tensor(0.1646, device='cuda:0'), 'train_loss_giou': tensor(0.5818, device='cuda:0'), 'train_cardinality_error': tensor(1.2500, device='cuda:0'), 'validation_loss': tensor(2.4028, device='cuda:0'), 'validation_loss_ce': tensor(0.5090, device='cuda:0'), 'validation_loss_bbox': tensor(0.1696, device='cuda:0'), 'validation_loss_giou': tensor(0.5230, device='cuda:0'), 'validation_cardinality_error': tensor(1.1227, device='cuda:0')} {'training_loss': tensor(2.4528, device='cuda:0'), 'train_loss_ce': tensor(0.4475, device='cuda:0'), 'train_loss_bbox': tensor(0.1614, device='cuda:0'), 'train_loss_giou': tensor(0.5991, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.3522, device='cuda:0'), 'validation_loss_ce': tensor(0.4847, device='cuda:0'), 'validation_loss_bbox': tensor(0.1584, device='cuda:0'), 'validation_loss_giou': tensor(0.5377, device='cuda:0'), 'validation_cardinality_error': tensor(1.1227, device='cuda:0')} ``` ### Validation process ``` {'validation_loss': tensor(6.1819, device='cuda:0'), 'validation_loss_ce': tensor(2.1987, device='cuda:0'), 'validation_loss_bbox': tensor(0.4528, device='cuda:0'), 'validation_loss_giou': tensor(0.8597, device='cuda:0'), 'validation_cardinality_error': tensor(97.7500, device='cuda:0')} {'training_loss': tensor(2.0822, device='cuda:0'), 'train_loss_ce': tensor(0.5032, device='cuda:0'), 'train_loss_bbox': tensor(0.1356, device='cuda:0'), 'train_loss_giou': tensor(0.4505, device='cuda:0'), 'train_cardinality_error': tensor(1.0625, device='cuda:0'), 'validation_loss': tensor(2.3906, device='cuda:0'), 'validation_loss_ce': tensor(0.5131, device='cuda:0'), 'validation_loss_bbox': tensor(0.1641, device='cuda:0'), 'validation_loss_giou': tensor(0.5285, device='cuda:0'), 'validation_cardinality_error': tensor(1.1227, device='cuda:0')} {'training_loss': tensor(2.5546, device='cuda:0'), 'train_loss_ce': tensor(0.5681, device='cuda:0'), 'train_loss_bbox': tensor(0.1646, device='cuda:0'), 'train_loss_giou': tensor(0.5818, device='cuda:0'), 'train_cardinality_error': tensor(1.2500, device='cuda:0'), 'validation_loss': tensor(2.4028, device='cuda:0'), 'validation_loss_ce': tensor(0.5090, device='cuda:0'), 'validation_loss_bbox': tensor(0.1696, device='cuda:0'), 'validation_loss_giou': tensor(0.5230, device='cuda:0'), 'validation_cardinality_error': tensor(1.1227, device='cuda:0')} {'training_loss': tensor(2.4528, device='cuda:0'), 'train_loss_ce': tensor(0.4475, device='cuda:0'), 'train_loss_bbox': tensor(0.1614, device='cuda:0'), 'train_loss_giou': tensor(0.5991, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.3522, device='cuda:0'), 'validation_loss_ce': tensor(0.4847, device='cuda:0'), 'validation_loss_bbox': tensor(0.1584, device='cuda:0'), 'validation_loss_giou': tensor(0.5377, device='cuda:0'), 'validation_cardinality_error': tensor(1.1227, device='cuda:0')} ```	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7" ]
mahim05078/detr-finetuned-bangla-logos-small	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MedicalVision/detr_nih_300ep	## Original result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.002 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.008 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.012 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.012 ``` ## After training result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.026 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.055 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.019 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.050 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.026 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.063 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.147 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.169 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.050 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.170 ``` ## Config - dataset: NIH - original model: facebook/detr-resnet-50 - lr: 0.0001 - dropout_rate: 0.1 - weight_decay: 0.0001 - max_epochs: 300 - train samples: 885 ## Logging ### Training process ``` {'validation_loss': tensor(6.1729, device='cuda:0'), 'validation_loss_ce': tensor(2.3765, device='cuda:0'), 'validation_loss_bbox': tensor(0.4626, device='cuda:0'), 'validation_loss_giou': tensor(0.7416, device='cuda:0'), 'validation_cardinality_error': tensor(99., device='cuda:0')} {'training_loss': tensor(1.9768, device='cuda:0'), 'train_loss_ce': tensor(0.4683, device='cuda:0'), 'train_loss_bbox': tensor(0.1455, device='cuda:0'), 'train_loss_giou': tensor(0.3905, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.5229, device='cuda:0'), 'validation_loss_ce': tensor(0.4876, device='cuda:0'), 'validation_loss_bbox': tensor(0.1898, device='cuda:0'), 'validation_loss_giou': tensor(0.5430, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.2565, device='cuda:0'), 'train_loss_ce': tensor(0.5037, device='cuda:0'), 'train_loss_bbox': tensor(0.1667, device='cuda:0'), 'train_loss_giou': tensor(0.4595, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.4304, device='cuda:0'), 'validation_loss_ce': tensor(0.4650, device='cuda:0'), 'validation_loss_bbox': tensor(0.1682, device='cuda:0'), 'validation_loss_giou': tensor(0.5622, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.8743, device='cuda:0'), 'train_loss_ce': tensor(0.4792, device='cuda:0'), 'train_loss_bbox': tensor(0.1430, device='cuda:0'), 'train_loss_giou': tensor(0.3400, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.2783, device='cuda:0'), 'validation_loss_ce': tensor(0.4509, device='cuda:0'), 'validation_loss_bbox': tensor(0.1640, device='cuda:0'), 'validation_loss_giou': tensor(0.5036, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.7595, device='cuda:0'), 'train_loss_ce': tensor(0.3362, device='cuda:0'), 'train_loss_bbox': tensor(0.1213, device='cuda:0'), 'train_loss_giou': tensor(0.4083, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.2793, device='cuda:0'), 'validation_loss_ce': tensor(0.4431, device='cuda:0'), 'validation_loss_bbox': tensor(0.1564, device='cuda:0'), 'validation_loss_giou': tensor(0.5271, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.0072, device='cuda:0'), 'train_loss_ce': tensor(0.3691, device='cuda:0'), 'train_loss_bbox': tensor(0.1479, device='cuda:0'), 'train_loss_giou': tensor(0.4493, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.3733, device='cuda:0'), 'validation_loss_ce': tensor(0.4411, 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'validation_loss': tensor(2.7376, device='cuda:0'), 'validation_loss_ce': tensor(1.1461, device='cuda:0'), 'validation_loss_bbox': tensor(0.1302, device='cuda:0'), 'validation_loss_giou': tensor(0.4703, device='cuda:0'), 'validation_cardinality_error': tensor(0.5960, device='cuda:0')} {'training_loss': tensor(0.3142, device='cuda:0'), 'train_loss_ce': tensor(0.0070, device='cuda:0'), 'train_loss_bbox': tensor(0.0146, device='cuda:0'), 'train_loss_giou': tensor(0.1172, device='cuda:0'), 'train_cardinality_error': tensor(0.2000, device='cuda:0'), 'validation_loss': tensor(2.6885, device='cuda:0'), 'validation_loss_ce': tensor(1.1088, device='cuda:0'), 'validation_loss_bbox': tensor(0.1281, device='cuda:0'), 'validation_loss_giou': tensor(0.4695, device='cuda:0'), 'validation_cardinality_error': tensor(0.5758, device='cuda:0')} {'training_loss': tensor(0.2394, device='cuda:0'), 'train_loss_ce': tensor(0.0141, device='cuda:0'), 'train_loss_bbox': tensor(0.0133, device='cuda:0'), 'train_loss_giou': tensor(0.0793, device='cuda:0'), 'train_cardinality_error': tensor(0.4000, device='cuda:0'), 'validation_loss': tensor(2.6745, device='cuda:0'), 'validation_loss_ce': tensor(1.0608, device='cuda:0'), 'validation_loss_bbox': tensor(0.1291, device='cuda:0'), 'validation_loss_giou': tensor(0.4840, device='cuda:0'), 'validation_cardinality_error': tensor(0.6263, device='cuda:0')} ``` ## Examples {'size': tensor([800, 800]), 'image_id': tensor([1]), 'class_labels': tensor([4]), 'boxes': tensor([[0.2622, 0.5729, 0.0847, 0.0773]]), 'area': tensor([4194.1973]), 'iscrowd': tensor([0]), 'orig_size': tensor([1024, 1024])} ![Example](./example.png)	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7" ]
MedicalVision/detr_nih_200ep	## Original result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.007 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.012 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.012 ``` ## After training result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.014 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.032 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.017 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.017 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.014 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.077 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.135 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.168 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.061 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.168 ``` ## Config - dataset: NIH - original model: facebook/detr-resnet-50 - lr: 0.0001 - max_epochs: 200 ## Logging ### Training process ``` {'validation_loss': tensor(6.4207, device='cuda:0'), 'validation_loss_ce': tensor(2.0609, device='cuda:0'), 'validation_loss_bbox': tensor(0.5410, device='cuda:0'), 'validation_loss_giou': tensor(0.8274, device='cuda:0'), 'validation_cardinality_error': tensor(84.9688, device='cuda:0')} {'training_loss': tensor(2.3578, device='cuda:0'), 'train_loss_ce': tensor(0.5497, device='cuda:0'), 'train_loss_bbox': tensor(0.1655, device='cuda:0'), 'train_loss_giou': tensor(0.4904, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(3.0166, device='cuda:0'), 'validation_loss_ce': tensor(0.5406, device='cuda:0'), 'validation_loss_bbox': tensor(0.2461, device='cuda:0'), 'validation_loss_giou': tensor(0.6227, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.6157, device='cuda:0'), 'train_loss_ce': tensor(0.4145, device='cuda:0'), 'train_loss_bbox': tensor(0.2141, device='cuda:0'), 'train_loss_giou': tensor(0.5653, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.6287, device='cuda:0'), 'validation_loss_ce': tensor(0.4480, device='cuda:0'), 'validation_loss_bbox': tensor(0.1945, device='cuda:0'), 'validation_loss_giou': tensor(0.6040, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.7792, device='cuda:0'), 'train_loss_ce': tensor(0.4003, device='cuda:0'), 'train_loss_bbox': tensor(0.1783, device='cuda:0'), 'train_loss_giou': tensor(0.7436, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.4423, device='cuda:0'), 'validation_loss_ce': tensor(0.4439, device='cuda:0'), 'validation_loss_bbox': tensor(0.1715, device='cuda:0'), 'validation_loss_giou': tensor(0.5705, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(3.0499, device='cuda:0'), 'train_loss_ce': tensor(0.4943, device='cuda:0'), 'train_loss_bbox': tensor(0.2466, device='cuda:0'), 'train_loss_giou': tensor(0.6613, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.3473, device='cuda:0'), 'validation_loss_ce': tensor(0.4454, device='cuda:0'), 'validation_loss_bbox': tensor(0.1586, device='cuda:0'), 'validation_loss_giou': tensor(0.5544, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.5991, device='cuda:0'), 'train_loss_ce': tensor(0.4014, device='cuda:0'), 'train_loss_bbox': tensor(0.1607, device='cuda:0'), 'train_loss_giou': tensor(0.6971, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.2988, device='cuda:0'), 'validation_loss_ce': tensor(0.4328, device='cuda:0'), 'validation_loss_bbox': 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'validation_loss': tensor(2.0154, device='cuda:0'), 'validation_loss_ce': tensor(0.3802, device='cuda:0'), 'validation_loss_bbox': tensor(0.1307, device='cuda:0'), 'validation_loss_giou': tensor(0.4909, device='cuda:0'), 'validation_cardinality_error': tensor(0.8788, device='cuda:0')} {'training_loss': tensor(1.0079, device='cuda:0'), 'train_loss_ce': tensor(0.1552, device='cuda:0'), 'train_loss_bbox': tensor(0.0418, device='cuda:0'), 'train_loss_giou': tensor(0.3218, device='cuda:0'), 'train_cardinality_error': tensor(0.8000, device='cuda:0'), 'validation_loss': tensor(2.0421, device='cuda:0'), 'validation_loss_ce': tensor(0.3847, device='cuda:0'), 'validation_loss_bbox': tensor(0.1331, device='cuda:0'), 'validation_loss_giou': tensor(0.4960, device='cuda:0'), 'validation_cardinality_error': tensor(0.8788, device='cuda:0')} {'training_loss': tensor(0.9804, device='cuda:0'), 'train_loss_ce': tensor(0.3195, device='cuda:0'), 'train_loss_bbox': tensor(0.0394, device='cuda:0'), 'train_loss_giou': tensor(0.2318, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.0393, device='cuda:0'), 'validation_loss_ce': tensor(0.3833, device='cuda:0'), 'validation_loss_bbox': tensor(0.1363, device='cuda:0'), 'validation_loss_giou': tensor(0.4871, device='cuda:0'), 'validation_cardinality_error': tensor(0.7778, device='cuda:0')} ```	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7" ]
farhanishraq/table_tr-finetuned-bs-2.0	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MedicalVision/detr_nih_50ep_raise	## Original result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.001 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.015 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.016 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.016 ``` ## After training result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.005 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.015 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.004 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.005 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.046 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.119 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.134 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.144 ``` ## Config - dataset: NIH - original model: facebook/detr-resnet-50 - lr: 0.0001 - dropout_rate: 0.3 - weight_decay: 0.01 - max_epochs: 50 ## Logging ### Training process ``` {'validation_loss': tensor(6.1640, device='cuda:0'), 'validation_loss_ce': tensor(2.3579, device='cuda:0'), 'validation_loss_bbox': tensor(0.4592, device='cuda:0'), 'validation_loss_giou': tensor(0.7551, device='cuda:0'), 'validation_cardinality_error': tensor(99., device='cuda:0')} {'training_loss': tensor(2.5012, device='cuda:0'), 'train_loss_ce': tensor(0.4724, device='cuda:0'), 'train_loss_bbox': tensor(0.2203, device='cuda:0'), 'train_loss_giou': tensor(0.4637, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.5773, device='cuda:0'), 'validation_loss_ce': tensor(0.4940, device='cuda:0'), 'validation_loss_bbox': tensor(0.1985, device='cuda:0'), 'validation_loss_giou': tensor(0.5455, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.5154, device='cuda:0'), 'train_loss_ce': tensor(0.5113, device='cuda:0'), 'train_loss_bbox': tensor(0.1683, device='cuda:0'), 'train_loss_giou': tensor(0.5813, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.8801, device='cuda:0'), 'validation_loss_ce': tensor(0.5555, device='cuda:0'), 'validation_loss_bbox': tensor(0.2172, device='cuda:0'), 'validation_loss_giou': tensor(0.6192, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.0469, device='cuda:0'), 'train_loss_ce': tensor(0.4756, device='cuda:0'), 'train_loss_bbox': tensor(0.1356, device='cuda:0'), 'train_loss_giou': tensor(0.4466, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.2915, device='cuda:0'), 'validation_loss_ce': tensor(0.4524, device='cuda:0'), 'validation_loss_bbox': tensor(0.1640, device='cuda:0'), 'validation_loss_giou': tensor(0.5096, 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device='cuda:0'), 'validation_loss_ce': tensor(0.3757, device='cuda:0'), 'validation_loss_bbox': tensor(0.1534, device='cuda:0'), 'validation_loss_giou': tensor(0.5201, device='cuda:0'), 'validation_cardinality_error': tensor(0.0303, device='cuda:0')} {'training_loss': tensor(2.3304, device='cuda:0'), 'train_loss_ce': tensor(0.4064, device='cuda:0'), 'train_loss_bbox': tensor(0.1459, device='cuda:0'), 'train_loss_giou': tensor(0.5972, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.2106, device='cuda:0'), 'validation_loss_ce': tensor(0.3762, device='cuda:0'), 'validation_loss_bbox': tensor(0.1587, device='cuda:0'), 'validation_loss_giou': tensor(0.5205, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} ```	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7" ]
MedicalVision/detr_nih_100ep_raise	## Original result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.011 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.011 ``` ## After training result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.005 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.018 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.001 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.005 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.036 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.131 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.141 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.145 ``` ## Config - dataset: NIH - original model: facebook/detr-resnet-50 - lr: 0.0001 - dropout_rate: 0.3 - weight_decay: 0.01 - max_epochs: 100 ## Logging ### Training process ``` {'validation_loss': tensor(5.9922, device='cuda:0'), 'validation_loss_ce': tensor(1.9217, device='cuda:0'), 'validation_loss_bbox': tensor(0.4632, device='cuda:0'), 'validation_loss_giou': tensor(0.8772, device='cuda:0'), 'validation_cardinality_error': tensor(40.3438, device='cuda:0')} {'training_loss': tensor(3.1585, device='cuda:0'), 'train_loss_ce': tensor(0.5599, device='cuda:0'), 'train_loss_bbox': tensor(0.2764, device='cuda:0'), 'train_loss_giou': tensor(0.6083, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.7145, device='cuda:0'), 'validation_loss_ce': tensor(0.5435, device='cuda:0'), 'validation_loss_bbox': tensor(0.1939, device='cuda:0'), 'validation_loss_giou': tensor(0.6009, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': 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'train_loss_giou': tensor(0.4454, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.1188, device='cuda:0'), 'validation_loss_ce': tensor(0.3850, device='cuda:0'), 'validation_loss_bbox': tensor(0.1409, device='cuda:0'), 'validation_loss_giou': tensor(0.5148, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.9090, device='cuda:0'), 'train_loss_ce': tensor(0.3899, device='cuda:0'), 'train_loss_bbox': tensor(0.1334, device='cuda:0'), 'train_loss_giou': tensor(0.4259, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.1303, device='cuda:0'), 'validation_loss_ce': tensor(0.3922, device='cuda:0'), 'validation_loss_bbox': tensor(0.1415, device='cuda:0'), 'validation_loss_giou': tensor(0.5154, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.7123, device='cuda:0'), 'train_loss_ce': tensor(0.3532, device='cuda:0'), 'train_loss_bbox': tensor(0.1297, device='cuda:0'), 'train_loss_giou': tensor(0.3552, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.1428, device='cuda:0'), 'validation_loss_ce': tensor(0.3844, device='cuda:0'), 'validation_loss_bbox': tensor(0.1410, device='cuda:0'), 'validation_loss_giou': tensor(0.5268, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.5493, device='cuda:0'), 'train_loss_ce': tensor(0.3313, device='cuda:0'), 'train_loss_bbox': tensor(0.1282, device='cuda:0'), 'train_loss_giou': tensor(0.2885, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.1454, device='cuda:0'), 'validation_loss_ce': tensor(0.3884, device='cuda:0'), 'validation_loss_bbox': tensor(0.1415, device='cuda:0'), 'validation_loss_giou': tensor(0.5247, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} ```	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7" ]
