Update README.md

README.md

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----
-license: apache-2.0
----
+---
+tags:
+- object-detection
+- birder
+- pytorch
+library_name: birder
+license: apache-2.0
+---
+
+# Model Card for deformable_detr_boxref_coco_convnext_v2_tiny_imagenet21k
+
+A Deformable DETR with box refinement object detection model with ConvNeXt v2 Tiny backbone (pre-trained on ImageNet-21k) and trained on COCO 2017 dataset.
+
+## Model Details
+
+- **Model Type:** Object detection
+- **Model Stats:**
+    - Params (M): 40.0
+    - Input image size: 640 x 640 (short side)
+- **Dataset:** COCO 2017 (91 classes)
+
+- **Papers:**
+    - Deformable DETR: Deformable Transformers for End-to-End Object Detection: <https://arxiv.org/abs/2010.04159>
+    - ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders: <https://arxiv.org/abs/2301.00808>
+
+- **Metrics:**
+    - mAP @ 512x512px: 41.04
+    - mAP @ 640px (short side) BS=1 (w/o masking): 45.75
+    - mAP @ 640px (short side) BS=2 (w. masking): 45.77
+    - mAP @ 800px (short side) BS=1 (w/o masking): 46.68
+    - mAP @ 800px (short side) BS=2 (w. masking): 46.63
+
+## Model Usage
+
+### Object Detection
+
+```python
+import birder
+from birder.inference.detection import infer_image
+
+(net, model_info) = birder.load_pretrained_model("deformable_detr_boxref_coco_convnext_v2_tiny_imagenet21k", inference=True)
+
+# Get the image size the model was trained on
+size = birder.get_size_from_signature(model_info.signature)
+
+# Create an inference transform
+transform = birder.detection_transform(size, model_info.rgb_stats, dynamic_size=model_info.signature["dynamic"])
+
+image = "path/to/image.jpeg"  # or a PIL image, must be loaded in RGB format
+detections = infer_image(net, image, transform)
+# detections is a dict with keys: 'boxes', 'labels', 'scores'
+# boxes: torch.Tensor with shape (N, 4) in [x1, y1, x2, y2] format
+# labels: torch.Tensor with shape (N,) containing class indices
+# scores: torch.Tensor with shape (N,) containing confidence scores
+```
+
+## Citation
+
+```bibtex
+@misc{zhu2021deformabledetrdeformabletransformers,
+      title={Deformable DETR: Deformable Transformers for End-to-End Object Detection},
+      author={Xizhou Zhu and Weijie Su and Lewei Lu and Bin Li and Xiaogang Wang and Jifeng Dai},
+      year={2021},
+      eprint={2010.04159},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2010.04159},
+}
+
+@misc{woo2023convnextv2codesigningscaling,
+      title={ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders},
+      author={Sanghyun Woo and Shoubhik Debnath and Ronghang Hu and Xinlei Chen and Zhuang Liu and In So Kweon and Saining Xie},
+      year={2023},
+      eprint={2301.00808},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2301.00808},
+}
+```