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---
tags:
- image-classification
- birder
- pytorch
library_name: birder
license: apache-2.0
---
# Model Card for hornet_tiny_7x7_danube-delta
A HorNet image classification model. This model was trained on the `danube-delta` dataset (all the relevant bird species found int the Danube Delta region).
The species list is derived from data available at <https://www.discoverdanubedelta.com/wp-content/uploads/2023/01/BirdsList-ian-2023.pdf>.
Note: this is a subset of the `eu-common` dataset.
## Model Details
- **Model Type:** Image classification and detection backbone
- **Model Stats:**
- Params (M): 22.1
- Input image size: 256 x 256
- **Dataset:** danube-delta (368 classes)
- **Papers:**
- HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions: <https://arxiv.org/abs/2207.14284>
## Model Usage
### Image Classification
```python
import birder
from birder.inference.classification import infer_image
(net, model_info) = birder.load_pretrained_model("hornet_tiny_7x7_danube-delta", 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.classification_transform(size, model_info.rgb_stats)
image = "path/to/image.jpeg" # or a PIL image, must be loaded in RGB format
(out, _) = infer_image(net, image, transform)
# out is a NumPy array with shape of (1, 368), representing class probabilities.
```
### Image Embeddings
```python
import birder
from birder.inference.classification import infer_image
(net, model_info) = birder.load_pretrained_model("hornet_tiny_7x7_danube-delta", 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.classification_transform(size, model_info.rgb_stats)
image = "path/to/image.jpeg" # or a PIL image
(out, embedding) = infer_image(net, image, transform, return_embedding=True)
# embedding is a NumPy array with shape of (1, 512)
```
### Detection Feature Map
```python
from PIL import Image
import birder
(net, model_info) = birder.load_pretrained_model("hornet_tiny_7x7_danube-delta", 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.classification_transform(size, model_info.rgb_stats)
image = Image.open("path/to/image.jpeg")
features = net.detection_features(transform(image).unsqueeze(0))
# features is a dict (stage name -> torch.Tensor)
print([(k, v.size()) for k, v in features.items()])
# Output example:
# [('stage1', torch.Size([1, 64, 64, 64])),
# ('stage2', torch.Size([1, 128, 32, 32])),
# ('stage3', torch.Size([1, 256, 16, 16])),
# ('stage4', torch.Size([1, 512, 8, 8]))]
```
## Citation
```bibtex
@misc{rao2022hornetefficienthighorderspatial,
title={HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions},
author={Yongming Rao and Wenliang Zhao and Yansong Tang and Jie Zhou and Ser-Nam Lim and Jiwen Lu},
year={2022},
eprint={2207.14284},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2207.14284},
}
```
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