MedicalVision/detr_test_overfitting	## Original result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.000 ``` ## After training result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 1.000 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 1.000 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 1.000 ``` ## Config - dataset: NIH - original model: facebook/detr-resnet-50 - lr: 0.0001 - dropout_rate: 0.1 - weight_decay: 0.0001 - max_epochs: 300 - train samples: 2 ## Logging ### Training process ``` {'validation_loss': tensor(6.0724, device='cuda:0'), 'validation_loss_ce': tensor(2.5304, device='cuda:0'), 'validation_loss_bbox': tensor(0.4019, device='cuda:0'), 'validation_loss_giou': tensor(0.7663, device='cuda:0'), 'validation_cardinality_error': tensor(98.5000, device='cuda:0')} {'training_loss': tensor(6.0724, device='cuda:0'), 'train_loss_ce': tensor(2.5304, device='cuda:0'), 'train_loss_bbox': tensor(0.4019, device='cuda:0'), 'train_loss_giou': tensor(0.7663, device='cuda:0'), 'train_cardinality_error': tensor(98.5000, device='cuda:0'), 'validation_loss': tensor(4.4364, device='cuda:0'), 'validation_loss_ce': tensor(2.4148, device='cuda:0'), 'validation_loss_bbox': tensor(0.1521, device='cuda:0'), 'validation_loss_giou': tensor(0.6306, device='cuda:0'), 'validation_cardinality_error': tensor(99., device='cuda:0')} {'training_loss': tensor(4.4364, device='cuda:0'), 'train_loss_ce': tensor(2.4148, device='cuda:0'), 'train_loss_bbox': tensor(0.1521, device='cuda:0'), 'train_loss_giou': tensor(0.6306, device='cuda:0'), 'train_cardinality_error': tensor(99., device='cuda:0'), 'validation_loss': tensor(4.2163, device='cuda:0'), 'validation_loss_ce': tensor(2.1667, device='cuda:0'), 'validation_loss_bbox': tensor(0.1160, device='cuda:0'), 'validation_loss_giou': tensor(0.7347, device='cuda:0'), 'validation_cardinality_error': tensor(95.5000, device='cuda:0')} {'training_loss': tensor(4.2163, device='cuda:0'), 'train_loss_ce': tensor(2.1667, device='cuda:0'), 'train_loss_bbox': tensor(0.1160, device='cuda:0'), 'train_loss_giou': tensor(0.7347, device='cuda:0'), 'train_cardinality_error': tensor(95.5000, device='cuda:0'), 'validation_loss': tensor(4.3139, device='cuda:0'), 'validation_loss_ce': tensor(2.2651, device='cuda:0'), 'validation_loss_bbox': tensor(0.1605, device='cuda:0'), 'validation_loss_giou': tensor(0.6232, device='cuda:0'), 'validation_cardinality_error': tensor(99., device='cuda:0')} {'training_loss': tensor(4.3139, device='cuda:0'), 'train_loss_ce': tensor(2.2651, device='cuda:0'), 'train_loss_bbox': tensor(0.1605, device='cuda:0'), 'train_loss_giou': tensor(0.6232, device='cuda:0'), 'train_cardinality_error': tensor(99., device='cuda:0'), 'validation_loss': tensor(4.0517, device='cuda:0'), 'validation_loss_ce': tensor(2.2360, device='cuda:0'), 'validation_loss_bbox': tensor(0.1019, device='cuda:0'), 'validation_loss_giou': tensor(0.6532, device='cuda:0'), 'validation_cardinality_error': tensor(99., device='cuda:0')} {'training_loss': tensor(4.0517, device='cuda:0'), 'train_loss_ce': tensor(2.2360, device='cuda:0'), 'train_loss_bbox': tensor(0.1019, device='cuda:0'), 'train_loss_giou': tensor(0.6532, device='cuda:0'), 'train_cardinality_error': tensor(99., device='cuda:0'), 'validation_loss': tensor(4.1543, device='cuda:0'), 'validation_loss_ce': 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tensor(0.0061, device='cuda:0'), 'validation_loss_giou': tensor(0.0554, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(0.3066, device='cuda:0'), 'train_loss_ce': tensor(0.1650, device='cuda:0'), 'train_loss_bbox': tensor(0.0061, device='cuda:0'), 'train_loss_giou': tensor(0.0554, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(0.2413, device='cuda:0'), 'validation_loss_ce': tensor(0.1640, device='cuda:0'), 'validation_loss_bbox': tensor(0.0045, device='cuda:0'), 'validation_loss_giou': tensor(0.0274, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(0.2413, device='cuda:0'), 'train_loss_ce': tensor(0.1640, device='cuda:0'), 'train_loss_bbox': tensor(0.0045, device='cuda:0'), 'train_loss_giou': tensor(0.0274, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(0.2695, device='cuda:0'), 'validation_loss_ce': tensor(0.1624, device='cuda:0'), 'validation_loss_bbox': tensor(0.0040, device='cuda:0'), 'validation_loss_giou': tensor(0.0435, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(0.2695, device='cuda:0'), 'train_loss_ce': tensor(0.1624, device='cuda:0'), 'train_loss_bbox': tensor(0.0040, device='cuda:0'), 'train_loss_giou': tensor(0.0435, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(0.2887, device='cuda:0'), 'validation_loss_ce': tensor(0.1601, device='cuda:0'), 'validation_loss_bbox': tensor(0.0067, device='cuda:0'), 'validation_loss_giou': tensor(0.0476, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(0.2887, device='cuda:0'), 'train_loss_ce': tensor(0.1601, device='cuda:0'), 'train_loss_bbox': tensor(0.0067, device='cuda:0'), 'train_loss_giou': tensor(0.0476, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(0.2860, device='cuda:0'), 'validation_loss_ce': tensor(0.1577, device='cuda:0'), 'validation_loss_bbox': tensor(0.0063, device='cuda:0'), 'validation_loss_giou': tensor(0.0485, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(0.2860, device='cuda:0'), 'train_loss_ce': tensor(0.1577, device='cuda:0'), 'train_loss_bbox': tensor(0.0063, device='cuda:0'), 'train_loss_giou': tensor(0.0485, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(0.2240, device='cuda:0'), 'validation_loss_ce': tensor(0.1560, device='cuda:0'), 'validation_loss_bbox': tensor(0.0036, device='cuda:0'), 'validation_loss_giou': tensor(0.0250, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(0.2240, device='cuda:0'), 'train_loss_ce': tensor(0.1560, device='cuda:0'), 'train_loss_bbox': tensor(0.0036, device='cuda:0'), 'train_loss_giou': tensor(0.0250, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(0.2140, device='cuda:0'), 'validation_loss_ce': tensor(0.1546, device='cuda:0'), 'validation_loss_bbox': tensor(0.0028, device='cuda:0'), 'validation_loss_giou': tensor(0.0227, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(0.2140, device='cuda:0'), 'train_loss_ce': tensor(0.1546, device='cuda:0'), 'train_loss_bbox': tensor(0.0028, device='cuda:0'), 'train_loss_giou': tensor(0.0227, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(0.2635, device='cuda:0'), 'validation_loss_ce': tensor(0.1530, device='cuda:0'), 'validation_loss_bbox': tensor(0.0049, device='cuda:0'), 'validation_loss_giou': tensor(0.0430, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(0.2635, device='cuda:0'), 'train_loss_ce': tensor(0.1530, device='cuda:0'), 'train_loss_bbox': tensor(0.0049, device='cuda:0'), 'train_loss_giou': tensor(0.0430, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(0.2690, device='cuda:0'), 'validation_loss_ce': tensor(0.1504, device='cuda:0'), 'validation_loss_bbox': tensor(0.0053, device='cuda:0'), 'validation_loss_giou': tensor(0.0460, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(0.2690, device='cuda:0'), 'train_loss_ce': tensor(0.1504, device='cuda:0'), 'train_loss_bbox': tensor(0.0053, device='cuda:0'), 'train_loss_giou': tensor(0.0460, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(0.2354, device='cuda:0'), 'validation_loss_ce': tensor(0.1478, device='cuda:0'), 'validation_loss_bbox': tensor(0.0050, device='cuda:0'), 'validation_loss_giou': tensor(0.0313, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(0.2354, device='cuda:0'), 'train_loss_ce': tensor(0.1478, device='cuda:0'), 'train_loss_bbox': tensor(0.0050, device='cuda:0'), 'train_loss_giou': tensor(0.0313, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(0.2619, device='cuda:0'), 'validation_loss_ce': tensor(0.1454, device='cuda:0'), 'validation_loss_bbox': tensor(0.0062, device='cuda:0'), 'validation_loss_giou': tensor(0.0428, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(0.2619, device='cuda:0'), 'train_loss_ce': tensor(0.1454, device='cuda:0'), 'train_loss_bbox': tensor(0.0062, device='cuda:0'), 'train_loss_giou': tensor(0.0428, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(0.2330, device='cuda:0'), 'validation_loss_ce': tensor(0.1426, device='cuda:0'), 'validation_loss_bbox': tensor(0.0057, device='cuda:0'), 'validation_loss_giou': tensor(0.0310, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} ``` ## Examples {'size': tensor([800, 800]), 'image_id': tensor([735]), 'class_labels': tensor([5]), 'boxes': tensor([[0.7651, 0.5307, 0.0952, 0.0942]]), 'area': tensor([5740.4888]), 'iscrowd': tensor([0]), 'orig_size': tensor([1024, 1024])} ![Example](./example.png)	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7" ]
MedicalVision/yolos_tiny_test	## Original result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.000 ``` ## After training result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.001 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.002 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.001 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.001 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.002 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.015 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.026 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.028 ``` ## Config - dataset: NIH - original model: hustvl/yolos-tiny - lr: 0.0001 - dropout_rate: 0.1 - weight_decay: 0.001 - max_epochs: 1 - train samples: 885 ## Logging ### Training process ``` {'validation_loss': tensor(7.2682, device='cuda:0'), 'validation_loss_ce': tensor(2.4654, device='cuda:0'), 'validation_loss_bbox': tensor(0.5599, device='cuda:0'), 'validation_loss_giou': tensor(1.0016, device='cuda:0'), 'validation_cardinality_error': tensor(99., device='cuda:0')} {'training_loss': tensor(3.1491, device='cuda:0'), 'train_loss_ce': tensor(0.3927, device='cuda:0'), 'train_loss_bbox': tensor(0.2719, device='cuda:0'), 'train_loss_giou': tensor(0.6985, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.2454, device='cuda:0'), 'validation_loss_ce': tensor(0.4346, device='cuda:0'), 'validation_loss_bbox': tensor(0.1519, device='cuda:0'), 'validation_loss_giou': tensor(0.5256, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} ``` ## Examples {'size': tensor([512, 512]), 'image_id': tensor([1]), 'class_labels': tensor([4]), 'boxes': tensor([[0.2622, 0.5729, 0.0847, 0.0773]]), 'area': tensor([1717.9431]), 'iscrowd': tensor([0]), 'orig_size': tensor([1024, 1024])} ![Example](./example.png)	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7" ]
MedicalVision/yolos_tiny_50ep	## Original result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.007 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.008 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.008 ``` ## After training result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.008 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.016 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.009 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.008 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.060 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.122 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.140 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.141 ``` ## Config - dataset: NIH - original model: hustvl/yolos-tiny - lr: 1e-05 - dropout_rate: 0.1 - weight_decay: 0.05 - max_epochs: 50 - train samples: 885 ## Logging ### Training process ``` {'validation_loss': tensor(7.0099, device='cuda:0'), 'validation_loss_ce': tensor(2.4436, device='cuda:0'), 'validation_loss_bbox': tensor(0.5540, device='cuda:0'), 'validation_loss_giou': tensor(0.8981, device='cuda:0'), 'validation_cardinality_error': tensor(99., device='cuda:0')} {'training_loss': tensor(3.9420, device='cuda:0'), 'train_loss_ce': tensor(1.3197, device='cuda:0'), 'train_loss_bbox': tensor(0.2500, device='cuda:0'), 'train_loss_giou': tensor(0.6860, device='cuda:0'), 'train_cardinality_error': tensor(7., device='cuda:0'), 'validation_loss': tensor(3.3165, device='cuda:0'), 'validation_loss_ce': tensor(1.2489, device='cuda:0'), 'validation_loss_bbox': tensor(0.1881, device='cuda:0'), 'validation_loss_giou': tensor(0.5635, device='cuda:0'), 'validation_cardinality_error': tensor(4.0101, device='cuda:0')} {'training_loss': tensor(3.3235, device='cuda:0'), 'train_loss_ce': tensor(0.6793, device='cuda:0'), 'train_loss_bbox': tensor(0.2541, device='cuda:0'), 'train_loss_giou': tensor(0.6867, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.4833, device='cuda:0'), 'validation_loss_ce': tensor(0.6021, device='cuda:0'), 'validation_loss_bbox': tensor(0.1646, device='cuda:0'), 'validation_loss_giou': tensor(0.5291, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.3983, device='cuda:0'), 'train_loss_ce': tensor(0.5598, device='cuda:0'), 'train_loss_bbox': tensor(0.1766, device='cuda:0'), 'train_loss_giou': tensor(0.4778, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.2504, device='cuda:0'), 'validation_loss_ce': tensor(0.4912, device='cuda:0'), 'validation_loss_bbox': tensor(0.1561, device='cuda:0'), 'validation_loss_giou': tensor(0.4895, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.9965, device='cuda:0'), 'train_loss_ce': tensor(0.4444, device='cuda:0'), 'train_loss_bbox': tensor(0.1335, device='cuda:0'), 'train_loss_giou': tensor(0.4423, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.2376, device='cuda:0'), 'validation_loss_ce': tensor(0.4733, device='cuda:0'), 'validation_loss_bbox': tensor(0.1545, device='cuda:0'), 'validation_loss_giou': tensor(0.4960, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.3605, device='cuda:0'), 'train_loss_ce': tensor(0.3837, device='cuda:0'), 'train_loss_bbox': tensor(0.1640, device='cuda:0'), 'train_loss_giou': tensor(0.5785, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.2257, device='cuda:0'), 'validation_loss_ce': tensor(0.4630, device='cuda:0'), 'validation_loss_bbox': tensor(0.1510, device='cuda:0'), 'validation_loss_giou': tensor(0.5039, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.8496, device='cuda:0'), 'train_loss_ce': tensor(0.4450, device='cuda:0'), 'train_loss_bbox': tensor(0.1345, device='cuda:0'), 'train_loss_giou': tensor(0.3660, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.0929, device='cuda:0'), 'validation_loss_ce': tensor(0.4594, device='cuda:0'), 'validation_loss_bbox': tensor(0.1348, device='cuda:0'), 'validation_loss_giou': tensor(0.4796, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.8037, device='cuda:0'), 'train_loss_ce': tensor(0.4246, device='cuda:0'), 'train_loss_bbox': tensor(0.1193, device='cuda:0'), 'train_loss_giou': tensor(0.3912, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.1036, device='cuda:0'), 'validation_loss_ce': tensor(0.4565, device='cuda:0'), 'validation_loss_bbox': tensor(0.1409, device='cuda:0'), 'validation_loss_giou': tensor(0.4713, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.4431, device='cuda:0'), 'train_loss_ce': tensor(0.4050, device='cuda:0'), 'train_loss_bbox': tensor(0.1342, device='cuda:0'), 'train_loss_giou': tensor(0.6835, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.0913, device='cuda:0'), 'validation_loss_ce': tensor(0.4534, device='cuda:0'), 'validation_loss_bbox': tensor(0.1405, device='cuda:0'), 'validation_loss_giou': tensor(0.4676, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.5937, device='cuda:0'), 'train_loss_ce': tensor(0.4950, device='cuda:0'), 'train_loss_bbox': tensor(0.0820, device='cuda:0'), 'train_loss_giou': 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tensor(0.1190, device='cuda:0'), 'validation_loss_giou': tensor(0.4448, device='cuda:0'), 'validation_cardinality_error': tensor(0.9697, device='cuda:0')} {'training_loss': tensor(1.0123, device='cuda:0'), 'train_loss_ce': tensor(0.4111, device='cuda:0'), 'train_loss_bbox': tensor(0.0362, device='cuda:0'), 'train_loss_giou': tensor(0.2100, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.9187, device='cuda:0'), 'validation_loss_ce': tensor(0.3924, device='cuda:0'), 'validation_loss_bbox': tensor(0.1253, device='cuda:0'), 'validation_loss_giou': tensor(0.4500, device='cuda:0'), 'validation_cardinality_error': tensor(0.9899, device='cuda:0')} {'training_loss': tensor(0.5481, device='cuda:0'), 'train_loss_ce': tensor(0.2719, device='cuda:0'), 'train_loss_bbox': tensor(0.0257, device='cuda:0'), 'train_loss_giou': tensor(0.0739, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.9145, device='cuda:0'), 'validation_loss_ce': tensor(0.3937, device='cuda:0'), 'validation_loss_bbox': tensor(0.1219, device='cuda:0'), 'validation_loss_giou': tensor(0.4556, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.0651, device='cuda:0'), 'train_loss_ce': tensor(0.3829, device='cuda:0'), 'train_loss_bbox': tensor(0.0515, device='cuda:0'), 'train_loss_giou': tensor(0.2123, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.8978, device='cuda:0'), 'validation_loss_ce': tensor(0.3919, device='cuda:0'), 'validation_loss_bbox': tensor(0.1215, device='cuda:0'), 'validation_loss_giou': tensor(0.4491, device='cuda:0'), 'validation_cardinality_error': tensor(0.9899, device='cuda:0')} ``` ## Examples {'size': tensor([512, 512]), 'image_id': tensor([1]), 'class_labels': tensor([4]), 'boxes': tensor([[0.2622, 0.5729, 0.0847, 0.0773]]), 'area': tensor([1717.9431]), 'iscrowd': tensor([0]), 'orig_size': tensor([1024, 1024])} ![Example](./example.png)	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7" ]
MedicalVision/yolos_tiny_100ep	## Original result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.000 ``` ## After training result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.002 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.009 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.003 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.004 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.033 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.054 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.056 ``` ## Config - dataset: NIH - original model: hustvl/yolos-tiny - lr: 1e-06 - dropout_rate: 0.1 - weight_decay: 0.05 - max_epochs: 100 - train samples: 885 ## Logging ### Training process ``` {'validation_loss': tensor(7.4421, device='cuda:0'), 'validation_loss_ce': tensor(2.5115, device='cuda:0'), 'validation_loss_bbox': tensor(0.5845, device='cuda:0'), 'validation_loss_giou': tensor(1.0041, device='cuda:0'), 'validation_cardinality_error': tensor(98.9688, device='cuda:0')} {'training_loss': tensor(5.3640, device='cuda:0'), 'train_loss_ce': tensor(2.2904, device='cuda:0'), 'train_loss_bbox': tensor(0.3264, device='cuda:0'), 'train_loss_giou': tensor(0.7207, device='cuda:0'), 'train_cardinality_error': tensor(99., device='cuda:0'), 'validation_loss': tensor(6.1285, device='cuda:0'), 'validation_loss_ce': tensor(2.2476, device='cuda:0'), 'validation_loss_bbox': tensor(0.4395, device='cuda:0'), 'validation_loss_giou': tensor(0.8417, device='cuda:0'), 'validation_cardinality_error': tensor(98.7273, device='cuda:0')} {'training_loss': tensor(4.6269, device='cuda:0'), 'train_loss_ce': tensor(2.0755, device='cuda:0'), 'train_loss_bbox': tensor(0.2549, device='cuda:0'), 'train_loss_giou': tensor(0.6384, device='cuda:0'), 'train_cardinality_error': tensor(97.6000, device='cuda:0'), 'validation_loss': tensor(5.2690, device='cuda:0'), 'validation_loss_ce': tensor(2.0870, device='cuda:0'), 'validation_loss_bbox': tensor(0.3288, device='cuda:0'), 'validation_loss_giou': tensor(0.7689, device='cuda:0'), 'validation_cardinality_error': tensor(96.6364, device='cuda:0')} {'training_loss': tensor(4.7931, device='cuda:0'), 'train_loss_ce': tensor(1.9471, device='cuda:0'), 'train_loss_bbox': tensor(0.2410, device='cuda:0'), 'train_loss_giou': tensor(0.8204, device='cuda:0'), 'train_cardinality_error': tensor(87.6000, device='cuda:0'), 'validation_loss': tensor(4.8855, device='cuda:0'), 'validation_loss_ce': tensor(1.9507, device='cuda:0'), 'validation_loss_bbox': tensor(0.2969, device='cuda:0'), 'validation_loss_giou': tensor(0.7252, device='cuda:0'), 'validation_cardinality_error': tensor(88.3535, device='cuda:0')} {'training_loss': tensor(4.7517, device='cuda:0'), 'train_loss_ce': tensor(1.8214, device='cuda:0'), 'train_loss_bbox': tensor(0.2749, device='cuda:0'), 'train_loss_giou': tensor(0.7778, device='cuda:0'), 'train_cardinality_error': tensor(72.2000, device='cuda:0'), 'validation_loss': tensor(4.4990, device='cuda:0'), 'validation_loss_ce': tensor(1.8171, device='cuda:0'), 'validation_loss_bbox': tensor(0.2615, device='cuda:0'), 'validation_loss_giou': tensor(0.6871, device='cuda:0'), 'validation_cardinality_error': tensor(71.3535, device='cuda:0')} {'training_loss': tensor(4.1045, device='cuda:0'), 'train_loss_ce': tensor(1.7791, device='cuda:0'), 'train_loss_bbox': tensor(0.1798, device='cuda:0'), 'train_loss_giou': tensor(0.7132, device='cuda:0'), 'train_cardinality_error': tensor(61.8000, device='cuda:0'), 'validation_loss': tensor(4.1904, device='cuda:0'), 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tensor(0.3836, device='cuda:0'), 'train_loss_bbox': tensor(0.1675, device='cuda:0'), 'train_loss_giou': tensor(0.3924, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.5247, device='cuda:0'), 'validation_loss_ce': tensor(0.4271, device='cuda:0'), 'validation_loss_bbox': tensor(0.1854, device='cuda:0'), 'validation_loss_giou': tensor(0.5853, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.4839, device='cuda:0'), 'train_loss_ce': tensor(0.2162, device='cuda:0'), 'train_loss_bbox': tensor(0.1028, device='cuda:0'), 'train_loss_giou': tensor(0.3769, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.5184, device='cuda:0'), 'validation_loss_ce': tensor(0.4272, device='cuda:0'), 'validation_loss_bbox': tensor(0.1824, device='cuda:0'), 'validation_loss_giou': tensor(0.5895, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.3401, device='cuda:0'), 'train_loss_ce': tensor(0.3501, device='cuda:0'), 'train_loss_bbox': tensor(0.0873, device='cuda:0'), 'train_loss_giou': tensor(0.2767, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.5114, device='cuda:0'), 'validation_loss_ce': tensor(0.4268, device='cuda:0'), 'validation_loss_bbox': tensor(0.1842, device='cuda:0'), 'validation_loss_giou': tensor(0.5817, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.3274, device='cuda:0'), 'train_loss_ce': tensor(0.4181, device='cuda:0'), 'train_loss_bbox': tensor(0.0620, device='cuda:0'), 'train_loss_giou': tensor(0.2997, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.5149, device='cuda:0'), 'validation_loss_ce': tensor(0.4258, device='cuda:0'), 'validation_loss_bbox': tensor(0.1843, device='cuda:0'), 'validation_loss_giou': tensor(0.5838, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} ``` ## Examples {'size': tensor([512, 512]), 'image_id': tensor([1]), 'class_labels': tensor([4]), 'boxes': tensor([[0.2622, 0.5729, 0.0847, 0.0773]]), 'area': tensor([1717.9431]), 'iscrowd': tensor([0]), 'orig_size': tensor([1024, 1024])} ![Example](./example.png)	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7" ]
MedicalVision/yolos_tiny_test_overfitting	## Original result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.000 ``` ## After training result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.900 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 1.000 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.900 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.900 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.900 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.900 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.900 ``` ## Config - dataset: NIH - original model: hustvl/yolos-tiny - lr: 0.0001 - dropout_rate: 0.1 - weight_decay: 0.0001 - max_epochs: 300 - train samples: 2 ## Logging ### Training process ``` {'validation_loss': tensor(8.4415, device='cuda:0'), 'validation_loss_ce': tensor(2.1892, device='cuda:0'), 'validation_loss_bbox': tensor(0.7321, device='cuda:0'), 'validation_loss_giou': tensor(1.2958, device='cuda:0'), 'validation_cardinality_error': tensor(97.5000, device='cuda:0')} {'training_loss': tensor(8.4415, device='cuda:0'), 'train_loss_ce': tensor(2.1892, device='cuda:0'), 'train_loss_bbox': tensor(0.7321, device='cuda:0'), 'train_loss_giou': tensor(1.2958, device='cuda:0'), 'train_cardinality_error': tensor(97.5000, device='cuda:0'), 'validation_loss': tensor(6.8429, device='cuda:0'), 'validation_loss_ce': tensor(1.7903, device='cuda:0'), 'validation_loss_bbox': tensor(0.4391, device='cuda:0'), 'validation_loss_giou': tensor(1.4286, device='cuda:0'), 'validation_cardinality_error': tensor(38., device='cuda:0')} {'training_loss': tensor(6.8429, device='cuda:0'), 'train_loss_ce': tensor(1.7903, device='cuda:0'), 'train_loss_bbox': tensor(0.4391, device='cuda:0'), 'train_loss_giou': tensor(1.4286, device='cuda:0'), 'train_cardinality_error': tensor(38., device='cuda:0'), 'validation_loss': tensor(4.6954, device='cuda:0'), 'validation_loss_ce': tensor(1.6042, device='cuda:0'), 'validation_loss_bbox': tensor(0.2544, device='cuda:0'), 'validation_loss_giou': tensor(0.9095, device='cuda:0'), 'validation_cardinality_error': tensor(21., device='cuda:0')} {'training_loss': tensor(4.6954, device='cuda:0'), 'train_loss_ce': tensor(1.6042, device='cuda:0'), 'train_loss_bbox': tensor(0.2544, device='cuda:0'), 'train_loss_giou': tensor(0.9095, device='cuda:0'), 'train_cardinality_error': tensor(21., device='cuda:0'), 'validation_loss': tensor(4.3832, device='cuda:0'), 'validation_loss_ce': tensor(1.4586, device='cuda:0'), 'validation_loss_bbox': tensor(0.1827, device='cuda:0'), 'validation_loss_giou': tensor(1.0055, device='cuda:0'), 'validation_cardinality_error': tensor(10.5000, device='cuda:0')} {'training_loss': tensor(4.3832, device='cuda:0'), 'train_loss_ce': tensor(1.4586, device='cuda:0'), 'train_loss_bbox': tensor(0.1827, device='cuda:0'), 'train_loss_giou': tensor(1.0055, device='cuda:0'), 'train_cardinality_error': tensor(10.5000, device='cuda:0'), 'validation_loss': tensor(2.5910, device='cuda:0'), 'validation_loss_ce': tensor(1.3605, device='cuda:0'), 'validation_loss_bbox': tensor(0.0772, device='cuda:0'), 'validation_loss_giou': tensor(0.4223, device='cuda:0'), 'validation_cardinality_error': tensor(0.5000, device='cuda:0')} {'training_loss': tensor(2.5910, device='cuda:0'), 'train_loss_ce': tensor(1.3605, device='cuda:0'), 'train_loss_bbox': tensor(0.0772, device='cuda:0'), 'train_loss_giou': tensor(0.4223, device='cuda:0'), 'train_cardinality_error': tensor(0.5000, device='cuda:0'), 'validation_loss': tensor(2.2764, device='cuda:0'), 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tensor(0.0076, device='cuda:0'), 'train_loss_giou': tensor(0.0550, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.1961, device='cuda:0'), 'validation_loss_ce': tensor(0.0020, device='cuda:0'), 'validation_loss_bbox': tensor(0.0111, device='cuda:0'), 'validation_loss_giou': tensor(0.0693, device='cuda:0'), 'validation_cardinality_error': tensor(0., device='cuda:0')} {'training_loss': tensor(0.1961, device='cuda:0'), 'train_loss_ce': tensor(0.0020, device='cuda:0'), 'train_loss_bbox': tensor(0.0111, device='cuda:0'), 'train_loss_giou': tensor(0.0693, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.1626, device='cuda:0'), 'validation_loss_ce': tensor(0.0021, device='cuda:0'), 'validation_loss_bbox': tensor(0.0083, device='cuda:0'), 'validation_loss_giou': tensor(0.0594, device='cuda:0'), 'validation_cardinality_error': tensor(0., device='cuda:0')} {'training_loss': tensor(0.1626, device='cuda:0'), 'train_loss_ce': tensor(0.0021, device='cuda:0'), 'train_loss_bbox': tensor(0.0083, device='cuda:0'), 'train_loss_giou': tensor(0.0594, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.1654, device='cuda:0'), 'validation_loss_ce': tensor(0.0021, device='cuda:0'), 'validation_loss_bbox': tensor(0.0094, device='cuda:0'), 'validation_loss_giou': tensor(0.0582, device='cuda:0'), 'validation_cardinality_error': tensor(0., device='cuda:0')} ``` ## Examples {'size': tensor([512, 512]), 'image_id': tensor([735]), 'class_labels': tensor([5]), 'boxes': tensor([[0.7651, 0.5307, 0.0952, 0.0942]]), 'area': tensor([2351.3042]), 'iscrowd': tensor([0]), 'orig_size': tensor([1024, 1024])} ![Example](./example.png)	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7" ]
MedicalVision/yolos_tiny_20ep	## Original result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.002 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.013 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.013 ``` ## After training result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.025 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.053 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.021 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.025 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.070 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.133 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.154 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.155 ``` ## Config - dataset: NIH - original model: hustvl/yolos-tiny - lr: 0.0001 - dropout_rate: 0.15 - weight_decay: 0.05 - max_epochs: 20 - train samples: 885 ## Logging ### Training process ``` {'validation_loss': tensor(6.7559, device='cuda:0'), 'validation_loss_ce': tensor(2.5739, device='cuda:0'), 'validation_loss_bbox': tensor(0.4952, device='cuda:0'), 'validation_loss_giou': tensor(0.8531, device='cuda:0'), 'validation_cardinality_error': tensor(99., device='cuda:0')} {'training_loss': tensor(2.4990, device='cuda:0'), 'train_loss_ce': tensor(0.4887, device='cuda:0'), 'train_loss_bbox': tensor(0.1862, device='cuda:0'), 'train_loss_giou': tensor(0.5398, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.4497, device='cuda:0'), 'validation_loss_ce': tensor(0.4524, device='cuda:0'), 'validation_loss_bbox': tensor(0.1829, device='cuda:0'), 'validation_loss_giou': tensor(0.5414, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.4763, device='cuda:0'), 'train_loss_ce': tensor(0.4236, device='cuda:0'), 'train_loss_bbox': tensor(0.1986, device='cuda:0'), 'train_loss_giou': tensor(0.5300, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.2358, device='cuda:0'), 'validation_loss_ce': tensor(0.4386, device='cuda:0'), 'validation_loss_bbox': tensor(0.1531, device='cuda:0'), 'validation_loss_giou': tensor(0.5160, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.0404, device='cuda:0'), 'train_loss_ce': tensor(0.4148, device='cuda:0'), 'train_loss_bbox': tensor(0.1398, device='cuda:0'), 'train_loss_giou': tensor(0.4634, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.3295, device='cuda:0'), 'validation_loss_ce': tensor(0.4369, device='cuda:0'), 'validation_loss_bbox': tensor(0.1697, device='cuda:0'), 'validation_loss_giou': tensor(0.5220, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.0230, device='cuda:0'), 'train_loss_ce': tensor(0.3600, device='cuda:0'), 'train_loss_bbox': tensor(0.1205, device='cuda:0'), 'train_loss_giou': tensor(0.5302, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.2546, device='cuda:0'), 'validation_loss_ce': tensor(0.4068, device='cuda:0'), 'validation_loss_bbox': tensor(0.1611, device='cuda:0'), 'validation_loss_giou': tensor(0.5210, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.1597, device='cuda:0'), 'train_loss_ce': tensor(0.4342, device='cuda:0'), 'train_loss_bbox': tensor(0.1431, device='cuda:0'), 'train_loss_giou': tensor(0.5049, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.0929, device='cuda:0'), 'validation_loss_ce': tensor(0.4126, device='cuda:0'), 'validation_loss_bbox': tensor(0.1394, device='cuda:0'), 'validation_loss_giou': tensor(0.4916, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.0645, device='cuda:0'), 'train_loss_ce': tensor(0.4740, device='cuda:0'), 'train_loss_bbox': tensor(0.1324, device='cuda:0'), 'train_loss_giou': tensor(0.4642, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.2642, device='cuda:0'), 'validation_loss_ce': tensor(0.4195, device='cuda:0'), 'validation_loss_bbox': tensor(0.1665, device='cuda:0'), 'validation_loss_giou': tensor(0.5060, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.7443, device='cuda:0'), 'train_loss_ce': tensor(0.3507, device='cuda:0'), 'train_loss_bbox': tensor(0.1351, device='cuda:0'), 'train_loss_giou': tensor(0.3591, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.9930, device='cuda:0'), 'validation_loss_ce': tensor(0.4063, device='cuda:0'), 'validation_loss_bbox': tensor(0.1294, device='cuda:0'), 'validation_loss_giou': tensor(0.4698, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.2440, device='cuda:0'), 'train_loss_ce': tensor(0.3884, device='cuda:0'), 'train_loss_bbox': tensor(0.1348, device='cuda:0'), 'train_loss_giou': tensor(0.5907, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.0082, device='cuda:0'), 'validation_loss_ce': tensor(0.4112, device='cuda:0'), 'validation_loss_bbox': tensor(0.1296, device='cuda:0'), 'validation_loss_giou': tensor(0.4744, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.7194, device='cuda:0'), 'train_loss_ce': tensor(0.3257, device='cuda:0'), 'train_loss_bbox': tensor(0.1185, device='cuda:0'), 'train_loss_giou': tensor(0.4007, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.0462, device='cuda:0'), 'validation_loss_ce': tensor(0.4009, device='cuda:0'), 'validation_loss_bbox': tensor(0.1423, device='cuda:0'), 'validation_loss_giou': tensor(0.4670, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.3192, device='cuda:0'), 'train_loss_ce': tensor(0.3495, device='cuda:0'), 'train_loss_bbox': tensor(0.1083, device='cuda:0'), 'train_loss_giou': tensor(0.2141, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.0731, device='cuda:0'), 'validation_loss_ce': tensor(0.4010, device='cuda:0'), 'validation_loss_bbox': tensor(0.1389, device='cuda:0'), 'validation_loss_giou': tensor(0.4888, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.5797, device='cuda:0'), 'train_loss_ce': tensor(0.4210, device='cuda:0'), 'train_loss_bbox': tensor(0.1568, device='cuda:0'), 'train_loss_giou': tensor(0.6874, device='cuda:0'), 'train_cardinality_error': tensor(1.4000, device='cuda:0'), 'validation_loss': tensor(2.1459, device='cuda:0'), 'validation_loss_ce': tensor(0.4006, device='cuda:0'), 'validation_loss_bbox': tensor(0.1465, device='cuda:0'), 'validation_loss_giou': tensor(0.5065, device='cuda:0'), 'validation_cardinality_error': tensor(0.9394, device='cuda:0')} {'training_loss': tensor(1.9156, device='cuda:0'), 'train_loss_ce': tensor(0.3240, device='cuda:0'), 'train_loss_bbox': tensor(0.1310, device='cuda:0'), 'train_loss_giou': tensor(0.4683, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.2520, device='cuda:0'), 'validation_loss_ce': tensor(0.3980, device='cuda:0'), 'validation_loss_bbox': tensor(0.1614, device='cuda:0'), 'validation_loss_giou': tensor(0.5236, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.9559, device='cuda:0'), 'train_loss_ce': tensor(0.4028, device='cuda:0'), 'train_loss_bbox': tensor(0.2567, device='cuda:0'), 'train_loss_giou': tensor(0.6347, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.4024, device='cuda:0'), 'validation_loss_ce': tensor(0.3812, device='cuda:0'), 'validation_loss_bbox': tensor(0.1705, device='cuda:0'), 'validation_loss_giou': tensor(0.5843, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.1148, device='cuda:0'), 'train_loss_ce': tensor(0.4487, device='cuda:0'), 'train_loss_bbox': tensor(0.1306, device='cuda:0'), 'train_loss_giou': tensor(0.5065, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.2119, device='cuda:0'), 'validation_loss_ce': tensor(0.3946, device='cuda:0'), 'validation_loss_bbox': tensor(0.1521, device='cuda:0'), 'validation_loss_giou': tensor(0.5285, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.6145, device='cuda:0'), 'train_loss_ce': tensor(0.3484, device='cuda:0'), 'train_loss_bbox': tensor(0.0966, device='cuda:0'), 'train_loss_giou': tensor(0.3917, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.2147, device='cuda:0'), 'validation_loss_ce': tensor(0.4000, device='cuda:0'), 'validation_loss_bbox': tensor(0.1524, device='cuda:0'), 'validation_loss_giou': tensor(0.5264, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.4464, device='cuda:0'), 'train_loss_ce': tensor(0.3513, device='cuda:0'), 'train_loss_bbox': tensor(0.1503, device='cuda:0'), 'train_loss_giou': tensor(0.6718, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.0945, device='cuda:0'), 'validation_loss_ce': tensor(0.3839, device='cuda:0'), 'validation_loss_bbox': tensor(0.1390, device='cuda:0'), 'validation_loss_giou': tensor(0.5079, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.1035, device='cuda:0'), 'train_loss_ce': tensor(0.3531, device='cuda:0'), 'train_loss_bbox': tensor(0.1833, device='cuda:0'), 'train_loss_giou': tensor(0.4169, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.0258, device='cuda:0'), 'validation_loss_ce': tensor(0.3667, device='cuda:0'), 'validation_loss_bbox': tensor(0.1385, device='cuda:0'), 'validation_loss_giou': tensor(0.4833, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.8120, device='cuda:0'), 'train_loss_ce': tensor(0.3834, device='cuda:0'), 'train_loss_bbox': tensor(0.1274, device='cuda:0'), 'train_loss_giou': tensor(0.3959, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.0069, device='cuda:0'), 'validation_loss_ce': tensor(0.3738, device='cuda:0'), 'validation_loss_bbox': tensor(0.1400, device='cuda:0'), 'validation_loss_giou': tensor(0.4665, device='cuda:0'), 'validation_cardinality_error': tensor(0.9697, device='cuda:0')} {'training_loss': tensor(1.2792, device='cuda:0'), 'train_loss_ce': tensor(0.3943, device='cuda:0'), 'train_loss_bbox': tensor(0.0620, device='cuda:0'), 'train_loss_giou': tensor(0.2874, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.9124, device='cuda:0'), 'validation_loss_ce': tensor(0.3761, device='cuda:0'), 'validation_loss_bbox': tensor(0.1317, device='cuda:0'), 'validation_loss_giou': tensor(0.4388, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.8847, device='cuda:0'), 'train_loss_ce': tensor(0.3796, device='cuda:0'), 'train_loss_bbox': tensor(0.1281, device='cuda:0'), 'train_loss_giou': tensor(0.4323, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.0097, device='cuda:0'), 'validation_loss_ce': tensor(0.3599, device='cuda:0'), 'validation_loss_bbox': tensor(0.1377, device='cuda:0'), 'validation_loss_giou': tensor(0.4806, device='cuda:0'), 'validation_cardinality_error': tensor(0.6263, device='cuda:0')} ``` ## Examples {'size': tensor([512, 512]), 'image_id': tensor([1]), 'class_labels': tensor([4]), 'boxes': tensor([[0.2622, 0.5729, 0.0847, 0.0773]]), 'area': tensor([1717.9431]), 'iscrowd': tensor([0]), 'orig_size': tensor([1024, 1024])} ![Example](./example.png)	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7" ]
MedicalVision/yolos_tiny_30ep	## Original result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.003 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.008 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.008 ``` ## After training result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.012 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.023 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.011 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.012 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.056 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.118 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.145 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.146 ``` ## Config - dataset: NIH - original model: hustvl/yolos-tiny - lr: 1e-05 - dropout_rate: 0.1 - weight_decay: 0.05 - max_epochs: 30 - train samples: 885 ## Logging ### Training process ``` {'validation_loss': tensor(6.7207, device='cuda:0'), 'validation_loss_ce': tensor(2.1866, device='cuda:0'), 'validation_loss_bbox': tensor(0.5249, device='cuda:0'), 'validation_loss_giou': tensor(0.9547, device='cuda:0'), 'validation_cardinality_error': tensor(98.5312, device='cuda:0')} {'training_loss': tensor(3.7287, device='cuda:0'), 'train_loss_ce': tensor(1.3098, device='cuda:0'), 'train_loss_bbox': tensor(0.2119, device='cuda:0'), 'train_loss_giou': tensor(0.6797, device='cuda:0'), 'train_cardinality_error': tensor(0.8000, device='cuda:0'), 'validation_loss': tensor(3.4332, device='cuda:0'), 'validation_loss_ce': tensor(1.2399, device='cuda:0'), 'validation_loss_bbox': tensor(0.2065, device='cuda:0'), 'validation_loss_giou': tensor(0.5804, device='cuda:0'), 'validation_cardinality_error': tensor(1.0909, device='cuda:0')} {'training_loss': tensor(2.8569, device='cuda:0'), 'train_loss_ce': tensor(0.5845, device='cuda:0'), 'train_loss_bbox': tensor(0.2106, device='cuda:0'), 'train_loss_giou': tensor(0.6097, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.4866, device='cuda:0'), 'validation_loss_ce': tensor(0.5491, device='cuda:0'), 'validation_loss_bbox': tensor(0.1759, device='cuda:0'), 'validation_loss_giou': tensor(0.5290, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.2491, device='cuda:0'), 'train_loss_ce': tensor(0.5182, device='cuda:0'), 'train_loss_bbox': tensor(0.1662, device='cuda:0'), 'train_loss_giou': tensor(0.4500, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.3079, device='cuda:0'), 'validation_loss_ce': tensor(0.4791, device='cuda:0'), 'validation_loss_bbox': tensor(0.1619, device='cuda:0'), 'validation_loss_giou': tensor(0.5096, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.9377, device='cuda:0'), 'train_loss_ce': tensor(0.5210, device='cuda:0'), 'train_loss_bbox': tensor(0.1273, device='cuda:0'), 'train_loss_giou': tensor(0.3902, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.2293, device='cuda:0'), 'validation_loss_ce': tensor(0.4691, device='cuda:0'), 'validation_loss_bbox': tensor(0.1544, device='cuda:0'), 'validation_loss_giou': tensor(0.4940, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.1924, device='cuda:0'), 'train_loss_ce': tensor(0.5255, device='cuda:0'), 'train_loss_bbox': tensor(0.1414, device='cuda:0'), 'train_loss_giou': tensor(0.4800, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.1471, device='cuda:0'), 'validation_loss_ce': tensor(0.4648, 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'validation_loss_giou': tensor(0.4634, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.4117, device='cuda:0'), 'train_loss_ce': tensor(0.4587, device='cuda:0'), 'train_loss_bbox': tensor(0.0724, device='cuda:0'), 'train_loss_giou': tensor(0.2954, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.9528, device='cuda:0'), 'validation_loss_ce': tensor(0.4217, device='cuda:0'), 'validation_loss_bbox': tensor(0.1242, device='cuda:0'), 'validation_loss_giou': tensor(0.4550, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.4619, device='cuda:0'), 'train_loss_ce': tensor(0.3838, device='cuda:0'), 'train_loss_bbox': tensor(0.0605, device='cuda:0'), 'train_loss_giou': tensor(0.3878, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.0346, device='cuda:0'), 'validation_loss_ce': tensor(0.4194, device='cuda:0'), 'validation_loss_bbox': tensor(0.1323, device='cuda:0'), 'validation_loss_giou': tensor(0.4769, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.2125, device='cuda:0'), 'train_loss_ce': tensor(0.3469, device='cuda:0'), 'train_loss_bbox': tensor(0.0725, device='cuda:0'), 'train_loss_giou': tensor(0.2517, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.9540, device='cuda:0'), 'validation_loss_ce': tensor(0.4169, device='cuda:0'), 'validation_loss_bbox': tensor(0.1248, device='cuda:0'), 'validation_loss_giou': tensor(0.4566, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.1738, device='cuda:0'), 'train_loss_ce': tensor(0.4249, device='cuda:0'), 'train_loss_bbox': tensor(0.0353, device='cuda:0'), 'train_loss_giou': tensor(0.2863, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.9534, device='cuda:0'), 'validation_loss_ce': tensor(0.4152, device='cuda:0'), 'validation_loss_bbox': tensor(0.1264, device='cuda:0'), 'validation_loss_giou': tensor(0.4531, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.2029, device='cuda:0'), 'train_loss_ce': tensor(0.4904, device='cuda:0'), 'train_loss_bbox': tensor(0.0577, device='cuda:0'), 'train_loss_giou': tensor(0.2119, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(1.9811, device='cuda:0'), 'validation_loss_ce': tensor(0.4117, device='cuda:0'), 'validation_loss_bbox': tensor(0.1291, device='cuda:0'), 'validation_loss_giou': tensor(0.4620, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} ``` ## Examples {'size': tensor([512, 512]), 'image_id': tensor([1]), 'class_labels': tensor([4]), 'boxes': tensor([[0.2622, 0.5729, 0.0847, 0.0773]]), 'area': tensor([1717.9431]), 'iscrowd': tensor([0]), 'orig_size': tensor([1024, 1024])} ![Example](./example.png)	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7" ]
MedicalVision/yolos_mini_overfitting	## Original result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.000 ``` ## After training result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.900 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 1.000 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.900 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.900 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.900 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.900 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.900 ``` ## Config - dataset: NIH - original model: hustvl/yolos-tiny - lr: 0.0001 - dropout_rate: 0.1 - weight_decay: 0.0001 - max_epochs: 300 - train samples: 2 ## Logging ### Training process ``` {'validation_loss': tensor(8.3559, device='cuda:0'), 'validation_loss_ce': tensor(2.1036, device='cuda:0'), 'validation_loss_bbox': tensor(0.7321, device='cuda:0'), 'validation_loss_giou': tensor(1.2958, device='cuda:0'), 'validation_cardinality_error': tensor(77.5000, device='cuda:0')} {'training_loss': tensor(8.3559, device='cuda:0'), 'train_loss_ce': tensor(2.1036, device='cuda:0'), 'train_loss_bbox': tensor(0.7321, device='cuda:0'), 'train_loss_giou': tensor(1.2958, device='cuda:0'), 'train_cardinality_error': tensor(77.5000, device='cuda:0'), 'validation_loss': tensor(6.2559, device='cuda:0'), 'validation_loss_ce': tensor(1.6991, device='cuda:0'), 'validation_loss_bbox': tensor(0.4806, device='cuda:0'), 'validation_loss_giou': tensor(1.0768, device='cuda:0'), 'validation_cardinality_error': tensor(16., device='cuda:0')} {'training_loss': tensor(6.2559, device='cuda:0'), 'train_loss_ce': tensor(1.6991, device='cuda:0'), 'train_loss_bbox': tensor(0.4806, device='cuda:0'), 'train_loss_giou': tensor(1.0768, device='cuda:0'), 'train_cardinality_error': tensor(16., device='cuda:0'), 'validation_loss': tensor(3.7908, device='cuda:0'), 'validation_loss_ce': tensor(1.6682, device='cuda:0'), 'validation_loss_bbox': tensor(0.1846, device='cuda:0'), 'validation_loss_giou': tensor(0.5999, device='cuda:0'), 'validation_cardinality_error': tensor(14., device='cuda:0')} {'training_loss': tensor(3.7908, device='cuda:0'), 'train_loss_ce': tensor(1.6682, device='cuda:0'), 'train_loss_bbox': tensor(0.1846, device='cuda:0'), 'train_loss_giou': tensor(0.5999, device='cuda:0'), 'train_cardinality_error': tensor(14., device='cuda:0'), 'validation_loss': tensor(3.5762, device='cuda:0'), 'validation_loss_ce': tensor(1.6408, device='cuda:0'), 'validation_loss_bbox': tensor(0.1302, device='cuda:0'), 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device='cuda:0')} {'training_loss': tensor(0.1628, device='cuda:0'), 'train_loss_ce': tensor(0.0033, device='cuda:0'), 'train_loss_bbox': tensor(0.0071, device='cuda:0'), 'train_loss_giou': tensor(0.0619, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.2424, device='cuda:0'), 'validation_loss_ce': tensor(0.0033, device='cuda:0'), 'validation_loss_bbox': tensor(0.0100, device='cuda:0'), 'validation_loss_giou': tensor(0.0945, device='cuda:0'), 'validation_cardinality_error': tensor(0., device='cuda:0')} {'training_loss': tensor(0.2424, device='cuda:0'), 'train_loss_ce': tensor(0.0033, device='cuda:0'), 'train_loss_bbox': tensor(0.0100, device='cuda:0'), 'train_loss_giou': tensor(0.0945, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.3781, device='cuda:0'), 'validation_loss_ce': tensor(0.0033, device='cuda:0'), 'validation_loss_bbox': tensor(0.0131, device='cuda:0'), 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device='cuda:0'), 'validation_loss': tensor(0.2871, device='cuda:0'), 'validation_loss_ce': tensor(0.0033, device='cuda:0'), 'validation_loss_bbox': tensor(0.0123, device='cuda:0'), 'validation_loss_giou': tensor(0.1112, device='cuda:0'), 'validation_cardinality_error': tensor(0., device='cuda:0')} {'training_loss': tensor(0.2871, device='cuda:0'), 'train_loss_ce': tensor(0.0033, device='cuda:0'), 'train_loss_bbox': tensor(0.0123, device='cuda:0'), 'train_loss_giou': tensor(0.1112, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.2668, device='cuda:0'), 'validation_loss_ce': tensor(0.0033, device='cuda:0'), 'validation_loss_bbox': tensor(0.0117, device='cuda:0'), 'validation_loss_giou': tensor(0.1025, device='cuda:0'), 'validation_cardinality_error': tensor(0., device='cuda:0')} {'training_loss': tensor(0.2668, device='cuda:0'), 'train_loss_ce': tensor(0.0033, device='cuda:0'), 'train_loss_bbox': tensor(0.0117, device='cuda:0'), 'train_loss_giou': tensor(0.1025, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.1305, device='cuda:0'), 'validation_loss_ce': tensor(0.0032, device='cuda:0'), 'validation_loss_bbox': tensor(0.0065, device='cuda:0'), 'validation_loss_giou': tensor(0.0475, device='cuda:0'), 'validation_cardinality_error': tensor(0., device='cuda:0')} {'training_loss': tensor(0.1305, device='cuda:0'), 'train_loss_ce': tensor(0.0032, device='cuda:0'), 'train_loss_bbox': tensor(0.0065, device='cuda:0'), 'train_loss_giou': tensor(0.0475, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.2405, device='cuda:0'), 'validation_loss_ce': tensor(0.0031, device='cuda:0'), 'validation_loss_bbox': tensor(0.0114, device='cuda:0'), 'validation_loss_giou': tensor(0.0903, device='cuda:0'), 'validation_cardinality_error': tensor(0., device='cuda:0')} ``` ## Examples {'size': tensor([512, 512]), 'image_id': tensor([735]), 'class_labels': tensor([5]), 'boxes': tensor([[0.7651, 0.5307, 0.0952, 0.0942]]), 'area': tensor([2351.3042]), 'iscrowd': tensor([0]), 'orig_size': tensor([1024, 1024])} ![Example](./example.png)	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7" ]
mrdbourke/detr_finetuned_trashify_box_detector_synthetic_and_real_data	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MedicalVision/yolos_tiny_300ep	## Original result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.001 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.004 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.004 ``` ## After training result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.016 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.028 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.020 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.016 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.068 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.123 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.135 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.136 ``` ## Config - dataset: NIH - original model: hustvl/yolos-tiny - lr: 5e-06 - dropout_rate: 0.1 - weight_decay: 0.05 - max_epochs: 300 - train samples: 885 ## Logging ### Training process ``` {'validation_loss': tensor(6.3551, device='cuda:0'), 'validation_loss_ce': tensor(2.0319, device='cuda:0'), 'validation_loss_bbox': tensor(0.4981, device='cuda:0'), 'validation_loss_giou': tensor(0.9164, device='cuda:0'), 'validation_cardinality_error': tensor(96., device='cuda:0')} {'training_loss': tensor(3.8686, device='cuda:0'), 'train_loss_ce': tensor(1.4275, device='cuda:0'), 'train_loss_bbox': tensor(0.2086, device='cuda:0'), 'train_loss_giou': tensor(0.6991, device='cuda:0'), 'train_cardinality_error': tensor(8.2000, device='cuda:0'), 'validation_loss': tensor(3.8399, device='cuda:0'), 'validation_loss_ce': tensor(1.3733, device='cuda:0'), 'validation_loss_bbox': tensor(0.2313, device='cuda:0'), 'validation_loss_giou': tensor(0.6552, device='cuda:0'), 'validation_cardinality_error': tensor(4.0505, device='cuda:0')} {'training_loss': tensor(3.0059, device='cuda:0'), 'train_loss_ce': tensor(0.8301, device='cuda:0'), 'train_loss_bbox': tensor(0.2366, device='cuda:0'), 'train_loss_giou': tensor(0.4965, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(3.0026, device='cuda:0'), 'validation_loss_ce': tensor(0.8374, device='cuda:0'), 'validation_loss_bbox': tensor(0.1967, device='cuda:0'), 'validation_loss_giou': tensor(0.5910, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(3.2795, device='cuda:0'), 'train_loss_ce': tensor(0.5039, device='cuda:0'), 'train_loss_bbox': tensor(0.2495, device='cuda:0'), 'train_loss_giou': tensor(0.7642, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.7219, device='cuda:0'), 'validation_loss_ce': tensor(0.6121, device='cuda:0'), 'validation_loss_bbox': tensor(0.1947, device='cuda:0'), 'validation_loss_giou': 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'validation_loss_bbox': tensor(0.1350, device='cuda:0'), 'validation_loss_giou': tensor(0.4985, device='cuda:0'), 'validation_cardinality_error': tensor(0.9697, device='cuda:0')} {'training_loss': tensor(0.4286, device='cuda:0'), 'train_loss_ce': tensor(0.2678, device='cuda:0'), 'train_loss_bbox': tensor(0.0076, device='cuda:0'), 'train_loss_giou': tensor(0.0615, device='cuda:0'), 'train_cardinality_error': tensor(0.8000, device='cuda:0'), 'validation_loss': tensor(2.0223, device='cuda:0'), 'validation_loss_ce': tensor(0.3601, device='cuda:0'), 'validation_loss_bbox': tensor(0.1343, device='cuda:0'), 'validation_loss_giou': tensor(0.4953, device='cuda:0'), 'validation_cardinality_error': tensor(0.9091, device='cuda:0')} {'training_loss': tensor(0.4372, device='cuda:0'), 'train_loss_ce': tensor(0.2876, device='cuda:0'), 'train_loss_bbox': tensor(0.0120, device='cuda:0'), 'train_loss_giou': tensor(0.0449, device='cuda:0'), 'train_cardinality_error': tensor(1.2000, device='cuda:0'), 'validation_loss': tensor(2.0332, device='cuda:0'), 'validation_loss_ce': tensor(0.3591, device='cuda:0'), 'validation_loss_bbox': tensor(0.1348, device='cuda:0'), 'validation_loss_giou': tensor(0.4999, device='cuda:0'), 'validation_cardinality_error': tensor(0.9394, device='cuda:0')} {'training_loss': tensor(0.4195, device='cuda:0'), 'train_loss_ce': tensor(0.2297, device='cuda:0'), 'train_loss_bbox': tensor(0.0168, device='cuda:0'), 'train_loss_giou': tensor(0.0530, device='cuda:0'), 'train_cardinality_error': tensor(0.6000, device='cuda:0'), 'validation_loss': tensor(2.0240, device='cuda:0'), 'validation_loss_ce': tensor(0.3607, device='cuda:0'), 'validation_loss_bbox': tensor(0.1351, device='cuda:0'), 'validation_loss_giou': tensor(0.4939, device='cuda:0'), 'validation_cardinality_error': tensor(0.9394, device='cuda:0')} {'training_loss': tensor(0.4228, device='cuda:0'), 'train_loss_ce': tensor(0.2680, device='cuda:0'), 'train_loss_bbox': tensor(0.0133, device='cuda:0'), 'train_loss_giou': tensor(0.0442, device='cuda:0'), 'train_cardinality_error': tensor(0.8000, device='cuda:0'), 'validation_loss': tensor(2.0364, device='cuda:0'), 'validation_loss_ce': tensor(0.3581, device='cuda:0'), 'validation_loss_bbox': tensor(0.1353, device='cuda:0'), 'validation_loss_giou': tensor(0.5009, device='cuda:0'), 'validation_cardinality_error': tensor(0.9394, device='cuda:0')} {'training_loss': tensor(0.5344, device='cuda:0'), 'train_loss_ce': tensor(0.3058, device='cuda:0'), 'train_loss_bbox': tensor(0.0090, device='cuda:0'), 'train_loss_giou': tensor(0.0917, device='cuda:0'), 'train_cardinality_error': tensor(0.8000, device='cuda:0'), 'validation_loss': tensor(2.0196, device='cuda:0'), 'validation_loss_ce': tensor(0.3588, device='cuda:0'), 'validation_loss_bbox': tensor(0.1336, device='cuda:0'), 'validation_loss_giou': tensor(0.4964, device='cuda:0'), 'validation_cardinality_error': tensor(0.9394, device='cuda:0')} ``` ## Examples {'size': tensor([512, 512]), 'image_id': tensor([1]), 'class_labels': tensor([4]), 'boxes': tensor([[0.2622, 0.5729, 0.0847, 0.0773]]), 'area': tensor([1717.9431]), 'iscrowd': tensor([0]), 'orig_size': tensor([1024, 1024])} ![Example](./example.png)	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7" ]
MedicalVision/yolos_tiny_500ep	## Original result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.004 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.005 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.005 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.005 ``` ## After training result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.031 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.061 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.026 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.031 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.063 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.097 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.110 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.112 ``` ## Config - dataset: NIH - original model: hustvl/yolos-tiny - lr: 5e-06 - dropout_rate: 0.1 - weight_decay: 0.05 - max_epochs: 500 - train samples: 885 ## Logging ### Training process ``` {'validation_loss': tensor(6.6117, device='cuda:0'), 'validation_loss_ce': tensor(1.8345, device='cuda:0'), 'validation_loss_bbox': tensor(0.5845, device='cuda:0'), 'validation_loss_giou': tensor(0.9274, device='cuda:0'), 'validation_cardinality_error': tensor(16.9062, device='cuda:0')} {'training_loss': tensor(3.7877, device='cuda:0'), 'train_loss_ce': tensor(1.4007, device='cuda:0'), 'train_loss_bbox': tensor(0.2434, device='cuda:0'), 'train_loss_giou': tensor(0.5849, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(3.8850, device='cuda:0'), 'validation_loss_ce': tensor(1.3982, device='cuda:0'), 'validation_loss_bbox': tensor(0.2301, device='cuda:0'), 'validation_loss_giou': tensor(0.6682, device='cuda:0'), 'validation_cardinality_error': tensor(1.3737, device='cuda:0')} {'training_loss': tensor(2.6493, device='cuda:0'), 'train_loss_ce': tensor(0.9938, device='cuda:0'), 'train_loss_bbox': tensor(0.1606, device='cuda:0'), 'train_loss_giou': tensor(0.4261, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(3.0517, device='cuda:0'), 'validation_loss_ce': tensor(0.9154, device='cuda:0'), 'validation_loss_bbox': tensor(0.1932, device='cuda:0'), 'validation_loss_giou': tensor(0.5851, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.3105, device='cuda:0'), 'train_loss_ce': tensor(0.6409, device='cuda:0'), 'train_loss_bbox': tensor(0.1369, device='cuda:0'), 'train_loss_giou': tensor(0.4925, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.6600, device='cuda:0'), 'validation_loss_ce': tensor(0.6187, device='cuda:0'), 'validation_loss_bbox': tensor(0.1830, device='cuda:0'), 'validation_loss_giou': 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'train_loss_giou': tensor(0.0294, device='cuda:0'), 'train_cardinality_error': tensor(0.6000, device='cuda:0'), 'validation_loss': tensor(2.1745, device='cuda:0'), 'validation_loss_ce': tensor(0.4750, device='cuda:0'), 'validation_loss_bbox': tensor(0.1467, device='cuda:0'), 'validation_loss_giou': tensor(0.4831, device='cuda:0'), 'validation_cardinality_error': tensor(1.1010, device='cuda:0')} {'training_loss': tensor(0.3083, device='cuda:0'), 'train_loss_ce': tensor(0.1908, device='cuda:0'), 'train_loss_bbox': tensor(0.0093, device='cuda:0'), 'train_loss_giou': tensor(0.0356, device='cuda:0'), 'train_cardinality_error': tensor(0.6000, device='cuda:0'), 'validation_loss': tensor(2.1796, device='cuda:0'), 'validation_loss_ce': tensor(0.4741, device='cuda:0'), 'validation_loss_bbox': tensor(0.1463, device='cuda:0'), 'validation_loss_giou': tensor(0.4870, device='cuda:0'), 'validation_cardinality_error': tensor(1.0606, device='cuda:0')} {'training_loss': tensor(0.2276, device='cuda:0'), 'train_loss_ce': tensor(0.1011, device='cuda:0'), 'train_loss_bbox': tensor(0.0105, device='cuda:0'), 'train_loss_giou': tensor(0.0369, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.1805, device='cuda:0'), 'validation_loss_ce': tensor(0.4705, device='cuda:0'), 'validation_loss_bbox': tensor(0.1456, device='cuda:0'), 'validation_loss_giou': tensor(0.4911, device='cuda:0'), 'validation_cardinality_error': tensor(1.1818, device='cuda:0')} {'training_loss': tensor(0.1932, device='cuda:0'), 'train_loss_ce': tensor(0.1009, device='cuda:0'), 'train_loss_bbox': tensor(0.0068, device='cuda:0'), 'train_loss_giou': tensor(0.0292, device='cuda:0'), 'train_cardinality_error': tensor(0.4000, device='cuda:0'), 'validation_loss': tensor(2.1712, device='cuda:0'), 'validation_loss_ce': tensor(0.4756, device='cuda:0'), 'validation_loss_bbox': tensor(0.1448, device='cuda:0'), 'validation_loss_giou': tensor(0.4859, device='cuda:0'), 'validation_cardinality_error': tensor(1.1616, device='cuda:0')} {'training_loss': tensor(0.5669, device='cuda:0'), 'train_loss_ce': tensor(0.1689, device='cuda:0'), 'train_loss_bbox': tensor(0.0165, device='cuda:0'), 'train_loss_giou': tensor(0.1577, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.2115, device='cuda:0'), 'validation_loss_ce': tensor(0.4918, device='cuda:0'), 'validation_loss_bbox': tensor(0.1488, device='cuda:0'), 'validation_loss_giou': tensor(0.4879, device='cuda:0'), 'validation_cardinality_error': tensor(1.0303, device='cuda:0')} {'training_loss': tensor(0.3650, device='cuda:0'), 'train_loss_ce': tensor(0.0755, device='cuda:0'), 'train_loss_bbox': tensor(0.0118, device='cuda:0'), 'train_loss_giou': tensor(0.1153, device='cuda:0'), 'train_cardinality_error': tensor(1.2000, device='cuda:0'), 'validation_loss': tensor(2.1864, device='cuda:0'), 'validation_loss_ce': tensor(0.4784, device='cuda:0'), 'validation_loss_bbox': tensor(0.1473, device='cuda:0'), 'validation_loss_giou': tensor(0.4857, device='cuda:0'), 'validation_cardinality_error': tensor(1.1010, device='cuda:0')} {'training_loss': tensor(0.1495, device='cuda:0'), 'train_loss_ce': tensor(0.0437, device='cuda:0'), 'train_loss_bbox': tensor(0.0076, device='cuda:0'), 'train_loss_giou': tensor(0.0338, device='cuda:0'), 'train_cardinality_error': tensor(0.4000, device='cuda:0'), 'validation_loss': tensor(2.2058, device='cuda:0'), 'validation_loss_ce': tensor(0.4909, device='cuda:0'), 'validation_loss_bbox': tensor(0.1483, device='cuda:0'), 'validation_loss_giou': tensor(0.4868, device='cuda:0'), 'validation_cardinality_error': tensor(1.1010, device='cuda:0')} {'training_loss': tensor(0.3046, device='cuda:0'), 'train_loss_ce': tensor(0.1153, device='cuda:0'), 'train_loss_bbox': tensor(0.0126, device='cuda:0'), 'train_loss_giou': tensor(0.0632, device='cuda:0'), 'train_cardinality_error': tensor(0.8000, device='cuda:0'), 'validation_loss': tensor(2.1851, device='cuda:0'), 'validation_loss_ce': tensor(0.4859, device='cuda:0'), 'validation_loss_bbox': tensor(0.1462, device='cuda:0'), 'validation_loss_giou': tensor(0.4842, device='cuda:0'), 'validation_cardinality_error': tensor(1.1212, device='cuda:0')} {'training_loss': tensor(0.2372, device='cuda:0'), 'train_loss_ce': tensor(0.1029, device='cuda:0'), 'train_loss_bbox': tensor(0.0122, device='cuda:0'), 'train_loss_giou': tensor(0.0366, device='cuda:0'), 'train_cardinality_error': tensor(0.6000, device='cuda:0'), 'validation_loss': tensor(2.1929, device='cuda:0'), 'validation_loss_ce': tensor(0.4953, device='cuda:0'), 'validation_loss_bbox': tensor(0.1481, device='cuda:0'), 'validation_loss_giou': tensor(0.4785, device='cuda:0'), 'validation_cardinality_error': tensor(1.0505, device='cuda:0')} {'training_loss': tensor(0.1698, device='cuda:0'), 'train_loss_ce': tensor(0.0754, device='cuda:0'), 'train_loss_bbox': tensor(0.0077, device='cuda:0'), 'train_loss_giou': tensor(0.0280, device='cuda:0'), 'train_cardinality_error': tensor(1.6000, device='cuda:0'), 'validation_loss': tensor(2.1856, device='cuda:0'), 'validation_loss_ce': tensor(0.4731, device='cuda:0'), 'validation_loss_bbox': tensor(0.1450, device='cuda:0'), 'validation_loss_giou': tensor(0.4938, device='cuda:0'), 'validation_cardinality_error': tensor(1.1313, device='cuda:0')} {'training_loss': tensor(0.3025, device='cuda:0'), 'train_loss_ce': tensor(0.1464, device='cuda:0'), 'train_loss_bbox': tensor(0.0105, device='cuda:0'), 'train_loss_giou': tensor(0.0517, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.2047, device='cuda:0'), 'validation_loss_ce': tensor(0.4956, device='cuda:0'), 'validation_loss_bbox': tensor(0.1479, device='cuda:0'), 'validation_loss_giou': tensor(0.4847, device='cuda:0'), 'validation_cardinality_error': tensor(1.1313, device='cuda:0')} {'training_loss': tensor(0.2424, device='cuda:0'), 'train_loss_ce': tensor(0.1020, device='cuda:0'), 'train_loss_bbox': tensor(0.0097, device='cuda:0'), 'train_loss_giou': tensor(0.0459, device='cuda:0'), 'train_cardinality_error': tensor(0.8000, device='cuda:0'), 'validation_loss': tensor(2.1770, device='cuda:0'), 'validation_loss_ce': tensor(0.4897, device='cuda:0'), 'validation_loss_bbox': tensor(0.1448, device='cuda:0'), 'validation_loss_giou': tensor(0.4816, device='cuda:0'), 'validation_cardinality_error': tensor(1.1919, device='cuda:0')} {'training_loss': tensor(0.1836, device='cuda:0'), 'train_loss_ce': tensor(0.0903, device='cuda:0'), 'train_loss_bbox': tensor(0.0073, device='cuda:0'), 'train_loss_giou': tensor(0.0285, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.2057, device='cuda:0'), 'validation_loss_ce': tensor(0.4968, device='cuda:0'), 'validation_loss_bbox': tensor(0.1479, device='cuda:0'), 'validation_loss_giou': tensor(0.4847, device='cuda:0'), 'validation_cardinality_error': tensor(1.1010, device='cuda:0')} ``` ## Examples {'size': tensor([512, 512]), 'image_id': tensor([1]), 'class_labels': tensor([4]), 'boxes': tensor([[0.2622, 0.5729, 0.0847, 0.0773]]), 'area': tensor([1717.9431]), 'iscrowd': tensor([0]), 'orig_size': tensor([1024, 1024])} ![Example](./example.png)	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7" ]
srv-sh007/table_structure_recognition	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "label_0", "label_1", "label_2", "label_3", "label_4" ]
MedicalVision/yolos_tiny_600ep_overfitting	## Original result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.001 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.001 ``` ## After training result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.826 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.867 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.864 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.716 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.844 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.841 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.844 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.844 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.750 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.854 ``` ## Config - dataset: NIH - original model: hustvl/yolos-tiny - lr: 1e-05 - dropout_rate: 0.1 - weight_decay: 0.001 - max_epochs: 600 - train samples: 354 ## Logging ### Training process ``` {'validation_loss': tensor(7.1177, device='cuda:0'), 'validation_loss_ce': tensor(2.3438, device='cuda:0'), 'validation_loss_bbox': tensor(0.5517, device='cuda:0'), 'validation_loss_giou': tensor(1.0076, device='cuda:0'), 'validation_cardinality_error': tensor(98.9375, device='cuda:0')} {'training_loss': tensor(4.8380, device='cuda:0'), 'train_loss_ce': tensor(1.9076, device='cuda:0'), 'train_loss_bbox': tensor(0.2720, device='cuda:0'), 'train_loss_giou': tensor(0.7852, device='cuda:0'), 'train_cardinality_error': tensor(40.5000, device='cuda:0'), 'validation_loss': tensor(4.6572, device='cuda:0'), 'validation_loss_ce': tensor(1.9323, device='cuda:0'), 'validation_loss_bbox': tensor(0.2644, device='cuda:0'), 'validation_loss_giou': tensor(0.7016, device='cuda:0'), 'validation_cardinality_error': tensor(64.3898, device='cuda:0')} {'training_loss': tensor(4.6511, device='cuda:0'), 'train_loss_ce': tensor(1.7157, device='cuda:0'), 'train_loss_bbox': tensor(0.2469, device='cuda:0'), 'train_loss_giou': tensor(0.8504, device='cuda:0'), 'train_cardinality_error': tensor(18.5000, device='cuda:0'), 'validation_loss': tensor(3.6326, device='cuda:0'), 'validation_loss_ce': tensor(1.5263, device='cuda:0'), 'validation_loss_bbox': tensor(0.1872, device='cuda:0'), 'validation_loss_giou': tensor(0.5851, device='cuda:0'), 'validation_cardinality_error': tensor(5.3277, device='cuda:0')} {'training_loss': tensor(4.4295, device='cuda:0'), 'train_loss_ce': tensor(1.1966, device='cuda:0'), 'train_loss_bbox': tensor(0.2759, device='cuda:0'), 'train_loss_giou': tensor(0.9268, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.9645, device='cuda:0'), 'validation_loss_ce': tensor(1.0482, device='cuda:0'), 'validation_loss_bbox': tensor(0.1677, device='cuda:0'), 'validation_loss_giou': tensor(0.5388, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(3.3436, device='cuda:0'), 'train_loss_ce': tensor(0.7888, device='cuda:0'), 'train_loss_bbox': tensor(0.2518, device='cuda:0'), 'train_loss_giou': tensor(0.6480, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.5727, device='cuda:0'), 'validation_loss_ce': tensor(0.7218, device='cuda:0'), 'validation_loss_bbox': tensor(0.1607, device='cuda:0'), 'validation_loss_giou': tensor(0.5237, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(1.6320, device='cuda:0'), 'train_loss_ce': tensor(0.5612, device='cuda:0'), 'train_loss_bbox': tensor(0.0874, device='cuda:0'), 'train_loss_giou': tensor(0.3170, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.3218, device='cuda:0'), 'validation_loss_ce': tensor(0.5700, device='cuda:0'), 'validation_loss_bbox': tensor(0.1488, device='cuda:0'), 'validation_loss_giou': tensor(0.5038, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(3.9124, device='cuda:0'), 'train_loss_ce': tensor(0.5738, device='cuda:0'), 'train_loss_bbox': tensor(0.3125, device='cuda:0'), 'train_loss_giou': tensor(0.8880, device='cuda:0'), 'train_cardinality_error': tensor(1., device='cuda:0'), 'validation_loss': tensor(2.2307, device='cuda:0'), 'validation_loss_ce': tensor(0.5038, device='cuda:0'), 'validation_loss_bbox': tensor(0.1460, device='cuda:0'), 'validation_loss_giou': tensor(0.4984, device='cuda:0'), 'validation_cardinality_error': tensor(1., device='cuda:0')} {'training_loss': tensor(2.0382, device='cuda:0'), 'train_loss_ce': tensor(0.4818, device='cuda:0'), 'train_loss_bbox': tensor(0.0997, device='cuda:0'), 'train_loss_giou': tensor(0.5291, device='cuda:0'), 'train_cardinality_error': tensor(1., 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'validation_loss': tensor(0.1297, device='cuda:0'), 'validation_loss_ce': tensor(0.0075, device='cuda:0'), 'validation_loss_bbox': tensor(0.0075, device='cuda:0'), 'validation_loss_giou': tensor(0.0424, device='cuda:0'), 'validation_cardinality_error': tensor(0.1949, device='cuda:0')} {'training_loss': tensor(0.0963, device='cuda:0'), 'train_loss_ce': tensor(0.0077, device='cuda:0'), 'train_loss_bbox': tensor(0.0100, device='cuda:0'), 'train_loss_giou': tensor(0.0192, device='cuda:0'), 'train_cardinality_error': tensor(0.5000, device='cuda:0'), 'validation_loss': tensor(0.1407, device='cuda:0'), 'validation_loss_ce': tensor(0.0077, device='cuda:0'), 'validation_loss_bbox': tensor(0.0081, device='cuda:0'), 'validation_loss_giou': tensor(0.0462, device='cuda:0'), 'validation_cardinality_error': tensor(0.1949, device='cuda:0')} {'training_loss': tensor(0.0949, device='cuda:0'), 'train_loss_ce': tensor(0.0038, device='cuda:0'), 'train_loss_bbox': tensor(0.0049, device='cuda:0'), 'train_loss_giou': tensor(0.0332, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.1268, device='cuda:0'), 'validation_loss_ce': tensor(0.0078, device='cuda:0'), 'validation_loss_bbox': tensor(0.0072, device='cuda:0'), 'validation_loss_giou': tensor(0.0416, device='cuda:0'), 'validation_cardinality_error': tensor(0.2062, device='cuda:0')} {'training_loss': tensor(0.1065, device='cuda:0'), 'train_loss_ce': tensor(0.0031, device='cuda:0'), 'train_loss_bbox': tensor(0.0098, device='cuda:0'), 'train_loss_giou': tensor(0.0271, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.1237, device='cuda:0'), 'validation_loss_ce': tensor(0.0076, device='cuda:0'), 'validation_loss_bbox': tensor(0.0068, device='cuda:0'), 'validation_loss_giou': tensor(0.0411, device='cuda:0'), 'validation_cardinality_error': tensor(0.1723, device='cuda:0')} {'training_loss': tensor(0.0699, device='cuda:0'), 'train_loss_ce': tensor(0.0079, device='cuda:0'), 'train_loss_bbox': tensor(0.0044, device='cuda:0'), 'train_loss_giou': tensor(0.0199, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.1210, device='cuda:0'), 'validation_loss_ce': tensor(0.0077, device='cuda:0'), 'validation_loss_bbox': tensor(0.0070, device='cuda:0'), 'validation_loss_giou': tensor(0.0393, device='cuda:0'), 'validation_cardinality_error': tensor(0.2203, device='cuda:0')} {'training_loss': tensor(0.0682, device='cuda:0'), 'train_loss_ce': tensor(0.0051, device='cuda:0'), 'train_loss_bbox': tensor(0.0059, device='cuda:0'), 'train_loss_giou': tensor(0.0168, device='cuda:0'), 'train_cardinality_error': tensor(0., device='cuda:0'), 'validation_loss': tensor(0.1295, device='cuda:0'), 'validation_loss_ce': tensor(0.0075, device='cuda:0'), 'validation_loss_bbox': tensor(0.0073, device='cuda:0'), 'validation_loss_giou': tensor(0.0427, device='cuda:0'), 'validation_cardinality_error': tensor(0.1695, device='cuda:0')} {'training_loss': tensor(0.0636, device='cuda:0'), 'train_loss_ce': tensor(0.0103, device='cuda:0'), 'train_loss_bbox': tensor(0.0037, device='cuda:0'), 'train_loss_giou': tensor(0.0173, device='cuda:0'), 'train_cardinality_error': tensor(0.5000, device='cuda:0'), 'validation_loss': tensor(0.1114, device='cuda:0'), 'validation_loss_ce': tensor(0.0071, device='cuda:0'), 'validation_loss_bbox': tensor(0.0064, device='cuda:0'), 'validation_loss_giou': tensor(0.0360, device='cuda:0'), 'validation_cardinality_error': tensor(0.2006, device='cuda:0')} ``` ## Examples {'size': tensor([512, 512]), 'image_id': tensor([1]), 'class_labels': tensor([4]), 'boxes': tensor([[0.2622, 0.5729, 0.0847, 0.0773]]), 'area': tensor([1717.9431]), 'iscrowd': tensor([0]), 'orig_size': tensor([1024, 1024])} ![Example](./example.png)	[ "label_0", "label_1", "label_2", "label_3", "label_4", "label_5", "label_6", "label_7" ]
Divyanshu97/table-transformer-structure-recognition-bank-statement	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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MarianaMCruz/detr-finetuned-cppe-5-10k-steps	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-finetuned-cppe-5-10k-steps This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset. It achieves the following results on the evaluation set: - Loss: 1.2564 - Map: 0.2948 - Map 50: 0.5884 - Map 75: 0.2425 - Map Coverall: 0.55 - Map Face Shield: 0.2939 - Map Gloves: 0.1976 - Map Goggles: 0.1503 - Map Mask: 0.282 - Map Large: 0.4679 - Map Medium: 0.2362 - Map Small: 0.0834 - Mar 1: 0.2966 - Mar 10: 0.4598 - Mar 100: 0.4716 - Mar 100 Coverall: 0.6766 - Mar 100 Face Shield: 0.4987 - Mar 100 Gloves: 0.3946 - Mar 100 Goggles: 0.3846 - Mar 100 Mask: 0.4036 - Mar Large: 0.6584 - Mar Medium: 0.4137 - Mar Small: 0.1785 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Coverall \| Map Face Shield \| Map Gloves \| Map Goggles \| Map Mask \| Map Large \| Map Medium \| Map Small \| Mar 1 \| Mar 10 \| Mar 100 \| Mar 100 Coverall \| Mar 100 Face Shield \| Mar 100 Gloves \| Mar 100 Goggles \| Mar 100 Mask \| Mar Large \| Mar Medium \| Mar Small \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:------------:\|:---------------:\|:----------:\|:-----------:\|:--------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:----------------:\|:-------------------:\|:--------------:\|:---------------:\|:------------:\|:---------:\|:----------:\|:---------:\| \| 2.8255 \| 1.0 \| 107 \| 2.4646 \| 0.0262 \| 0.0647 \| 0.0201 \| 0.1126 \| 0.0 \| 0.0104 \| 0.0 \| 0.008 \| 0.0299 \| 0.0089 \| 0.0038 \| 0.0468 \| 0.1244 \| 0.1694 \| 0.4752 \| 0.0 \| 0.1433 \| 0.0 \| 0.2284 \| 0.1707 \| 0.1465 \| 0.07 \| \| 2.4162 \| 2.0 \| 214 \| 2.6974 \| 0.0172 \| 0.0482 \| 0.0105 \| 0.0761 \| 0.0 \| 0.0034 \| 0.0 \| 0.0067 \| 0.0193 \| 0.0145 \| 0.0013 \| 0.0205 \| 0.0976 \| 0.1138 \| 0.3514 \| 0.0 \| 0.104 \| 0.0 \| 0.1138 \| 0.1342 \| 0.0852 \| 0.0223 \| \| 2.0883 \| 3.0 \| 321 \| 2.4750 \| 0.0312 \| 0.0758 \| 0.021 \| 0.1376 \| 0.0 \| 0.0027 \| 0.0 \| 0.016 \| 0.0392 \| 0.0196 \| 0.0043 \| 0.0577 \| 0.1028 \| 0.1375 \| 0.3113 \| 0.0 \| 0.1281 \| 0.0 \| 0.248 \| 0.1399 \| 0.117 \| 0.0809 \| \| 1.8872 \| 4.0 \| 428 \| 2.0300 \| 0.0583 \| 0.134 \| 0.0452 \| 0.2421 \| 0.0 \| 0.0135 \| 0.0 \| 0.0358 \| 0.0702 \| 0.0395 \| 0.0109 \| 0.0754 \| 0.1726 \| 0.1972 \| 0.5689 \| 0.0 \| 0.1969 \| 0.0 \| 0.22 \| 0.2348 \| 0.152 \| 0.0608 \| \| 1.8577 \| 5.0 \| 535 \| 1.9218 \| 0.0823 \| 0.1798 \| 0.0655 \| 0.3197 \| 0.0 \| 0.0268 \| 0.0 \| 0.0649 \| 0.0911 \| 0.0576 \| 0.017 \| 0.0964 \| 0.1911 \| 0.2088 \| 0.5581 \| 0.0 \| 0.2188 \| 0.0 \| 0.2671 \| 0.2491 \| 0.1701 \| 0.0718 \| \| 1.7723 \| 6.0 \| 642 \| 1.9397 \| 0.0945 \| 0.2072 \| 0.0766 \| 0.3258 \| 0.0326 \| 0.0212 \| 0.0004 \| 0.0926 \| 0.1066 \| 0.0808 \| 0.0299 \| 0.1046 \| 0.2043 \| 0.2177 \| 0.5414 \| 0.1304 \| 0.1759 \| 0.0046 \| 0.236 \| 0.2573 \| 0.1748 \| 0.0883 \| \| 1.6972 \| 7.0 \| 749 \| 1.8258 \| 0.1137 \| 0.2603 \| 0.0836 \| 0.3377 \| 0.0521 \| 0.0521 \| 0.0109 \| 0.1156 \| 0.1299 \| 0.1054 \| 0.0324 \| 0.1388 \| 0.2648 \| 0.2912 \| 0.5842 \| 0.243 \| 0.2893 \| 0.0538 \| 0.2858 \| 0.3473 \| 0.2499 \| 0.1498 \| \| 1.6244 \| 8.0 \| 856 \| 1.8033 \| 0.1184 \| 0.2566 \| 0.0951 \| 0.3741 \| 0.0522 \| 0.0363 \| 0.0004 \| 0.1289 \| 0.1388 \| 0.0968 \| 0.0342 \| 0.1355 \| 0.2559 \| 0.2776 \| 0.5883 \| 0.2304 \| 0.2366 \| 0.0108 \| 0.3218 \| 0.3421 \| 0.2259 \| 0.102 \| \| 1.5748 \| 9.0 \| 963 \| 1.7707 \| 0.1186 \| 0.2686 \| 0.0942 \| 0.3855 \| 0.0505 \| 0.0427 \| 0.0005 \| 0.1139 \| 0.1413 \| 0.0946 \| 0.0284 \| 0.1472 \| 0.2763 \| 0.2939 \| 0.5977 \| 0.3063 \| 0.2237 \| 0.0308 \| 0.3111 \| 0.369 \| 0.2425 \| 0.1111 \| \| 1.5378 \| 10.0 \| 1070 \| 1.7505 \| 0.1306 \| 0.294 \| 0.0999 \| 0.3696 \| 0.0606 \| 0.0411 \| 0.0141 \| 0.1677 \| 0.1879 \| 0.1128 \| 0.0519 \| 0.1592 \| 0.3028 \| 0.3333 \| 0.577 \| 0.3152 \| 0.2759 \| 0.1785 \| 0.32 \| 0.4145 \| 0.2938 \| 0.1471 \| \| 1.5538 \| 11.0 \| 1177 \| 1.6579 \| 0.1405 \| 0.3058 \| 0.1111 \| 0.4391 \| 0.0565 \| 0.058 \| 0.0063 \| 0.1424 \| 0.199 \| 0.1076 \| 0.0452 \| 0.1545 \| 0.3171 \| 0.3423 \| 0.6523 \| 0.338 \| 0.2692 \| 0.1646 \| 0.2876 \| 0.4596 \| 0.2709 \| 0.1385 \| \| 1.509 \| 12.0 \| 1284 \| 1.6763 \| 0.1392 \| 0.3021 \| 0.105 \| 0.4083 \| 0.0671 \| 0.0505 \| 0.0255 \| 0.1447 \| 0.2012 \| 0.1032 \| 0.051 \| 0.1685 \| 0.3179 \| 0.3409 \| 0.5883 \| 0.343 \| 0.2848 \| 0.1708 \| 0.3173 \| 0.4777 \| 0.2687 \| 0.1274 \| \| 1.5149 \| 13.0 \| 1391 \| 1.6443 \| 0.1443 \| 0.3208 \| 0.116 \| 0.4067 \| 0.0772 \| 0.0569 \| 0.014 \| 0.1668 \| 0.2052 \| 0.1248 \| 0.0375 \| 0.174 \| 0.3313 \| 0.3479 \| 0.6135 \| 0.338 \| 0.2924 \| 0.1815 \| 0.3142 \| 0.4748 \| 0.2775 \| 0.1601 \| \| 1.4515 \| 14.0 \| 1498 \| 1.7081 \| 0.1427 \| 0.3155 \| 0.1082 \| 0.4072 \| 0.0551 \| 0.0767 \| 0.0155 \| 0.1591 \| 0.2035 \| 0.1138 \| 0.0403 \| 0.1746 \| 0.3404 \| 0.3703 \| 0.6063 \| 0.3582 \| 0.3027 \| 0.2738 \| 0.3107 \| 0.485 \| 0.3095 \| 0.1499 \| \| 1.5002 \| 15.0 \| 1605 \| 1.6553 \| 0.1419 \| 0.3111 \| 0.1024 \| 0.4224 \| 0.0675 \| 0.0555 \| 0.0077 \| 0.1564 \| 0.216 \| 0.1112 \| 0.0544 \| 0.1705 \| 0.3136 \| 0.3353 \| 0.6324 \| 0.2709 \| 0.2946 \| 0.1969 \| 0.2818 \| 0.4421 \| 0.2707 \| 0.1414 \| \| 1.4747 \| 16.0 \| 1712 \| 1.5881 \| 0.1518 \| 0.33 \| 0.1305 \| 0.4355 \| 0.0589 \| 0.0634 \| 0.0111 \| 0.1904 \| 0.221 \| 0.1175 \| 0.045 \| 0.1797 \| 0.3366 \| 0.3583 \| 0.6225 \| 0.3405 \| 0.2777 \| 0.2231 \| 0.3276 \| 0.5102 \| 0.2928 \| 0.1459 \| \| 1.4134 \| 17.0 \| 1819 \| 1.6045 \| 0.1546 \| 0.3384 \| 0.1261 \| 0.4177 \| 0.0606 \| 0.068 \| 0.0187 \| 0.2083 \| 0.2225 \| 0.1172 \| 0.0446 \| 0.1911 \| 0.3443 \| 0.3644 \| 0.6027 \| 0.338 \| 0.292 \| 0.2523 \| 0.3369 \| 0.5104 \| 0.3014 \| 0.1206 \| \| 1.4155 \| 18.0 \| 1926 \| 1.6723 \| 0.1538 \| 0.3377 \| 0.1249 \| 0.4035 \| 0.0626 \| 0.076 \| 0.0371 \| 0.1896 \| 0.2264 \| 0.1194 \| 0.0599 \| 0.198 \| 0.3546 \| 0.3833 \| 0.6014 \| 0.3759 \| 0.2799 \| 0.34 \| 0.3191 \| 0.5551 \| 0.3167 \| 0.13 \| \| 1.4372 \| 19.0 \| 2033 \| 1.6629 \| 0.1531 \| 0.3303 \| 0.1219 \| 0.4185 \| 0.0517 \| 0.0692 \| 0.0278 \| 0.1984 \| 0.2333 \| 0.1221 \| 0.0459 \| 0.1854 \| 0.3515 \| 0.3839 \| 0.6023 \| 0.3873 \| 0.275 \| 0.3154 \| 0.3396 \| 0.5284 \| 0.3285 \| 0.1541 \| \| 1.4156 \| 20.0 \| 2140 \| 1.5876 \| 0.1587 \| 0.3393 \| 0.137 \| 0.4228 \| 0.0659 \| 0.0745 \| 0.0258 \| 0.2043 \| 0.2403 \| 0.1184 \| 0.067 \| 0.1917 \| 0.3286 \| 0.3542 \| 0.5914 \| 0.3089 \| 0.3076 \| 0.2215 \| 0.3418 \| 0.4914 \| 0.2925 \| 0.1423 \| \| 1.3659 \| 21.0 \| 2247 \| 1.6484 \| 0.1497 \| 0.3398 \| 0.124 \| 0.4217 \| 0.0657 \| 0.0644 \| 0.019 \| 0.1774 \| 0.2263 \| 0.1088 \| 0.0359 \| 0.186 \| 0.3318 \| 0.3466 \| 0.6041 \| 0.319 \| 0.254 \| 0.2646 \| 0.2911 \| 0.4984 \| 0.2859 \| 0.101 \| \| 1.339 \| 22.0 \| 2354 \| 1.5649 \| 0.1655 \| 0.3626 \| 0.1294 \| 0.4309 \| 0.0856 \| 0.0979 \| 0.0208 \| 0.1923 \| 0.2475 \| 0.1316 \| 0.046 \| 0.1937 \| 0.3682 \| 0.3943 \| 0.6252 \| 0.362 \| 0.3321 \| 0.3308 \| 0.3213 \| 0.5332 \| 0.3458 \| 0.1509 \| \| 1.4317 \| 23.0 \| 2461 \| 1.5822 \| 0.162 \| 0.3599 \| 0.1265 \| 0.4617 \| 0.0562 \| 0.0818 \| 0.0144 \| 0.1962 \| 0.2323 \| 0.122 \| 0.055 \| 0.1958 \| 0.3482 \| 0.3666 \| 0.6514 \| 0.3405 \| 0.275 \| 0.2646 \| 0.3013 \| 0.5214 \| 0.3049 \| 0.1204 \| \| 1.3442 \| 24.0 \| 2568 \| 1.5509 \| 0.1758 \| 0.3742 \| 0.1367 \| 0.4346 \| 0.092 \| 0.1071 \| 0.0356 \| 0.2099 \| 0.2524 \| 0.1523 \| 0.0461 \| 0.2152 \| 0.3713 \| 0.3953 \| 0.6212 \| 0.381 \| 0.3375 \| 0.3062 \| 0.3307 \| 0.5532 \| 0.3408 \| 0.1443 \| \| 1.3088 \| 25.0 \| 2675 \| 1.5144 \| 0.1732 \| 0.3703 \| 0.1431 \| 0.4627 \| 0.0835 \| 0.0882 \| 0.0167 \| 0.2149 \| 0.255 \| 0.138 \| 0.0488 \| 0.1954 \| 0.3597 \| 0.3789 \| 0.655 \| 0.3456 \| 0.3143 \| 0.2462 \| 0.3338 \| 0.5433 \| 0.3152 \| 0.1227 \| \| 1.2841 \| 26.0 \| 2782 \| 1.5131 \| 0.1863 \| 0.3861 \| 0.1567 \| 0.4666 \| 0.1093 \| 0.0956 \| 0.0371 \| 0.223 \| 0.281 \| 0.1384 \| 0.0514 \| 0.2129 \| 0.3647 \| 0.3846 \| 0.6707 \| 0.3506 \| 0.3027 \| 0.2338 \| 0.3653 \| 0.543 \| 0.3154 \| 0.1479 \| \| 1.2947 \| 27.0 \| 2889 \| 1.5201 \| 0.1728 \| 0.3797 \| 0.1302 \| 0.4428 \| 0.1068 \| 0.1116 \| 0.0222 \| 0.1807 \| 0.2608 \| 0.134 \| 0.0476 \| 0.1976 \| 0.3634 \| 0.3875 \| 0.6239 \| 0.3468 \| 0.3598 \| 0.2938 \| 0.3129 \| 0.5232 \| 0.331 \| 0.1466 \| \| 1.252 \| 28.0 \| 2996 \| 1.4757 \| 0.1899 \| 0.3907 \| 0.1572 \| 0.4615 \| 0.1026 \| 0.1288 \| 0.0302 \| 0.2262 \| 0.295 \| 0.1446 \| 0.0665 \| 0.2119 \| 0.3774 \| 0.3957 \| 0.6477 \| 0.3861 \| 0.3402 \| 0.2631 \| 0.3413 \| 0.5794 \| 0.3256 \| 0.1531 \| \| 1.2745 \| 29.0 \| 3103 \| 1.5029 \| 0.1826 \| 0.395 \| 0.1331 \| 0.4466 \| 0.1041 \| 0.113 \| 0.0466 \| 0.2026 \| 0.2817 \| 0.157 \| 0.0457 \| 0.2141 \| 0.3623 \| 0.3802 \| 0.6446 \| 0.3354 \| 0.3464 \| 0.2554 \| 0.3191 \| 0.4998 \| 0.3378 \| 0.1535 \| \| 1.2365 \| 30.0 \| 3210 \| 1.4733 \| 0.1966 \| 0.4269 \| 0.1553 \| 0.4552 \| 0.1469 \| 0.1287 \| 0.035 \| 0.217 \| 0.2938 \| 0.1694 \| 0.0659 \| 0.2247 \| 0.388 \| 0.3993 \| 0.6284 \| 0.3633 \| 0.3536 \| 0.3292 \| 0.3222 \| 0.5594 \| 0.3502 \| 0.1447 \| \| 1.2055 \| 31.0 \| 3317 \| 1.5310 \| 0.1831 \| 0.3896 \| 0.1457 \| 0.4614 \| 0.1266 \| 0.0936 \| 0.0351 \| 0.1988 \| 0.2706 \| 0.1474 \| 0.0524 \| 0.2158 \| 0.3636 \| 0.38 \| 0.6225 \| 0.3696 \| 0.3174 \| 0.2923 \| 0.2982 \| 0.5223 \| 0.3278 \| 0.1527 \| \| 1.2181 \| 32.0 \| 3424 \| 1.4300 \| 0.206 \| 0.4241 \| 0.1698 \| 0.4943 \| 0.125 \| 0.1308 \| 0.0418 \| 0.2381 \| 0.3151 \| 0.1464 \| 0.0786 \| 0.2372 \| 0.3845 \| 0.3965 \| 0.6622 \| 0.3608 \| 0.3384 \| 0.2815 \| 0.3396 \| 0.5687 \| 0.3259 \| 0.1504 \| \| 1.1936 \| 33.0 \| 3531 \| 1.3983 \| 0.219 \| 0.4472 \| 0.1882 \| 0.5025 \| 0.161 \| 0.1486 \| 0.0388 \| 0.2439 \| 0.3481 \| 0.1659 \| 0.0532 \| 0.2361 \| 0.3955 \| 0.4157 \| 0.6477 \| 0.3962 \| 0.3768 \| 0.2969 \| 0.3609 \| 0.5569 \| 0.3681 \| 0.1852 \| \| 1.1766 \| 34.0 \| 3638 \| 1.4252 \| 0.2112 \| 0.4429 \| 0.186 \| 0.4788 \| 0.1525 \| 0.1409 \| 0.0413 \| 0.2424 \| 0.3333 \| 0.1618 \| 0.0649 \| 0.2415 \| 0.4032 \| 0.4171 \| 0.6333 \| 0.4139 \| 0.3723 \| 0.3031 \| 0.3627 \| 0.5865 \| 0.3613 \| 0.1671 \| \| 1.1881 \| 35.0 \| 3745 \| 1.4474 \| 0.2089 \| 0.4392 \| 0.1807 \| 0.4701 \| 0.1567 \| 0.1537 \| 0.0433 \| 0.2206 \| 0.3283 \| 0.1735 \| 0.0476 \| 0.2301 \| 0.3828 \| 0.3938 \| 0.6059 \| 0.4 \| 0.3723 \| 0.2523 \| 0.3387 \| 0.5522 \| 0.3409 \| 0.1313 \| \| 1.1731 \| 36.0 \| 3852 \| 1.4049 \| 0.2193 \| 0.4635 \| 0.1851 \| 0.5034 \| 0.1324 \| 0.1668 \| 0.0523 \| 0.2416 \| 0.3293 \| 0.1738 \| 0.0544 \| 0.2415 \| 0.3998 \| 0.4116 \| 0.6459 \| 0.3785 \| 0.3737 \| 0.3154 \| 0.3444 \| 0.5581 \| 0.3617 \| 0.1726 \| \| 1.14 \| 37.0 \| 3959 \| 1.4157 \| 0.2097 \| 0.4485 \| 0.1698 \| 0.4988 \| 0.1456 \| 0.1321 \| 0.0615 \| 0.2106 \| 0.343 \| 0.1592 \| 0.061 \| 0.2267 \| 0.3779 \| 0.3934 \| 0.6297 \| 0.3582 \| 0.35 \| 0.2892 \| 0.34 \| 0.5569 \| 0.3352 \| 0.1308 \| \| 1.1286 \| 38.0 \| 4066 \| 1.3872 \| 0.2135 \| 0.4475 \| 0.1802 \| 0.4968 \| 0.1468 \| 0.1432 \| 0.0545 \| 0.2264 \| 0.3308 \| 0.1695 \| 0.0541 \| 0.2388 \| 0.4061 \| 0.4215 \| 0.6495 \| 0.4228 \| 0.3821 \| 0.3138 \| 0.3391 \| 0.5656 \| 0.3647 \| 0.2025 \| \| 1.1398 \| 39.0 \| 4173 \| 1.4290 \| 0.2131 \| 0.4419 \| 0.1668 \| 0.4882 \| 0.1032 \| 0.1618 \| 0.0638 \| 0.2485 \| 0.3403 \| 0.1659 \| 0.0482 \| 0.2407 \| 0.4155 \| 0.4279 \| 0.6405 \| 0.4228 \| 0.3661 \| 0.3385 \| 0.3716 \| 0.6137 \| 0.372 \| 0.1347 \| \| 1.1372 \| 40.0 \| 4280 \| 1.3823 \| 0.212 \| 0.4684 \| 0.1785 \| 0.4819 \| 0.1455 \| 0.1478 \| 0.05 \| 0.235 \| 0.3349 \| 0.1684 \| 0.0616 \| 0.2547 \| 0.4168 \| 0.4355 \| 0.6306 \| 0.4304 \| 0.3866 \| 0.3523 \| 0.3773 \| 0.6017 \| 0.3784 \| 0.1724 \| \| 1.1173 \| 41.0 \| 4387 \| 1.4033 \| 0.2191 \| 0.4555 \| 0.1875 \| 0.5062 \| 0.1254 \| 0.1576 \| 0.0658 \| 0.2402 \| 0.3384 \| 0.1826 \| 0.0538 \| 0.2474 \| 0.4157 \| 0.4311 \| 0.6437 \| 0.4291 \| 0.3879 \| 0.3262 \| 0.3684 \| 0.6138 \| 0.3651 \| 0.1632 \| \| 1.1065 \| 42.0 \| 4494 \| 1.4276 \| 0.2176 \| 0.4688 \| 0.1656 \| 0.4854 \| 0.1977 \| 0.1425 \| 0.0682 \| 0.1943 \| 0.3273 \| 0.1838 \| 0.069 \| 0.2479 \| 0.3991 \| 0.4159 \| 0.6405 \| 0.4506 \| 0.3482 \| 0.3108 \| 0.3293 \| 0.5677 \| 0.3603 \| 0.1665 \| \| 1.1245 \| 43.0 \| 4601 \| 1.4040 \| 0.2073 \| 0.4466 \| 0.1712 \| 0.4941 \| 0.1068 \| 0.1453 \| 0.0624 \| 0.2281 \| 0.3255 \| 0.1618 \| 0.0731 \| 0.2357 \| 0.402 \| 0.4105 \| 0.6176 \| 0.3797 \| 0.3536 \| 0.3415 \| 0.36 \| 0.5864 \| 0.3442 \| 0.1519 \| \| 1.1049 \| 44.0 \| 4708 \| 1.3916 \| 0.2169 \| 0.4598 \| 0.1835 \| 0.4769 \| 0.1651 \| 0.154 \| 0.0539 \| 0.2347 \| 0.3413 \| 0.1707 \| 0.0746 \| 0.2535 \| 0.406 \| 0.422 \| 0.6284 \| 0.4025 \| 0.3924 \| 0.32 \| 0.3667 \| 0.5728 \| 0.3657 \| 0.1741 \| \| 1.07 \| 45.0 \| 4815 \| 1.3629 \| 0.2307 \| 0.479 \| 0.1857 \| 0.5024 \| 0.1537 \| 0.1864 \| 0.075 \| 0.2361 \| 0.3512 \| 0.1931 \| 0.0677 \| 0.258 \| 0.4256 \| 0.4405 \| 0.6468 \| 0.4228 \| 0.379 \| 0.3892 \| 0.3644 \| 0.6005 \| 0.3921 \| 0.1622 \| \| 1.0862 \| 46.0 \| 4922 \| 1.3544 \| 0.2329 \| 0.4875 \| 0.1938 \| 0.5099 \| 0.1729 \| 0.1743 \| 0.0601 \| 0.2472 \| 0.3683 \| 0.1849 \| 0.069 \| 0.2417 \| 0.4062 \| 0.4213 \| 0.6671 \| 0.3785 \| 0.3884 \| 0.3154 \| 0.3569 \| 0.5998 \| 0.3649 \| 0.1415 \| \| 1.0606 \| 47.0 \| 5029 \| 1.3490 \| 0.2279 \| 0.4671 \| 0.1828 \| 0.491 \| 0.1715 \| 0.1666 \| 0.064 \| 0.2465 \| 0.3586 \| 0.1748 \| 0.0727 \| 0.2603 \| 0.4306 \| 0.4434 \| 0.6559 \| 0.438 \| 0.3848 \| 0.3615 \| 0.3769 \| 0.6136 \| 0.372 \| 0.1867 \| \| 1.0517 \| 48.0 \| 5136 \| 1.3711 \| 0.2323 \| 0.4913 \| 0.181 \| 0.4912 \| 0.2222 \| 0.1615 \| 0.0442 \| 0.2426 \| 0.3617 \| 0.1758 \| 0.0705 \| 0.2434 \| 0.4108 \| 0.4233 \| 0.627 \| 0.4443 \| 0.3683 \| 0.3154 \| 0.3613 \| 0.5821 \| 0.3642 \| 0.1745 \| \| 1.0691 \| 49.0 \| 5243 \| 1.3260 \| 0.2389 \| 0.4867 \| 0.199 \| 0.5088 \| 0.1961 \| 0.1662 \| 0.0433 \| 0.28 \| 0.3837 \| 0.1806 \| 0.0863 \| 0.2603 \| 0.4327 \| 0.4406 \| 0.6486 \| 0.4544 \| 0.371 \| 0.3385 \| 0.3907 \| 0.6257 \| 0.3787 \| 0.167 \| \| 1.0383 \| 50.0 \| 5350 \| 1.3316 \| 0.2444 \| 0.4987 \| 0.2146 \| 0.5157 \| 0.1974 \| 0.1867 \| 0.0746 \| 0.2476 \| 0.3849 \| 0.19 \| 0.0714 \| 0.2569 \| 0.425 \| 0.4383 \| 0.6608 \| 0.4494 \| 0.3987 \| 0.3231 \| 0.3596 \| 0.6142 \| 0.3792 \| 0.1694 \| \| 1.0445 \| 51.0 \| 5457 \| 1.3406 \| 0.2374 \| 0.4855 \| 0.1968 \| 0.5008 \| 0.1831 \| 0.1698 \| 0.0791 \| 0.2545 \| 0.3681 \| 0.1947 \| 0.0747 \| 0.2525 \| 0.4258 \| 0.4403 \| 0.6572 \| 0.4468 \| 0.3853 \| 0.3431 \| 0.3693 \| 0.6079 \| 0.3877 \| 0.1859 \| \| 1.0122 \| 52.0 \| 5564 \| 1.3005 \| 0.2477 \| 0.5029 \| 0.2034 \| 0.5084 \| 0.1895 \| 0.1706 \| 0.1017 \| 0.268 \| 0.3893 \| 0.1935 \| 0.0755 \| 0.2747 \| 0.4432 \| 0.4526 \| 0.6599 \| 0.4608 \| 0.392 \| 0.3662 \| 0.3844 \| 0.6098 \| 0.3925 \| 0.2031 \| \| 0.9938 \| 53.0 \| 5671 \| 1.3286 \| 0.2532 \| 0.5169 \| 0.2223 \| 0.5146 \| 0.1887 \| 0.1815 \| 0.1132 \| 0.2682 \| 0.3849 \| 0.205 \| 0.0745 \| 0.2782 \| 0.4453 \| 0.4591 \| 0.6743 \| 0.4785 \| 0.3772 \| 0.3754 \| 0.3902 \| 0.626 \| 0.396 \| 0.1838 \| \| 1.0204 \| 54.0 \| 5778 \| 1.3251 \| 0.2528 \| 0.518 \| 0.224 \| 0.5171 \| 0.2166 \| 0.1915 \| 0.0875 \| 0.251 \| 0.404 \| 0.1964 \| 0.0822 \| 0.2674 \| 0.4319 \| 0.4424 \| 0.6568 \| 0.4519 \| 0.3808 \| 0.3354 \| 0.3871 \| 0.6108 \| 0.3899 \| 0.1771 \| \| 0.9882 \| 55.0 \| 5885 \| 1.3231 \| 0.2495 \| 0.5069 \| 0.22 \| 0.531 \| 0.2113 \| 0.1735 \| 0.1058 \| 0.2257 \| 0.3987 \| 0.2008 \| 0.0674 \| 0.2629 \| 0.4312 \| 0.4405 \| 0.6784 \| 0.4519 \| 0.3804 \| 0.3354 \| 0.3564 \| 0.6144 \| 0.3806 \| 0.1674 \| \| 1.0046 \| 56.0 \| 5992 \| 1.3377 \| 0.2524 \| 0.523 \| 0.2186 \| 0.5272 \| 0.2091 \| 0.1884 \| 0.0904 \| 0.2472 \| 0.4021 \| 0.201 \| 0.0665 \| 0.2729 \| 0.4396 \| 0.4498 \| 0.6671 \| 0.4646 \| 0.3826 \| 0.3692 \| 0.3653 \| 0.6219 \| 0.3964 \| 0.1789 \| \| 1.0016 \| 57.0 \| 6099 \| 1.3185 \| 0.2503 \| 0.5142 \| 0.2258 \| 0.5272 \| 0.216 \| 0.1628 \| 0.0851 \| 0.2606 \| 0.3973 \| 0.1923 \| 0.0674 \| 0.2689 \| 0.4362 \| 0.4497 \| 0.6577 \| 0.4747 \| 0.3746 \| 0.3631 \| 0.3787 \| 0.6336 \| 0.3877 \| 0.1685 \| \| 0.9883 \| 58.0 \| 6206 \| 1.3084 \| 0.2568 \| 0.5155 \| 0.2298 \| 0.5325 \| 0.2181 \| 0.1826 \| 0.0855 \| 0.2653 \| 0.4028 \| 0.2064 \| 0.0776 \| 0.2762 \| 0.4401 \| 0.4531 \| 0.6671 \| 0.4785 \| 0.3804 \| 0.3585 \| 0.3809 \| 0.636 \| 0.3909 \| 0.1667 \| \| 0.9602 \| 59.0 \| 6313 \| 1.2997 \| 0.2547 \| 0.5081 \| 0.2266 \| 0.5313 \| 0.2093 \| 0.1846 \| 0.0738 \| 0.2743 \| 0.3964 \| 0.1954 \| 0.0712 \| 0.2736 \| 0.4402 \| 0.4507 \| 0.6559 \| 0.4608 \| 0.3893 \| 0.3477 \| 0.4 \| 0.6033 \| 0.394 \| 0.1952 \| \| 0.9773 \| 60.0 \| 6420 \| 1.3113 \| 0.2645 \| 0.5457 \| 0.2198 \| 0.5352 \| 0.238 \| 0.188 \| 0.1104 \| 0.2508 \| 0.417 \| 0.207 \| 0.066 \| 0.2807 \| 0.4395 \| 0.4497 \| 0.6563 \| 0.4873 \| 0.3643 \| 0.3646 \| 0.376 \| 0.6122 \| 0.3969 \| 0.1644 \| \| 0.9686 \| 61.0 \| 6527 \| 1.3026 \| 0.2559 \| 0.5301 \| 0.2045 \| 0.5328 \| 0.2314 \| 0.1755 \| 0.0815 \| 0.2583 \| 0.4018 \| 0.2057 \| 0.0625 \| 0.2682 \| 0.4381 \| 0.4518 \| 0.6766 \| 0.4722 \| 0.3723 \| 0.3646 \| 0.3733 \| 0.6317 \| 0.3969 \| 0.1681 \| \| 0.9567 \| 62.0 \| 6634 \| 1.2796 \| 0.2705 \| 0.553 \| 0.2302 \| 0.5388 \| 0.2515 \| 0.1876 \| 0.1091 \| 0.2653 \| 0.4179 \| 0.2184 \| 0.0828 \| 0.2758 \| 0.4523 \| 0.4657 \| 0.6806 \| 0.4949 \| 0.3808 \| 0.3831 \| 0.3889 \| 0.6367 \| 0.4141 \| 0.1773 \| \| 0.9708 \| 63.0 \| 6741 \| 1.3059 \| 0.2601 \| 0.5508 \| 0.2151 \| 0.5379 \| 0.2271 \| 0.1795 \| 0.1046 \| 0.2514 \| 0.4032 \| 0.203 \| 0.0794 \| 0.266 \| 0.4358 \| 0.452 \| 0.6698 \| 0.4747 \| 0.3705 \| 0.38 \| 0.3649 \| 0.6006 \| 0.4044 \| 0.1817 \| \| 0.952 \| 64.0 \| 6848 \| 1.3013 \| 0.2637 \| 0.5312 \| 0.228 \| 0.5374 \| 0.227 \| 0.1805 \| 0.0944 \| 0.2791 \| 0.4199 \| 0.2113 \| 0.0749 \| 0.2824 \| 0.4484 \| 0.4629 \| 0.6626 \| 0.4937 \| 0.3911 \| 0.3677 \| 0.3996 \| 0.6251 \| 0.4122 \| 0.1934 \| \| 0.9335 \| 65.0 \| 6955 \| 1.3231 \| 0.2644 \| 0.5376 \| 0.2208 \| 0.5362 \| 0.2564 \| 0.1668 \| 0.0945 \| 0.2681 \| 0.4075 \| 0.2091 \| 0.083 \| 0.2741 \| 0.4524 \| 0.4649 \| 0.6721 \| 0.5089 \| 0.3777 \| 0.3892 \| 0.3764 \| 0.6225 \| 0.4149 \| 0.1879 \| \| 0.9378 \| 66.0 \| 7062 \| 1.2839 \| 0.258 \| 0.5316 \| 0.2175 \| 0.5311 \| 0.2351 \| 0.1747 \| 0.0904 \| 0.2586 \| 0.4166 \| 0.2101 \| 0.0745 \| 0.2793 \| 0.4521 \| 0.4661 \| 0.6689 \| 0.5025 \| 0.3955 \| 0.3785 \| 0.3849 \| 0.6323 \| 0.4171 \| 0.188 \| \| 0.9359 \| 67.0 \| 7169 \| 1.3037 \| 0.2606 \| 0.548 \| 0.2147 \| 0.5387 \| 0.2334 \| 0.1713 \| 0.1064 \| 0.2533 \| 0.415 \| 0.2146 \| 0.0795 \| 0.2728 \| 0.4415 \| 0.4559 \| 0.6784 \| 0.4835 \| 0.3781 \| 0.36 \| 0.3796 \| 0.6312 \| 0.4075 \| 0.1637 \| \| 0.9218 \| 68.0 \| 7276 \| 1.2800 \| 0.269 \| 0.5546 \| 0.2302 \| 0.5459 \| 0.2507 \| 0.1823 \| 0.1058 \| 0.2603 \| 0.4256 \| 0.2151 \| 0.083 \| 0.2706 \| 0.4481 \| 0.4597 \| 0.6712 \| 0.4696 \| 0.3835 \| 0.3846 \| 0.3898 \| 0.6186 \| 0.4051 \| 0.2107 \| \| 0.9189 \| 69.0 \| 7383 \| 1.2613 \| 0.2724 \| 0.5536 \| 0.2313 \| 0.5453 \| 0.2606 \| 0.1822 \| 0.1036 \| 0.27 \| 0.4374 \| 0.2123 \| 0.0791 \| 0.2795 \| 0.4526 \| 0.4663 \| 0.6676 \| 0.4987 \| 0.3902 \| 0.3723 \| 0.4027 \| 0.6376 \| 0.4138 \| 0.2006 \| \| 0.9092 \| 70.0 \| 7490 \| 1.2850 \| 0.2628 \| 0.5303 \| 0.2188 \| 0.5448 \| 0.247 \| 0.1701 \| 0.0999 \| 0.2524 \| 0.4314 \| 0.2052 \| 0.0815 \| 0.2755 \| 0.4481 \| 0.4618 \| 0.668 \| 0.4949 \| 0.3848 \| 0.3677 \| 0.3938 \| 0.645 \| 0.4011 \| 0.1933 \| \| 0.9069 \| 71.0 \| 7597 \| 1.2548 \| 0.2751 \| 0.5565 \| 0.2414 \| 0.5499 \| 0.2558 \| 0.1791 \| 0.1248 \| 0.2662 \| 0.4429 \| 0.2188 \| 0.0703 \| 0.2929 \| 0.4524 \| 0.4669 \| 0.6775 \| 0.4747 \| 0.3938 \| 0.3908 \| 0.3978 \| 0.6499 \| 0.4108 \| 0.1824 \| \| 0.9118 \| 72.0 \| 7704 \| 1.2529 \| 0.2691 \| 0.5525 \| 0.2408 \| 0.5462 \| 0.2308 \| 0.1804 \| 0.1244 \| 0.2638 \| 0.4295 \| 0.2146 \| 0.0737 \| 0.2817 \| 0.4469 \| 0.4569 \| 0.6703 \| 0.4481 \| 0.3871 \| 0.3877 \| 0.3916 \| 0.6281 \| 0.4004 \| 0.1912 \| \| 0.8915 \| 73.0 \| 7811 \| 1.2830 \| 0.2778 \| 0.5637 \| 0.2352 \| 0.5459 \| 0.2499 \| 0.1818 \| 0.1303 \| 0.2814 \| 0.4376 \| 0.2216 \| 0.085 \| 0.2877 \| 0.454 \| 0.4651 \| 0.6644 \| 0.4873 \| 0.3777 \| 0.3923 \| 0.4036 \| 0.6459 \| 0.4085 \| 0.1722 \| \| 0.8906 \| 74.0 \| 7918 \| 1.2621 \| 0.2812 \| 0.5762 \| 0.2419 \| 0.5442 \| 0.274 \| 0.1863 \| 0.1225 \| 0.2788 \| 0.435 \| 0.2297 \| 0.0872 \| 0.2833 \| 0.4524 \| 0.4695 \| 0.6721 \| 0.5038 \| 0.3955 \| 0.3677 \| 0.4084 \| 0.6412 \| 0.4176 \| 0.1956 \| \| 0.8748 \| 75.0 \| 8025 \| 1.2661 \| 0.2813 \| 0.5702 \| 0.2334 \| 0.5428 \| 0.2763 \| 0.1817 \| 0.1276 \| 0.2784 \| 0.4281 \| 0.232 \| 0.0904 \| 0.2843 \| 0.4608 \| 0.4733 \| 0.6658 \| 0.5051 \| 0.3969 \| 0.3923 \| 0.4067 \| 0.6409 \| 0.4236 \| 0.197 \| \| 0.8745 \| 76.0 \| 8132 \| 1.2626 \| 0.2823 \| 0.5652 \| 0.2466 \| 0.5456 \| 0.2825 \| 0.1917 \| 0.1227 \| 0.269 \| 0.4304 \| 0.2321 \| 0.0955 \| 0.2872 \| 0.4495 \| 0.4641 \| 0.6658 \| 0.4987 \| 0.3848 \| 0.3769 \| 0.3942 \| 0.6428 \| 0.4117 \| 0.1705 \| \| 0.8898 \| 77.0 \| 8239 \| 1.2889 \| 0.2811 \| 0.5707 \| 0.2403 \| 0.5353 \| 0.2697 \| 0.1868 \| 0.1405 \| 0.273 \| 0.4344 \| 0.2318 \| 0.0737 \| 0.2846 \| 0.458 \| 0.4696 \| 0.6599 \| 0.5165 \| 0.3799 \| 0.3877 \| 0.404 \| 0.6407 \| 0.4147 \| 0.2015 \| \| 0.8799 \| 78.0 \| 8346 \| 1.2816 \| 0.2865 \| 0.5721 \| 0.2468 \| 0.535 \| 0.288 \| 0.1895 \| 0.1505 \| 0.2696 \| 0.4493 \| 0.2353 \| 0.0869 \| 0.2873 \| 0.4518 \| 0.4688 \| 0.6671 \| 0.5038 \| 0.4 \| 0.3831 \| 0.3898 \| 0.6507 \| 0.4175 \| 0.1889 \| \| 0.861 \| 79.0 \| 8453 \| 1.2650 \| 0.2894 \| 0.5759 \| 0.26 \| 0.5441 \| 0.2918 \| 0.1965 \| 0.1429 \| 0.2718 \| 0.4567 \| 0.2339 \| 0.0807 \| 0.2924 \| 0.4512 \| 0.4629 \| 0.6721 \| 0.5089 \| 0.3799 \| 0.3631 \| 0.3907 \| 0.6506 \| 0.404 \| 0.1721 \| \| 0.8627 \| 80.0 \| 8560 \| 1.2548 \| 0.2861 \| 0.5717 \| 0.2503 \| 0.5476 \| 0.2736 \| 0.1949 \| 0.1458 \| 0.2687 \| 0.4461 \| 0.2372 \| 0.0873 \| 0.2868 \| 0.4531 \| 0.4677 \| 0.6757 \| 0.4949 \| 0.3938 \| 0.3738 \| 0.4004 \| 0.6411 \| 0.4176 \| 0.1916 \| \| 0.8622 \| 81.0 \| 8667 \| 1.2928 \| 0.2847 \| 0.5631 \| 0.2481 \| 0.5391 \| 0.2657 \| 0.1996 \| 0.1288 \| 0.2904 \| 0.4474 \| 0.2295 \| 0.0797 \| 0.2879 \| 0.4585 \| 0.4705 \| 0.6644 \| 0.5089 \| 0.3781 \| 0.38 \| 0.4209 \| 0.6554 \| 0.4199 \| 0.1616 \| \| 0.8586 \| 82.0 \| 8774 \| 1.2744 \| 0.2801 \| 0.5663 \| 0.2436 \| 0.5414 \| 0.2653 \| 0.1943 \| 0.1235 \| 0.2761 \| 0.4351 \| 0.2301 \| 0.0846 \| 0.2866 \| 0.4532 \| 0.4655 \| 0.6703 \| 0.481 \| 0.3857 \| 0.3938 \| 0.3969 \| 0.6363 \| 0.4059 \| 0.2026 \| \| 0.8523 \| 83.0 \| 8881 \| 1.2564 \| 0.2885 \| 0.5749 \| 0.2494 \| 0.548 \| 0.2971 \| 0.1862 \| 0.1276 \| 0.2838 \| 0.4436 \| 0.2355 \| 0.0837 \| 0.2887 \| 0.4608 \| 0.4761 \| 0.6716 \| 0.5215 \| 0.3915 \| 0.3908 \| 0.4049 \| 0.6465 \| 0.4221 \| 0.1964 \| \| 0.8366 \| 84.0 \| 8988 \| 1.2571 \| 0.2953 \| 0.5842 \| 0.2646 \| 0.5476 \| 0.3256 \| 0.1859 \| 0.14 \| 0.2772 \| 0.4622 \| 0.243 \| 0.0893 \| 0.3019 \| 0.4561 \| 0.47 \| 0.6734 \| 0.5127 \| 0.3786 \| 0.3769 \| 0.4084 \| 0.6532 \| 0.4145 \| 0.1874 \| \| 0.8217 \| 85.0 \| 9095 \| 1.2677 \| 0.2967 \| 0.5928 \| 0.268 \| 0.5481 \| 0.3208 \| 0.1918 \| 0.1424 \| 0.2805 \| 0.4632 \| 0.2397 \| 0.087 \| 0.2971 \| 0.457 \| 0.4695 \| 0.6779 \| 0.5152 \| 0.3879 \| 0.3569 \| 0.4093 \| 0.6515 \| 0.4172 \| 0.1938 \| \| 0.8385 \| 86.0 \| 9202 \| 1.2808 \| 0.2903 \| 0.583 \| 0.2494 \| 0.5434 \| 0.3045 \| 0.1934 \| 0.141 \| 0.2691 \| 0.4654 \| 0.2261 \| 0.0893 \| 0.2932 \| 0.4558 \| 0.4655 \| 0.6649 \| 0.5114 \| 0.3879 \| 0.3708 \| 0.3924 \| 0.655 \| 0.401 \| 0.1873 \| \| 0.8304 \| 87.0 \| 9309 \| 1.2956 \| 0.2888 \| 0.5773 \| 0.2582 \| 0.5437 \| 0.2936 \| 0.1877 \| 0.1485 \| 0.2707 \| 0.4552 \| 0.231 \| 0.0846 \| 0.2898 \| 0.4587 \| 0.4728 \| 0.6671 \| 0.519 \| 0.3902 \| 0.3985 \| 0.3893 \| 0.6544 \| 0.4195 \| 0.1903 \| \| 0.8369 \| 88.0 \| 9416 \| 1.2704 \| 0.2884 \| 0.5857 \| 0.2531 \| 0.5443 \| 0.3007 \| 0.1908 \| 0.1325 \| 0.2738 \| 0.4529 \| 0.2275 \| 0.086 \| 0.2853 \| 0.4556 \| 0.4681 \| 0.6779 \| 0.5025 \| 0.3911 \| 0.3708 \| 0.3982 \| 0.6439 \| 0.4036 \| 0.2107 \| \| 0.8317 \| 89.0 \| 9523 \| 1.2721 \| 0.2837 \| 0.5738 \| 0.2341 \| 0.5465 \| 0.2812 \| 0.1887 \| 0.1291 \| 0.2728 \| 0.4506 \| 0.2235 \| 0.0871 \| 0.286 \| 0.4533 \| 0.4638 \| 0.6734 \| 0.4873 \| 0.3848 \| 0.3846 \| 0.3889 \| 0.6381 \| 0.4021 \| 0.2009 \| \| 0.8319 \| 90.0 \| 9630 \| 1.2615 \| 0.2838 \| 0.573 \| 0.2459 \| 0.5477 \| 0.2789 \| 0.1854 \| 0.1354 \| 0.2716 \| 0.4556 \| 0.2219 \| 0.0856 \| 0.2887 \| 0.4537 \| 0.4641 \| 0.6662 \| 0.4797 \| 0.396 \| 0.3862 \| 0.3924 \| 0.6459 \| 0.4055 \| 0.1986 \| \| 0.8259 \| 91.0 \| 9737 \| 1.2576 \| 0.2862 \| 0.575 \| 0.2422 \| 0.5465 \| 0.2978 \| 0.1854 \| 0.1336 \| 0.2678 \| 0.4559 \| 0.2227 \| 0.0904 \| 0.2854 \| 0.4596 \| 0.4715 \| 0.6761 \| 0.5013 \| 0.3964 \| 0.3985 \| 0.3853 \| 0.6493 \| 0.4163 \| 0.2011 \| \| 0.8261 \| 92.0 \| 9844 \| 1.2630 \| 0.2903 \| 0.578 \| 0.2561 \| 0.5502 \| 0.2979 \| 0.191 \| 0.133 \| 0.2793 \| 0.4549 \| 0.2275 \| 0.0854 \| 0.2922 \| 0.4582 \| 0.4681 \| 0.6707 \| 0.4987 \| 0.392 \| 0.3862 \| 0.3929 \| 0.6396 \| 0.4136 \| 0.1935 \| \| 0.8222 \| 93.0 \| 9951 \| 1.2568 \| 0.2926 \| 0.5865 \| 0.2522 \| 0.5484 \| 0.2989 \| 0.1909 \| 0.1454 \| 0.2793 \| 0.4598 \| 0.2371 \| 0.0844 \| 0.2895 \| 0.4604 \| 0.473 \| 0.6766 \| 0.5 \| 0.3938 \| 0.3969 \| 0.3978 \| 0.6476 \| 0.425 \| 0.1893 \| \| 0.815 \| 94.0 \| 10058 \| 1.2552 \| 0.2918 \| 0.5843 \| 0.2481 \| 0.548 \| 0.2894 \| 0.1927 \| 0.1499 \| 0.2788 \| 0.4632 \| 0.2349 \| 0.0855 \| 0.2906 \| 0.4547 \| 0.4687 \| 0.6739 \| 0.4987 \| 0.3942 \| 0.3785 \| 0.3982 \| 0.6562 \| 0.4095 \| 0.1851 \| \| 0.8016 \| 95.0 \| 10165 \| 1.2575 \| 0.2899 \| 0.5824 \| 0.2444 \| 0.5516 \| 0.2855 \| 0.1968 \| 0.1366 \| 0.2789 \| 0.4661 \| 0.2233 \| 0.085 \| 0.2875 \| 0.4542 \| 0.4672 \| 0.6775 \| 0.4962 \| 0.3978 \| 0.3723 \| 0.3924 \| 0.6563 \| 0.4053 \| 0.179 \| \| 0.8191 \| 96.0 \| 10272 \| 1.2532 \| 0.294 \| 0.5848 \| 0.2411 \| 0.5536 \| 0.2938 \| 0.1956 \| 0.1438 \| 0.2833 \| 0.4694 \| 0.225 \| 0.0885 \| 0.2972 \| 0.4581 \| 0.4707 \| 0.677 \| 0.4937 \| 0.3978 \| 0.3785 \| 0.4067 \| 0.6565 \| 0.4116 \| 0.1814 \| \| 0.7974 \| 97.0 \| 10379 \| 1.2629 \| 0.2915 \| 0.5849 \| 0.236 \| 0.5456 \| 0.2891 \| 0.1935 \| 0.1465 \| 0.2827 \| 0.4674 \| 0.2285 \| 0.0838 \| 0.2964 \| 0.4575 \| 0.4695 \| 0.6721 \| 0.4924 \| 0.3897 \| 0.3846 \| 0.4089 \| 0.6555 \| 0.4113 \| 0.1808 \| \| 0.8021 \| 98.0 \| 10486 \| 1.2549 \| 0.2939 \| 0.5839 \| 0.2424 \| 0.5491 \| 0.2927 \| 0.1977 \| 0.1489 \| 0.2811 \| 0.464 \| 0.2358 \| 0.0839 \| 0.2969 \| 0.459 \| 0.4717 \| 0.6739 \| 0.4975 \| 0.3978 \| 0.3846 \| 0.4049 \| 0.652 \| 0.4136 \| 0.193 \| \| 0.8073 \| 99.0 \| 10593 \| 1.2519 \| 0.2945 \| 0.5865 \| 0.241 \| 0.5512 \| 0.2915 \| 0.198 \| 0.1521 \| 0.2798 \| 0.4662 \| 0.2335 \| 0.0834 \| 0.297 \| 0.46 \| 0.4713 \| 0.6761 \| 0.4949 \| 0.396 \| 0.3846 \| 0.4049 \| 0.6554 \| 0.4146 \| 0.1775 \| \| 0.8109 \| 100.0 \| 10700 \| 1.2564 \| 0.2948 \| 0.5884 \| 0.2425 \| 0.55 \| 0.2939 \| 0.1976 \| 0.1503 \| 0.282 \| 0.4679 \| 0.2362 \| 0.0834 \| 0.2966 \| 0.4598 \| 0.4716 \| 0.6766 \| 0.4987 \| 0.3946 \| 0.3846 \| 0.4036 \| 0.6584 \| 0.4137 \| 0.1785 \| ### Framework versions - Transformers 4.45.0.dev0 - Pytorch 2.4.0+cu124 - Datasets 2.21.0 - Tokenizers 0.19.1	[ "coverall", "face_shield", "gloves", "goggles", "mask" ]
MarianaMCruz/detr-finetuned-ppe	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # detr-finetuned-ppe This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the Francesco/construction-safety-gsnvb dataset. It achieves the following results on the evaluation set: - Loss: 1.0499 - Map: 0.3948 - Map 50: 0.7609 - Map 75: 0.3767 - Map Small: 0.0619 - Map Medium: 0.3097 - Map Large: 0.4634 - Mar 1: 0.2239 - Mar 10: 0.5134 - Mar 100: 0.5368 - Mar Small: 0.1158 - Mar Medium: 0.4656 - Mar Large: 0.5905 - Map Helmet: 0.4535 - Mar 100 Helmet: 0.5418 - Map No-helmet: 0.1846 - Mar 100 No-helmet: 0.3125 - Map No-vest: 0.3122 - Mar 100 No-vest: 0.4984 - Map Person: 0.5839 - Mar 100 Person: 0.7285 - Map Vest: 0.4398 - Mar 100 Vest: 0.6031 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Small \| Map Medium \| Map Large \| Mar 1 \| Mar 10 \| Mar 100 \| Mar Small \| Mar Medium \| Mar Large \| Map Helmet \| Mar 100 Helmet \| Map No-helmet \| Mar 100 No-helmet \| Map No-vest \| Mar 100 No-vest \| Map Person \| Mar 100 Person \| Map Vest \| Mar 100 Vest \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:---------:\|:----------:\|:---------:\|:----------:\|:--------------:\|:-------------:\|:-----------------:\|:-----------:\|:---------------:\|:----------:\|:--------------:\|:--------:\|:------------:\| \| 2.5109 \| 1.0 \| 125 \| 1.9828 \| 0.0558 \| 0.1174 \| 0.0493 \| 0.0064 \| 0.0485 \| 0.0694 \| 0.0648 \| 0.1756 \| 0.2281 \| 0.0719 \| 0.136 \| 0.2687 \| 0.0782 \| 0.4985 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2008 \| 0.6421 \| 0.0 \| 0.0 \| \| 1.7905 \| 2.0 \| 250 \| 1.6901 \| 0.0831 \| 0.1758 \| 0.0628 \| 0.018 \| 0.0768 \| 0.1105 \| 0.1162 \| 0.2681 \| 0.3363 \| 0.0789 \| 0.2158 \| 0.4014 \| 0.1284 \| 0.5077 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 0.2131 \| 0.6949 \| 0.0739 \| 0.4789 \| \| 1.6164 \| 3.0 \| 375 \| 1.5478 \| 0.1351 \| 0.2758 \| 0.1104 \| 0.0084 \| 0.1233 \| 0.1683 \| 0.1286 \| 0.3095 \| 0.3542 \| 0.0737 \| 0.2677 \| 0.4032 \| 0.2175 \| 0.5433 \| 0.0 \| 0.0 \| 0.0033 \| 0.0213 \| 0.3345 \| 0.6636 \| 0.1205 \| 0.543 \| \| 1.3782 \| 4.0 \| 500 \| 1.4839 \| 0.1675 \| 0.339 \| 0.1427 \| 0.0194 \| 0.1138 \| 0.2255 \| 0.1403 \| 0.3236 \| 0.3638 \| 0.0754 \| 0.237 \| 0.4257 \| 0.3086 \| 0.5376 \| 0.0 \| 0.0 \| 0.0028 \| 0.0787 \| 0.3963 \| 0.6505 \| 0.1296 \| 0.5523 \| \| 1.2708 \| 5.0 \| 625 \| 1.3486 \| 0.208 \| 0.4184 \| 0.1731 \| 0.012 \| 0.1542 \| 0.267 \| 0.1361 \| 0.3427 \| 0.3664 \| 0.0491 \| 0.3041 \| 0.4139 \| 0.345 \| 0.4974 \| 0.0 \| 0.0 \| 0.0058 \| 0.1459 \| 0.4403 \| 0.6509 \| 0.2488 \| 0.5375 \| \| 1.2041 \| 6.0 \| 750 \| 1.2969 \| 0.225 \| 0.4379 \| 0.2059 \| 0.0235 \| 0.1577 \| 0.2902 \| 0.1632 \| 0.3864 \| 0.4178 \| 0.0614 \| 0.291 \| 0.4897 \| 0.3662 \| 0.5165 \| 0.0 \| 0.0 \| 0.0245 \| 0.4197 \| 0.4365 \| 0.6514 \| 0.2976 \| 0.5016 \| \| 1.167 \| 7.0 \| 875 \| 1.2861 \| 0.2344 \| 0.4597 \| 0.2018 \| 0.0109 \| 0.1613 \| 0.2969 \| 0.1589 \| 0.3848 \| 0.4244 \| 0.0298 \| 0.3216 \| 0.489 \| 0.3452 \| 0.4825 \| 0.0 \| 0.0 \| 0.0349 \| 0.4574 \| 0.4691 \| 0.6397 \| 0.3228 \| 0.5422 \| \| 1.1438 \| 8.0 \| 1000 \| 1.3371 \| 0.2339 \| 0.4571 \| 0.2078 \| 0.0124 \| 0.1638 \| 0.29 \| 0.1722 \| 0.3952 \| 0.421 \| 0.0386 \| 0.3192 \| 0.4806 \| 0.371 \| 0.5046 \| 0.0 \| 0.0 \| 0.0285 \| 0.4656 \| 0.4588 \| 0.6416 \| 0.3113 \| 0.493 \| \| 1.1373 \| 9.0 \| 1125 \| 1.2976 \| 0.2392 \| 0.4773 \| 0.2183 \| 0.0197 \| 0.1831 \| 0.297 \| 0.1647 \| 0.3854 \| 0.4153 \| 0.0544 \| 0.3236 \| 0.479 \| 0.3741 \| 0.5072 \| 0.0 \| 0.0 \| 0.0544 \| 0.382 \| 0.4769 \| 0.6804 \| 0.2905 \| 0.507 \| \| 1.1265 \| 10.0 \| 1250 \| 1.2837 \| 0.2404 \| 0.4778 \| 0.217 \| 0.0218 \| 0.1868 \| 0.2931 \| 0.16 \| 0.3908 \| 0.4199 \| 0.0526 \| 0.3448 \| 0.4705 \| 0.3835 \| 0.5206 \| 0.0 \| 0.0 \| 0.0609 \| 0.4623 \| 0.4815 \| 0.6495 \| 0.2763 \| 0.4672 \| \| 1.0924 \| 11.0 \| 1375 \| 1.1893 \| 0.272 \| 0.5167 \| 0.252 \| 0.0293 \| 0.1892 \| 0.3339 \| 0.1792 \| 0.412 \| 0.4501 \| 0.0667 \| 0.368 \| 0.5018 \| 0.3881 \| 0.5299 \| 0.0 \| 0.0 \| 0.0721 \| 0.5148 \| 0.5236 \| 0.6762 \| 0.376 \| 0.5297 \| \| 1.0577 \| 12.0 \| 1500 \| 1.2619 \| 0.262 \| 0.5288 \| 0.226 \| 0.0366 \| 0.1793 \| 0.3175 \| 0.1741 \| 0.3945 \| 0.4351 \| 0.0719 \| 0.3543 \| 0.4875 \| 0.3686 \| 0.4964 \| 0.0 \| 0.0 \| 0.0684 \| 0.4803 \| 0.4905 \| 0.6621 \| 0.3825 \| 0.5367 \| \| 1.058 \| 13.0 \| 1625 \| 1.2249 \| 0.286 \| 0.535 \| 0.2727 \| 0.029 \| 0.227 \| 0.3347 \| 0.1812 \| 0.4169 \| 0.4501 \| 0.0667 \| 0.3595 \| 0.5052 \| 0.4021 \| 0.5165 \| 0.005 \| 0.0167 \| 0.0932 \| 0.4967 \| 0.5213 \| 0.6916 \| 0.4082 \| 0.5289 \| \| 1.0255 \| 14.0 \| 1750 \| 1.2688 \| 0.2831 \| 0.5428 \| 0.2802 \| 0.0337 \| 0.1855 \| 0.3399 \| 0.1797 \| 0.4067 \| 0.4405 \| 0.0614 \| 0.3503 \| 0.4961 \| 0.4099 \| 0.5258 \| 0.0 \| 0.0 \| 0.1036 \| 0.4934 \| 0.4915 \| 0.6584 \| 0.4104 \| 0.525 \| \| 1.0236 \| 15.0 \| 1875 \| 1.2698 \| 0.2663 \| 0.5138 \| 0.253 \| 0.0281 \| 0.2051 \| 0.3141 \| 0.1742 \| 0.4018 \| 0.4302 \| 0.0579 \| 0.3234 \| 0.4836 \| 0.3949 \| 0.5119 \| 0.0 \| 0.0 \| 0.112 \| 0.4328 \| 0.5151 \| 0.7056 \| 0.3097 \| 0.5008 \| \| 1.0098 \| 16.0 \| 2000 \| 1.2096 \| 0.2869 \| 0.5506 \| 0.2667 \| 0.0269 \| 0.2097 \| 0.3437 \| 0.1897 \| 0.4196 \| 0.4527 \| 0.0667 \| 0.3446 \| 0.515 \| 0.3993 \| 0.5098 \| 0.0021 \| 0.0208 \| 0.0932 \| 0.4754 \| 0.5153 \| 0.6967 \| 0.4246 \| 0.5609 \| \| 0.9636 \| 17.0 \| 2125 \| 1.1379 \| 0.2929 \| 0.564 \| 0.269 \| 0.0285 \| 0.2219 \| 0.3811 \| 0.1874 \| 0.4375 \| 0.4642 \| 0.0754 \| 0.3713 \| 0.5393 \| 0.4182 \| 0.5139 \| 0.0053 \| 0.075 \| 0.1532 \| 0.5426 \| 0.5408 \| 0.7136 \| 0.3469 \| 0.4758 \| \| 0.962 \| 18.0 \| 2250 \| 1.2004 \| 0.2941 \| 0.5636 \| 0.2888 \| 0.0297 \| 0.23 \| 0.3505 \| 0.1887 \| 0.4349 \| 0.4616 \| 0.0509 \| 0.3711 \| 0.5168 \| 0.3959 \| 0.4938 \| 0.0028 \| 0.0458 \| 0.1337 \| 0.5049 \| 0.5262 \| 0.7033 \| 0.412 \| 0.5602 \| \| 0.9555 \| 19.0 \| 2375 \| 1.1228 \| 0.3116 \| 0.5828 \| 0.2922 \| 0.0225 \| 0.2412 \| 0.3766 \| 0.1805 \| 0.4539 \| 0.4877 \| 0.0614 \| 0.3979 \| 0.5619 \| 0.4299 \| 0.5309 \| 0.0073 \| 0.0958 \| 0.1398 \| 0.5393 \| 0.5455 \| 0.7178 \| 0.4358 \| 0.5547 \| \| 0.9251 \| 20.0 \| 2500 \| 1.1541 \| 0.3116 \| 0.5875 \| 0.2999 \| 0.028 \| 0.2293 \| 0.4057 \| 0.2139 \| 0.4652 \| 0.4973 \| 0.0561 \| 0.4187 \| 0.5584 \| 0.4107 \| 0.5 \| 0.0592 \| 0.1958 \| 0.1483 \| 0.5115 \| 0.5294 \| 0.7126 \| 0.4106 \| 0.5664 \| \| 0.9298 \| 21.0 \| 2625 \| 1.1406 \| 0.3091 \| 0.591 \| 0.2913 \| 0.035 \| 0.2095 \| 0.3635 \| 0.2027 \| 0.4631 \| 0.494 \| 0.0632 \| 0.3792 \| 0.5336 \| 0.4337 \| 0.533 \| 0.0212 \| 0.1375 \| 0.1154 \| 0.5016 \| 0.5318 \| 0.7019 \| 0.4432 \| 0.5961 \| \| 0.9384 \| 22.0 \| 2750 \| 1.1709 \| 0.2984 \| 0.5983 \| 0.2653 \| 0.0309 \| 0.2227 \| 0.356 \| 0.1979 \| 0.4421 \| 0.4706 \| 0.0526 \| 0.3809 \| 0.5139 \| 0.3935 \| 0.517 \| 0.0194 \| 0.0792 \| 0.1556 \| 0.5 \| 0.51 \| 0.6879 \| 0.4135 \| 0.5688 \| \| 0.932 \| 23.0 \| 2875 \| 1.1606 \| 0.3024 \| 0.5767 \| 0.2927 \| 0.0143 \| 0.2188 \| 0.3643 \| 0.2012 \| 0.4343 \| 0.4543 \| 0.0351 \| 0.3625 \| 0.5066 \| 0.3937 \| 0.4794 \| 0.0222 \| 0.0708 \| 0.1487 \| 0.4754 \| 0.5293 \| 0.6967 \| 0.4181 \| 0.5492 \| \| 0.9298 \| 24.0 \| 3000 \| 1.1233 \| 0.3052 \| 0.5806 \| 0.2883 \| 0.0302 \| 0.2114 \| 0.3692 \| 0.2053 \| 0.4602 \| 0.4842 \| 0.0632 \| 0.3951 \| 0.5177 \| 0.3979 \| 0.499 \| 0.0304 \| 0.1708 \| 0.1618 \| 0.4754 \| 0.536 \| 0.7107 \| 0.3997 \| 0.5648 \| \| 0.9172 \| 25.0 \| 3125 \| 1.1104 \| 0.3169 \| 0.6001 \| 0.2979 \| 0.0234 \| 0.2242 \| 0.3795 \| 0.2 \| 0.4626 \| 0.4864 \| 0.0491 \| 0.4061 \| 0.5225 \| 0.4179 \| 0.5139 \| 0.0263 \| 0.1042 \| 0.1934 \| 0.5164 \| 0.5305 \| 0.7075 \| 0.4164 \| 0.5898 \| \| 0.8898 \| 26.0 \| 3250 \| 1.1196 \| 0.3241 \| 0.6218 \| 0.3101 \| 0.0504 \| 0.2405 \| 0.4022 \| 0.1959 \| 0.4528 \| 0.4823 \| 0.0807 \| 0.4021 \| 0.5336 \| 0.4387 \| 0.5335 \| 0.0172 \| 0.0917 \| 0.2206 \| 0.5164 \| 0.5061 \| 0.6799 \| 0.4379 \| 0.5898 \| \| 0.8917 \| 27.0 \| 3375 \| 1.0439 \| 0.3374 \| 0.6444 \| 0.3261 \| 0.0355 \| 0.2518 \| 0.4023 \| 0.224 \| 0.4934 \| 0.52 \| 0.0772 \| 0.4439 \| 0.5436 \| 0.45 \| 0.5351 \| 0.0503 \| 0.1875 \| 0.2058 \| 0.5475 \| 0.5475 \| 0.7285 \| 0.4334 \| 0.6016 \| \| 0.8787 \| 28.0 \| 3500 \| 1.1134 \| 0.3303 \| 0.6379 \| 0.3143 \| 0.0197 \| 0.2547 \| 0.378 \| 0.2082 \| 0.4709 \| 0.5017 \| 0.0667 \| 0.418 \| 0.539 \| 0.4113 \| 0.5036 \| 0.0339 \| 0.1292 \| 0.2369 \| 0.5492 \| 0.5332 \| 0.7266 \| 0.4359 \| 0.6 \| \| 0.883 \| 29.0 \| 3625 \| 1.1225 \| 0.3156 \| 0.6336 \| 0.2867 \| 0.025 \| 0.2447 \| 0.3632 \| 0.2014 \| 0.4621 \| 0.4898 \| 0.0746 \| 0.3937 \| 0.5254 \| 0.3868 \| 0.4835 \| 0.0196 \| 0.125 \| 0.2024 \| 0.5328 \| 0.5395 \| 0.7126 \| 0.4297 \| 0.5953 \| \| 0.8696 \| 30.0 \| 3750 \| 1.0521 \| 0.3552 \| 0.68 \| 0.3438 \| 0.0227 \| 0.2741 \| 0.4085 \| 0.2235 \| 0.4986 \| 0.5299 \| 0.0474 \| 0.4498 \| 0.5693 \| 0.4338 \| 0.5191 \| 0.1184 \| 0.2792 \| 0.2113 \| 0.5328 \| 0.558 \| 0.714 \| 0.4547 \| 0.6047 \| \| 0.8639 \| 31.0 \| 3875 \| 1.0890 \| 0.3285 \| 0.6298 \| 0.3099 \| 0.0342 \| 0.2473 \| 0.4063 \| 0.2153 \| 0.4859 \| 0.5049 \| 0.0798 \| 0.4127 \| 0.544 \| 0.435 \| 0.5268 \| 0.0789 \| 0.2417 \| 0.2448 \| 0.5344 \| 0.5462 \| 0.7075 \| 0.3375 \| 0.5141 \| \| 0.8479 \| 32.0 \| 4000 \| 1.0418 \| 0.3613 \| 0.6655 \| 0.3486 \| 0.0553 \| 0.2694 \| 0.4411 \| 0.2169 \| 0.518 \| 0.5364 \| 0.1053 \| 0.4537 \| 0.5705 \| 0.4646 \| 0.5448 \| 0.1411 \| 0.3458 \| 0.2282 \| 0.5066 \| 0.552 \| 0.7033 \| 0.4205 \| 0.5813 \| \| 0.8375 \| 33.0 \| 4125 \| 1.0895 \| 0.342 \| 0.6686 \| 0.3098 \| 0.0374 \| 0.248 \| 0.4414 \| 0.2262 \| 0.4985 \| 0.5157 \| 0.0614 \| 0.4194 \| 0.5884 \| 0.438 \| 0.5242 \| 0.1269 \| 0.325 \| 0.2639 \| 0.5279 \| 0.5288 \| 0.6967 \| 0.3525 \| 0.5047 \| \| 0.8285 \| 34.0 \| 4250 \| 1.0510 \| 0.3576 \| 0.6709 \| 0.3589 \| 0.0289 \| 0.2624 \| 0.4457 \| 0.218 \| 0.5088 \| 0.5358 \| 0.0737 \| 0.4211 \| 0.6168 \| 0.4464 \| 0.532 \| 0.1416 \| 0.3375 \| 0.2019 \| 0.4754 \| 0.5553 \| 0.7093 \| 0.4426 \| 0.625 \| \| 0.8398 \| 35.0 \| 4375 \| 1.0646 \| 0.3448 \| 0.6598 \| 0.3461 \| 0.033 \| 0.2641 \| 0.4097 \| 0.2262 \| 0.5089 \| 0.5354 \| 0.0763 \| 0.4438 \| 0.606 \| 0.431 \| 0.5196 \| 0.1202 \| 0.325 \| 0.1912 \| 0.4836 \| 0.5487 \| 0.7042 \| 0.4327 \| 0.6445 \| \| 0.8327 \| 36.0 \| 4500 \| 1.0480 \| 0.3598 \| 0.6928 \| 0.334 \| 0.0306 \| 0.2819 \| 0.4678 \| 0.2234 \| 0.5114 \| 0.5411 \| 0.0667 \| 0.451 \| 0.6129 \| 0.4196 \| 0.5113 \| 0.1628 \| 0.3833 \| 0.2531 \| 0.4984 \| 0.5459 \| 0.6986 \| 0.4172 \| 0.6141 \| \| 0.8194 \| 37.0 \| 4625 \| 1.0323 \| 0.356 \| 0.6866 \| 0.334 \| 0.0395 \| 0.2632 \| 0.479 \| 0.2202 \| 0.5051 \| 0.5296 \| 0.0746 \| 0.4121 \| 0.6385 \| 0.418 \| 0.5093 \| 0.1338 \| 0.3375 \| 0.2652 \| 0.5 \| 0.5539 \| 0.7238 \| 0.409 \| 0.5773 \| \| 0.82 \| 38.0 \| 4750 \| 1.0444 \| 0.3564 \| 0.6953 \| 0.3388 \| 0.0411 \| 0.2767 \| 0.4383 \| 0.2318 \| 0.5137 \| 0.5363 \| 0.0596 \| 0.4355 \| 0.5846 \| 0.4249 \| 0.5165 \| 0.1367 \| 0.3583 \| 0.2447 \| 0.5148 \| 0.5426 \| 0.6972 \| 0.433 \| 0.5945 \| \| 0.8197 \| 39.0 \| 4875 \| 1.0617 \| 0.359 \| 0.6728 \| 0.337 \| 0.0434 \| 0.2664 \| 0.4577 \| 0.2289 \| 0.5157 \| 0.5415 \| 0.1053 \| 0.4469 \| 0.6131 \| 0.4459 \| 0.5407 \| 0.1162 \| 0.35 \| 0.2497 \| 0.5098 \| 0.554 \| 0.7201 \| 0.4293 \| 0.5867 \| \| 0.8064 \| 40.0 \| 5000 \| 1.0729 \| 0.3635 \| 0.7076 \| 0.3642 \| 0.048 \| 0.268 \| 0.4452 \| 0.2271 \| 0.5046 \| 0.5334 \| 0.086 \| 0.4447 \| 0.6253 \| 0.4333 \| 0.517 \| 0.1675 \| 0.3833 \| 0.2596 \| 0.5148 \| 0.551 \| 0.6958 \| 0.4061 \| 0.5562 \| \| 0.796 \| 41.0 \| 5125 \| 1.1061 \| 0.359 \| 0.7112 \| 0.3253 \| 0.0359 \| 0.2875 \| 0.43 \| 0.2193 \| 0.5111 \| 0.5356 \| 0.0675 \| 0.4415 \| 0.5808 \| 0.3865 \| 0.4897 \| 0.136 \| 0.3375 \| 0.2818 \| 0.5197 \| 0.5513 \| 0.7028 \| 0.4391 \| 0.6281 \| \| 0.8013 \| 42.0 \| 5250 \| 1.0606 \| 0.3659 \| 0.7225 \| 0.3409 \| 0.0227 \| 0.275 \| 0.4292 \| 0.218 \| 0.5087 \| 0.5343 \| 0.0579 \| 0.446 \| 0.5935 \| 0.4211 \| 0.5093 \| 0.1117 \| 0.2708 \| 0.2921 \| 0.5508 \| 0.5599 \| 0.7037 \| 0.4448 \| 0.6367 \| \| 0.7947 \| 43.0 \| 5375 \| 1.0473 \| 0.3659 \| 0.699 \| 0.3413 \| 0.0494 \| 0.2698 \| 0.4622 \| 0.2311 \| 0.5197 \| 0.5491 \| 0.1211 \| 0.4527 \| 0.6269 \| 0.4211 \| 0.5134 \| 0.167 \| 0.425 \| 0.2655 \| 0.5311 \| 0.559 \| 0.707 \| 0.417 \| 0.5688 \| \| 0.792 \| 44.0 \| 5500 \| 1.0752 \| 0.3654 \| 0.7163 \| 0.3508 \| 0.0469 \| 0.2804 \| 0.4471 \| 0.2243 \| 0.5066 \| 0.531 \| 0.0877 \| 0.441 \| 0.596 \| 0.4316 \| 0.5402 \| 0.1157 \| 0.275 \| 0.2922 \| 0.518 \| 0.5537 \| 0.6991 \| 0.4337 \| 0.6227 \| \| 0.7713 \| 45.0 \| 5625 \| 1.0290 \| 0.3786 \| 0.7036 \| 0.3584 \| 0.0495 \| 0.2775 \| 0.459 \| 0.2225 \| 0.5189 \| 0.5448 \| 0.0737 \| 0.4521 \| 0.6117 \| 0.4421 \| 0.5227 \| 0.1884 \| 0.3542 \| 0.2585 \| 0.5197 \| 0.5701 \| 0.7407 \| 0.4341 \| 0.5867 \| \| 0.7569 \| 46.0 \| 5750 \| 1.0664 \| 0.3702 \| 0.7162 \| 0.3385 \| 0.0241 \| 0.3008 \| 0.446 \| 0.2249 \| 0.5208 \| 0.5498 \| 0.0509 \| 0.4528 \| 0.6383 \| 0.4071 \| 0.4979 \| 0.1928 \| 0.375 \| 0.2405 \| 0.5197 \| 0.56 \| 0.7182 \| 0.4506 \| 0.6383 \| \| 0.7632 \| 47.0 \| 5875 \| 1.0491 \| 0.3669 \| 0.7101 \| 0.3577 \| 0.047 \| 0.2875 \| 0.4469 \| 0.2218 \| 0.5138 \| 0.543 \| 0.136 \| 0.462 \| 0.621 \| 0.4487 \| 0.5459 \| 0.1173 \| 0.3125 \| 0.2859 \| 0.5361 \| 0.5676 \| 0.7285 \| 0.4149 \| 0.5922 \| \| 0.7693 \| 48.0 \| 6000 \| 1.0445 \| 0.3664 \| 0.6865 \| 0.3622 \| 0.0635 \| 0.2783 \| 0.4332 \| 0.2275 \| 0.5122 \| 0.5407 \| 0.0965 \| 0.44 \| 0.6027 \| 0.4581 \| 0.5381 \| 0.1285 \| 0.3125 \| 0.2623 \| 0.5279 \| 0.5748 \| 0.7271 \| 0.4085 \| 0.5977 \| \| 0.7516 \| 49.0 \| 6125 \| 1.0276 \| 0.374 \| 0.7253 \| 0.3508 \| 0.0452 \| 0.2669 \| 0.4398 \| 0.2265 \| 0.5035 \| 0.5359 \| 0.0684 \| 0.4354 \| 0.6124 \| 0.4327 \| 0.5216 \| 0.131 \| 0.3208 \| 0.2946 \| 0.4934 \| 0.5596 \| 0.7313 \| 0.452 \| 0.6125 \| \| 0.7538 \| 50.0 \| 6250 \| 1.0193 \| 0.3692 \| 0.7065 \| 0.3365 \| 0.0591 \| 0.2716 \| 0.4337 \| 0.2229 \| 0.5147 \| 0.5464 \| 0.0965 \| 0.4402 \| 0.6081 \| 0.4553 \| 0.5448 \| 0.1087 \| 0.3333 \| 0.2603 \| 0.4869 \| 0.5641 \| 0.7192 \| 0.4576 \| 0.6477 \| \| 0.7504 \| 51.0 \| 6375 \| 1.0346 \| 0.3685 \| 0.7171 \| 0.3413 \| 0.0905 \| 0.2821 \| 0.4316 \| 0.2249 \| 0.521 \| 0.5556 \| 0.1386 \| 0.4632 \| 0.6369 \| 0.4318 \| 0.5124 \| 0.1205 \| 0.3583 \| 0.2767 \| 0.5361 \| 0.5592 \| 0.7234 \| 0.4542 \| 0.6477 \| \| 0.7429 \| 52.0 \| 6500 \| 1.0513 \| 0.3646 \| 0.6926 \| 0.3456 \| 0.0475 \| 0.2755 \| 0.4719 \| 0.2277 \| 0.518 \| 0.5514 \| 0.0807 \| 0.4627 \| 0.6297 \| 0.4381 \| 0.5237 \| 0.1272 \| 0.3875 \| 0.2817 \| 0.5148 \| 0.5639 \| 0.7271 \| 0.4123 \| 0.6039 \| \| 0.7353 \| 53.0 \| 6625 \| 1.0335 \| 0.3694 \| 0.687 \| 0.3477 \| 0.0545 \| 0.2744 \| 0.4644 \| 0.2298 \| 0.5102 \| 0.543 \| 0.0912 \| 0.4469 \| 0.6114 \| 0.4364 \| 0.5232 \| 0.1481 \| 0.4 \| 0.2684 \| 0.4557 \| 0.572 \| 0.7407 \| 0.4221 \| 0.5953 \| \| 0.7437 \| 54.0 \| 6750 \| 1.0574 \| 0.3717 \| 0.7126 \| 0.3367 \| 0.0362 \| 0.2924 \| 0.4434 \| 0.2248 \| 0.5097 \| 0.5369 \| 0.0842 \| 0.438 \| 0.6111 \| 0.4318 \| 0.5201 \| 0.1387 \| 0.3375 \| 0.2964 \| 0.4918 \| 0.5696 \| 0.7243 \| 0.4218 \| 0.6109 \| \| 0.7356 \| 55.0 \| 6875 \| 1.0435 \| 0.3815 \| 0.7309 \| 0.3448 \| 0.0371 \| 0.3081 \| 0.4492 \| 0.22 \| 0.5156 \| 0.5521 \| 0.093 \| 0.4667 \| 0.6244 \| 0.4181 \| 0.5046 \| 0.1659 \| 0.3792 \| 0.33 \| 0.5311 \| 0.5719 \| 0.7383 \| 0.4218 \| 0.607 \| \| 0.7278 \| 56.0 \| 7000 \| 1.0575 \| 0.3789 \| 0.7154 \| 0.365 \| 0.0471 \| 0.3017 \| 0.456 \| 0.223 \| 0.5054 \| 0.5351 \| 0.0684 \| 0.4455 \| 0.6165 \| 0.4294 \| 0.517 \| 0.1677 \| 0.3208 \| 0.2983 \| 0.5148 \| 0.5725 \| 0.7215 \| 0.4264 \| 0.6016 \| \| 0.7322 \| 57.0 \| 7125 \| 1.0405 \| 0.3746 \| 0.7104 \| 0.3383 \| 0.0543 \| 0.2889 \| 0.4816 \| 0.2296 \| 0.5216 \| 0.5524 \| 0.1088 \| 0.4479 \| 0.6339 \| 0.4292 \| 0.5206 \| 0.15 \| 0.4 \| 0.2867 \| 0.4852 \| 0.5619 \| 0.7294 \| 0.4454 \| 0.6266 \| \| 0.719 \| 58.0 \| 7250 \| 1.0121 \| 0.3835 \| 0.7468 \| 0.3546 \| 0.066 \| 0.3042 \| 0.4607 \| 0.228 \| 0.524 \| 0.558 \| 0.114 \| 0.4667 \| 0.6315 \| 0.451 \| 0.5428 \| 0.179 \| 0.4125 \| 0.2806 \| 0.4738 \| 0.5579 \| 0.7243 \| 0.4487 \| 0.6367 \| \| 0.7097 \| 59.0 \| 7375 \| 1.0164 \| 0.3814 \| 0.7456 \| 0.3344 \| 0.0614 \| 0.3027 \| 0.437 \| 0.2252 \| 0.5141 \| 0.5352 \| 0.0982 \| 0.4579 \| 0.5734 \| 0.4339 \| 0.5253 \| 0.163 \| 0.3208 \| 0.2944 \| 0.4934 \| 0.5728 \| 0.7145 \| 0.4429 \| 0.6219 \| \| 0.6941 \| 60.0 \| 7500 \| 1.0747 \| 0.3672 \| 0.7035 \| 0.3351 \| 0.0463 \| 0.2865 \| 0.4363 \| 0.2281 \| 0.5008 \| 0.5285 \| 0.0842 \| 0.4362 \| 0.6127 \| 0.4181 \| 0.5072 \| 0.1872 \| 0.3792 \| 0.3047 \| 0.5328 \| 0.5736 \| 0.7327 \| 0.3523 \| 0.4906 \| \| 0.7108 \| 61.0 \| 7625 \| 1.0140 \| 0.3956 \| 0.7255 \| 0.3997 \| 0.0649 \| 0.3137 \| 0.4592 \| 0.2351 \| 0.5327 \| 0.5548 \| 0.164 \| 0.4674 \| 0.6131 \| 0.4544 \| 0.5412 \| 0.1958 \| 0.375 \| 0.3081 \| 0.5033 \| 0.5788 \| 0.7318 \| 0.4408 \| 0.6227 \| \| 0.6952 \| 62.0 \| 7750 \| 1.0354 \| 0.3877 \| 0.7308 \| 0.3723 \| 0.055 \| 0.3067 \| 0.441 \| 0.2349 \| 0.5143 \| 0.5376 \| 0.1 \| 0.4521 \| 0.5973 \| 0.4315 \| 0.5191 \| 0.1995 \| 0.3333 \| 0.3031 \| 0.5066 \| 0.5783 \| 0.7327 \| 0.4262 \| 0.5961 \| \| 0.6899 \| 63.0 \| 7875 \| 1.0220 \| 0.3822 \| 0.7322 \| 0.3614 \| 0.0478 \| 0.2985 \| 0.4276 \| 0.2257 \| 0.5098 \| 0.5375 \| 0.0947 \| 0.4565 \| 0.5916 \| 0.4354 \| 0.5258 \| 0.18 \| 0.3292 \| 0.2982 \| 0.4984 \| 0.564 \| 0.7262 \| 0.4334 \| 0.6078 \| \| 0.6958 \| 64.0 \| 8000 \| 0.9985 \| 0.3976 \| 0.7625 \| 0.363 \| 0.0559 \| 0.3047 \| 0.459 \| 0.2245 \| 0.5216 \| 0.5472 \| 0.1123 \| 0.4562 \| 0.6028 \| 0.4425 \| 0.5366 \| 0.2016 \| 0.3333 \| 0.3182 \| 0.5 \| 0.568 \| 0.7327 \| 0.4575 \| 0.6336 \| \| 0.6946 \| 65.0 \| 8125 \| 1.0314 \| 0.3914 \| 0.7321 \| 0.3656 \| 0.0616 \| 0.3101 \| 0.438 \| 0.2253 \| 0.5217 \| 0.5472 \| 0.1 \| 0.4643 \| 0.6156 \| 0.4418 \| 0.5325 \| 0.1895 \| 0.3333 \| 0.3056 \| 0.5148 \| 0.5742 \| 0.7327 \| 0.4461 \| 0.6227 \| \| 0.6716 \| 66.0 \| 8250 \| 1.0477 \| 0.3792 \| 0.7178 \| 0.3634 \| 0.0537 \| 0.2806 \| 0.4455 \| 0.2317 \| 0.5114 \| 0.5364 \| 0.1035 \| 0.4339 \| 0.6199 \| 0.4299 \| 0.5237 \| 0.199 \| 0.3542 \| 0.2903 \| 0.5082 \| 0.565 \| 0.7215 \| 0.4119 \| 0.5742 \| \| 0.6771 \| 67.0 \| 8375 \| 1.0453 \| 0.3932 \| 0.747 \| 0.3852 \| 0.0468 \| 0.2993 \| 0.4701 \| 0.2243 \| 0.5267 \| 0.5499 \| 0.0912 \| 0.4802 \| 0.5973 \| 0.4397 \| 0.5335 \| 0.1758 \| 0.375 \| 0.3342 \| 0.5131 \| 0.5649 \| 0.7178 \| 0.4514 \| 0.6102 \| \| 0.6752 \| 68.0 \| 8500 \| 1.0427 \| 0.3936 \| 0.7533 \| 0.3712 \| 0.0615 \| 0.2892 \| 0.4722 \| 0.2287 \| 0.5139 \| 0.537 \| 0.0851 \| 0.4403 \| 0.6085 \| 0.4357 \| 0.5211 \| 0.184 \| 0.3125 \| 0.3235 \| 0.518 \| 0.5744 \| 0.7308 \| 0.4503 \| 0.6023 \| \| 0.6732 \| 69.0 \| 8625 \| 1.0298 \| 0.3996 \| 0.7544 \| 0.3858 \| 0.0739 \| 0.3003 \| 0.4836 \| 0.2361 \| 0.5265 \| 0.5549 \| 0.1614 \| 0.4536 \| 0.6302 \| 0.4538 \| 0.5423 \| 0.2011 \| 0.3958 \| 0.3333 \| 0.5082 \| 0.5811 \| 0.7374 \| 0.4287 \| 0.5906 \| \| 0.6685 \| 70.0 \| 8750 \| 1.0314 \| 0.3921 \| 0.749 \| 0.358 \| 0.0533 \| 0.3091 \| 0.4485 \| 0.2309 \| 0.5113 \| 0.5384 \| 0.1061 \| 0.4475 \| 0.596 \| 0.4487 \| 0.5345 \| 0.1969 \| 0.3417 \| 0.3054 \| 0.5 \| 0.5722 \| 0.7136 \| 0.4371 \| 0.6023 \| \| 0.663 \| 71.0 \| 8875 \| 1.0564 \| 0.3851 \| 0.7407 \| 0.3575 \| 0.0467 \| 0.3091 \| 0.4536 \| 0.2195 \| 0.5054 \| 0.5291 \| 0.093 \| 0.4607 \| 0.5722 \| 0.4294 \| 0.517 \| 0.181 \| 0.3042 \| 0.3183 \| 0.5197 \| 0.5651 \| 0.7033 \| 0.4317 \| 0.6016 \| \| 0.6497 \| 72.0 \| 9000 \| 1.0388 \| 0.3996 \| 0.7587 \| 0.365 \| 0.0601 \| 0.3128 \| 0.4669 \| 0.2296 \| 0.5162 \| 0.5447 \| 0.1018 \| 0.459 \| 0.6203 \| 0.4496 \| 0.5304 \| 0.2049 \| 0.3625 \| 0.3233 \| 0.4885 \| 0.5801 \| 0.728 \| 0.4402 \| 0.6141 \| \| 0.6479 \| 73.0 \| 9125 \| 1.0326 \| 0.3948 \| 0.7596 \| 0.3619 \| 0.0539 \| 0.3109 \| 0.4588 \| 0.2306 \| 0.5127 \| 0.5396 \| 0.093 \| 0.4613 \| 0.5903 \| 0.4449 \| 0.5294 \| 0.1929 \| 0.3333 \| 0.3218 \| 0.4984 \| 0.5808 \| 0.7355 \| 0.4337 \| 0.6016 \| \| 0.6681 \| 74.0 \| 9250 \| 1.0166 \| 0.3931 \| 0.747 \| 0.3762 \| 0.0528 \| 0.3058 \| 0.4622 \| 0.2261 \| 0.5163 \| 0.5376 \| 0.107 \| 0.4503 \| 0.5846 \| 0.4533 \| 0.5412 \| 0.1658 \| 0.3292 \| 0.342 \| 0.4934 \| 0.5629 \| 0.7271 \| 0.4417 \| 0.5969 \| \| 0.6632 \| 75.0 \| 9375 \| 1.0265 \| 0.3993 \| 0.7544 \| 0.3837 \| 0.0528 \| 0.3158 \| 0.46 \| 0.2328 \| 0.5144 \| 0.5432 \| 0.107 \| 0.4664 \| 0.6026 \| 0.4507 \| 0.5443 \| 0.2036 \| 0.3125 \| 0.3309 \| 0.5 \| 0.5722 \| 0.7341 \| 0.4391 \| 0.625 \| \| 0.6359 \| 76.0 \| 9500 \| 1.0260 \| 0.3937 \| 0.7561 \| 0.3659 \| 0.0653 \| 0.3062 \| 0.4582 \| 0.2289 \| 0.5119 \| 0.5446 \| 0.1228 \| 0.466 \| 0.6294 \| 0.4545 \| 0.5454 \| 0.1809 \| 0.325 \| 0.3197 \| 0.5016 \| 0.5885 \| 0.7439 \| 0.4248 \| 0.607 \| \| 0.6419 \| 77.0 \| 9625 \| 1.0309 \| 0.3921 \| 0.7408 \| 0.366 \| 0.0555 \| 0.2971 \| 0.4838 \| 0.2322 \| 0.512 \| 0.5414 \| 0.086 \| 0.4503 \| 0.6169 \| 0.4278 \| 0.5134 \| 0.183 \| 0.35 \| 0.3302 \| 0.5049 \| 0.586 \| 0.7308 \| 0.4337 \| 0.6078 \| \| 0.6379 \| 78.0 \| 9750 \| 1.0483 \| 0.3911 \| 0.7289 \| 0.3711 \| 0.0684 \| 0.2959 \| 0.4524 \| 0.23 \| 0.5112 \| 0.5358 \| 0.1228 \| 0.452 \| 0.5746 \| 0.4508 \| 0.5433 \| 0.1882 \| 0.2958 \| 0.308 \| 0.4984 \| 0.5904 \| 0.7393 \| 0.4182 \| 0.6023 \| \| 0.6355 \| 79.0 \| 9875 \| 1.0382 \| 0.3883 \| 0.7481 \| 0.3549 \| 0.0627 \| 0.2954 \| 0.4571 \| 0.2302 \| 0.5076 \| 0.5334 \| 0.1018 \| 0.4463 \| 0.5883 \| 0.4503 \| 0.5361 \| 0.183 \| 0.325 \| 0.3161 \| 0.4902 \| 0.5763 \| 0.7355 \| 0.4157 \| 0.5805 \| \| 0.6348 \| 80.0 \| 10000 \| 1.0524 \| 0.3949 \| 0.7432 \| 0.383 \| 0.0646 \| 0.2971 \| 0.4503 \| 0.2304 \| 0.5097 \| 0.535 \| 0.1263 \| 0.4598 \| 0.5552 \| 0.4613 \| 0.5515 \| 0.1776 \| 0.275 \| 0.329 \| 0.5098 \| 0.5774 \| 0.7294 \| 0.4293 \| 0.6094 \| \| 0.6286 \| 81.0 \| 10125 \| 1.0426 \| 0.3957 \| 0.7445 \| 0.3925 \| 0.0583 \| 0.306 \| 0.4901 \| 0.2298 \| 0.5086 \| 0.5349 \| 0.1158 \| 0.4637 \| 0.5952 \| 0.454 \| 0.5479 \| 0.1911 \| 0.2958 \| 0.3362 \| 0.4951 \| 0.566 \| 0.7262 \| 0.4312 \| 0.6094 \| \| 0.6236 \| 82.0 \| 10250 \| 1.0544 \| 0.3878 \| 0.7336 \| 0.3682 \| 0.0487 \| 0.296 \| 0.4629 \| 0.2239 \| 0.5005 \| 0.527 \| 0.1193 \| 0.4405 \| 0.5747 \| 0.4507 \| 0.5392 \| 0.1724 \| 0.2875 \| 0.3215 \| 0.4852 \| 0.5682 \| 0.7215 \| 0.426 \| 0.6016 \| \| 0.63 \| 83.0 \| 10375 \| 1.0655 \| 0.3886 \| 0.7454 \| 0.3761 \| 0.065 \| 0.2948 \| 0.4519 \| 0.2205 \| 0.5194 \| 0.5421 \| 0.1544 \| 0.4551 \| 0.606 \| 0.4515 \| 0.5381 \| 0.1845 \| 0.3333 \| 0.3119 \| 0.4918 \| 0.5753 \| 0.7308 \| 0.4199 \| 0.6164 \| \| 0.6245 \| 84.0 \| 10500 \| 1.0492 \| 0.3898 \| 0.7552 \| 0.3627 \| 0.0612 \| 0.2927 \| 0.4624 \| 0.2323 \| 0.5138 \| 0.5363 \| 0.1211 \| 0.445 \| 0.5873 \| 0.4403 \| 0.5247 \| 0.2005 \| 0.3417 \| 0.3085 \| 0.4902 \| 0.5759 \| 0.729 \| 0.4237 \| 0.5961 \| \| 0.6174 \| 85.0 \| 10625 \| 1.0339 \| 0.4002 \| 0.7577 \| 0.3794 \| 0.0641 \| 0.3085 \| 0.4772 \| 0.2314 \| 0.5197 \| 0.5449 \| 0.1088 \| 0.4639 \| 0.6087 \| 0.4426 \| 0.5376 \| 0.2042 \| 0.3458 \| 0.33 \| 0.5082 \| 0.5884 \| 0.7266 \| 0.4356 \| 0.6062 \| \| 0.6133 \| 86.0 \| 10750 \| 1.0456 \| 0.3959 \| 0.7531 \| 0.3756 \| 0.0544 \| 0.3039 \| 0.4799 \| 0.2319 \| 0.515 \| 0.5365 \| 0.1088 \| 0.461 \| 0.5995 \| 0.4534 \| 0.5423 \| 0.18 \| 0.2958 \| 0.3258 \| 0.4967 \| 0.5835 \| 0.7299 \| 0.4369 \| 0.618 \| \| 0.6081 \| 87.0 \| 10875 \| 1.0713 \| 0.3883 \| 0.7387 \| 0.3777 \| 0.0603 \| 0.3018 \| 0.4519 \| 0.2255 \| 0.4967 \| 0.523 \| 0.1123 \| 0.4519 \| 0.5789 \| 0.4519 \| 0.5371 \| 0.1789 \| 0.2708 \| 0.3117 \| 0.4836 \| 0.578 \| 0.7224 \| 0.421 \| 0.6008 \| \| 0.6096 \| 88.0 \| 11000 \| 1.0603 \| 0.3939 \| 0.7597 \| 0.3706 \| 0.0631 \| 0.2977 \| 0.4767 \| 0.2241 \| 0.5097 \| 0.5317 \| 0.1088 \| 0.4551 \| 0.5866 \| 0.4463 \| 0.5361 \| 0.1858 \| 0.2792 \| 0.3182 \| 0.5049 \| 0.5782 \| 0.7257 \| 0.4412 \| 0.6125 \| \| 0.6164 \| 89.0 \| 11125 \| 1.0610 \| 0.3948 \| 0.7531 \| 0.3699 \| 0.0534 \| 0.305 \| 0.4605 \| 0.227 \| 0.5168 \| 0.5353 \| 0.1088 \| 0.4595 \| 0.5858 \| 0.4488 \| 0.5351 \| 0.1898 \| 0.2958 \| 0.3237 \| 0.5131 \| 0.5813 \| 0.7299 \| 0.4305 \| 0.6023 \| \| 0.6034 \| 90.0 \| 11250 \| 1.0559 \| 0.3984 \| 0.775 \| 0.3731 \| 0.0629 \| 0.3104 \| 0.4689 \| 0.2256 \| 0.5148 \| 0.5355 \| 0.1035 \| 0.4579 \| 0.593 \| 0.4423 \| 0.5314 \| 0.2074 \| 0.3 \| 0.3173 \| 0.5049 \| 0.5825 \| 0.7262 \| 0.4426 \| 0.6148 \| \| 0.602 \| 91.0 \| 11375 \| 1.0424 \| 0.3992 \| 0.757 \| 0.3948 \| 0.0629 \| 0.3061 \| 0.4826 \| 0.2291 \| 0.5162 \| 0.5392 \| 0.114 \| 0.4584 \| 0.6199 \| 0.4506 \| 0.5366 \| 0.2115 \| 0.3208 \| 0.3256 \| 0.5 \| 0.5893 \| 0.7369 \| 0.4191 \| 0.6016 \| \| 0.6038 \| 92.0 \| 11500 \| 1.0444 \| 0.396 \| 0.767 \| 0.3642 \| 0.0553 \| 0.3041 \| 0.4848 \| 0.2288 \| 0.5147 \| 0.5396 \| 0.093 \| 0.4538 \| 0.6288 \| 0.439 \| 0.5263 \| 0.1974 \| 0.3375 \| 0.316 \| 0.4934 \| 0.5878 \| 0.7308 \| 0.4398 \| 0.6102 \| \| 0.5982 \| 93.0 \| 11625 \| 1.0463 \| 0.3973 \| 0.7604 \| 0.3895 \| 0.0561 \| 0.3099 \| 0.4892 \| 0.2252 \| 0.5157 \| 0.5451 \| 0.1035 \| 0.4678 \| 0.6353 \| 0.4483 \| 0.534 \| 0.1939 \| 0.3375 \| 0.3251 \| 0.5115 \| 0.5873 \| 0.7308 \| 0.4321 \| 0.6117 \| \| 0.5879 \| 94.0 \| 11750 \| 1.0578 \| 0.393 \| 0.7637 \| 0.373 \| 0.0607 \| 0.3019 \| 0.4779 \| 0.2259 \| 0.5173 \| 0.5394 \| 0.1158 \| 0.4641 \| 0.6119 \| 0.4539 \| 0.5443 \| 0.1773 \| 0.3208 \| 0.312 \| 0.4902 \| 0.5835 \| 0.7299 \| 0.438 \| 0.6117 \| \| 0.6044 \| 95.0 \| 11875 \| 1.0631 \| 0.3917 \| 0.7619 \| 0.3591 \| 0.0591 \| 0.3023 \| 0.477 \| 0.222 \| 0.51 \| 0.5345 \| 0.1035 \| 0.4526 \| 0.6163 \| 0.454 \| 0.5392 \| 0.178 \| 0.3167 \| 0.3152 \| 0.4951 \| 0.5826 \| 0.7285 \| 0.4286 \| 0.593 \| \| 0.5918 \| 96.0 \| 12000 \| 1.0576 \| 0.3961 \| 0.7651 \| 0.3802 \| 0.0559 \| 0.3026 \| 0.4708 \| 0.2261 \| 0.514 \| 0.538 \| 0.1035 \| 0.4589 \| 0.5961 \| 0.4513 \| 0.5376 \| 0.1855 \| 0.3042 \| 0.3217 \| 0.5016 \| 0.5842 \| 0.7308 \| 0.4377 \| 0.6156 \| \| 0.5973 \| 97.0 \| 12125 \| 1.0587 \| 0.3938 \| 0.758 \| 0.3776 \| 0.0582 \| 0.2962 \| 0.4658 \| 0.2226 \| 0.5132 \| 0.535 \| 0.107 \| 0.4528 \| 0.5931 \| 0.4496 \| 0.5361 \| 0.1832 \| 0.3083 \| 0.3225 \| 0.5 \| 0.5842 \| 0.7313 \| 0.4293 \| 0.5992 \| \| 0.5919 \| 98.0 \| 12250 \| 1.0515 \| 0.3936 \| 0.7612 \| 0.3704 \| 0.062 \| 0.3038 \| 0.4771 \| 0.2188 \| 0.5096 \| 0.54 \| 0.1123 \| 0.4635 \| 0.6172 \| 0.4557 \| 0.5418 \| 0.1783 \| 0.3292 \| 0.3147 \| 0.4885 \| 0.584 \| 0.728 \| 0.4353 \| 0.6125 \| \| 0.5931 \| 99.0 \| 12375 \| 1.0502 \| 0.3947 \| 0.7609 \| 0.3773 \| 0.0599 \| 0.3094 \| 0.4612 \| 0.2229 \| 0.5116 \| 0.5366 \| 0.1123 \| 0.4615 \| 0.5916 \| 0.4537 \| 0.5407 \| 0.1847 \| 0.3125 \| 0.3124 \| 0.4984 \| 0.5849 \| 0.7313 \| 0.4378 \| 0.6 \| \| 0.5924 \| 100.0 \| 12500 \| 1.0499 \| 0.3948 \| 0.7609 \| 0.3767 \| 0.0619 \| 0.3097 \| 0.4634 \| 0.2239 \| 0.5134 \| 0.5368 \| 0.1158 \| 0.4656 \| 0.5905 \| 0.4535 \| 0.5418 \| 0.1846 \| 0.3125 \| 0.3122 \| 0.4984 \| 0.5839 \| 0.7285 \| 0.4398 \| 0.6031 \| ### Framework versions - Transformers 4.45.0.dev0 - Pytorch 2.4.0+cu124 - Datasets 2.21.0 - Tokenizers 0.19.1	[ "construction-safety", "helmet", "no-helmet", "no-vest", "person", "vest" ]
MedicalVision/test_remove	## Original result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.005 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.005 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.005 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = 0.203 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.068 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.005 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.029 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.029 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.029 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = 0.200 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.067 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.029 ``` ## After training result ``` IoU metric: bbox Average Precision (AP) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.009 Average Precision (AP) @[ IoU=0.50 \| area= all \| maxDets=100 ] = 0.020 Average Precision (AP) @[ IoU=0.75 \| area= all \| maxDets=100 ] = 0.008 Average Precision (AP) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.009 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 1 ] = 0.043 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets= 10 ] = 0.076 Average Recall (AR) @[ IoU=0.50:0.95 \| area= all \| maxDets=100 ] = 0.087 Average Recall (AR) @[ IoU=0.50:0.95 \| area= small \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area=medium \| maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 \| area= large \| maxDets=100 ] = 0.089 ``` ## Config - dataset: VinXray - original model: hustvl/yolos-tiny - lr: 0.0001 - dropout_rate: 0.1 - weight_decay: 0.0001 - max_epochs: 1 - train samples: 67234 ## Logging ### Training process ``` {'validation_loss': tensor(8.5927, device='cuda:0'), 'validation_loss_ce': tensor(3.4775, device='cuda:0'), 'validation_loss_bbox': tensor(0.5756, device='cuda:0'), 'validation_loss_giou': tensor(1.1184, device='cuda:0'), 'validation_cardinality_error': tensor(99.5938, device='cuda:0')} {'training_loss': tensor(1.3630, device='cuda:0'), 'train_loss_ce': tensor(0.2593, device='cuda:0'), 'train_loss_bbox': tensor(0.0803, device='cuda:0'), 'train_loss_giou': tensor(0.3511, device='cuda:0'), 'train_cardinality_error': tensor(0.5294, device='cuda:0'), 'validation_loss': tensor(1.5262, device='cuda:0'), 'validation_loss_ce': tensor(0.2351, device='cuda:0'), 'validation_loss_bbox': tensor(0.0827, device='cuda:0'), 'validation_loss_giou': tensor(0.4389, device='cuda:0'), 'validation_cardinality_error': tensor(0.4794, device='cuda:0')} ``` ## Examples {'size': tensor([560, 512]), 'image_id': tensor([1]), 'class_labels': tensor([], dtype=torch.int64), 'boxes': tensor([], size=(0, 4)), 'area': tensor([]), 'iscrowd': tensor([], dtype=torch.int64), 'orig_size': tensor([2580, 2332])} ![Example](./example.png)	[ "aortic enlargement", "atelectasis", "calcification", "cardiomegaly", "consolidation", "ild", "infiltration", "lung opacity", "nodule/mass", "other lesion", "pleural effusion", "pleural thickening", "pneumothorax", "pulmonary fibrosis", "no finding" ]
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