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--- |
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tags: |
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- image-classification |
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- birder |
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library_name: birder |
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license: apache-2.0 |
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--- |
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# Model Card for regnet_y_8g_intermediate-eu-common |
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RegNet Y image classification model. The model follows a two-stage training process: first undergoing intermediate training on a large-scale dataset containing diverse bird species from around the world, then fine-tuned specifically on the `eu-common` dataset containing common European bird species. |
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The species list is derived from the Collins bird guide [^1]. |
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[^1]: Svensson, L., Mullarney, K., & Zetterström, D. (2022). Collins bird guide (3rd ed.). London, England: William Collins. |
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## Model Details |
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- **Model Type:** Image classification and detection backbone |
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- **Model Stats:** |
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- Params (M): 38.8 |
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- Input image size: 384 x 384 |
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- **Dataset:** eu-common (707 classes) |
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- **Papers:** |
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- Designing Network Design Spaces: <https://arxiv.org/abs/2003.13678> |
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## Model Usage |
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### Image Classification |
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```python |
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import birder |
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from birder.inference.classification import infer_image |
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(net, class_to_idx, signature, rgb_stats) = birder.load_pretrained_model("regnet_y_8g_intermediate-eu-common", inference=True) |
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# Get the image size the model was trained on |
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size = birder.get_size_from_signature(signature) |
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# Create an inference transform |
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transform = birder.classification_transform(size, rgb_stats) |
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image = "path/to/image.jpeg" # or a PIL image |
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(out, _) = infer_image(net, image, transform) |
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# out is a NumPy array with shape of (1, num_classes) |
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``` |
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### Image Embeddings |
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```python |
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import birder |
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from birder.inference.classification import infer_image |
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(net, class_to_idx, signature, rgb_stats) = birder.load_pretrained_model("regnet_y_8g_intermediate-eu-common", inference=True) |
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# Get the image size the model was trained on |
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size = birder.get_size_from_signature(signature) |
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# Create an inference transform |
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transform = birder.classification_transform(size, rgb_stats) |
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image = "path/to/image.jpeg" # or a PIL image |
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(out, embedding) = infer_image(net, image, transform, return_embedding=True) |
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# embedding is a NumPy array with shape of (1, embedding_size) |
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``` |
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### Detection Feature Map |
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```python |
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from PIL import Image |
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import birder |
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(net, class_to_idx, signature, rgb_stats) = birder.load_pretrained_model("regnet_y_8g_intermediate-eu-common", inference=True) |
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# Get the image size the model was trained on |
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size = birder.get_size_from_signature(signature) |
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# Create an inference transform |
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transform = birder.classification_transform(size, rgb_stats) |
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image = Image.open("path/to/image.jpeg") |
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features = net.detection_features(transform(image).unsqueeze(0)) |
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# features is a dict (stage name -> torch.Tensor) |
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print([(k, v.size()) for k, v in features.items()]) |
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# Output example: |
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# [('stage1', torch.Size([1, 96, 96, 96])), |
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# ('stage2', torch.Size([1, 192, 48, 48])), |
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# ('stage3', torch.Size([1, 384, 24, 24])), |
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# ('stage4', torch.Size([1, 768, 12, 12]))] |
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``` |
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## Citation |
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```bibtex |
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@misc{radosavovic2020designingnetworkdesignspaces, |
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title={Designing Network Design Spaces}, |
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author={Ilija Radosavovic and Raj Prateek Kosaraju and Ross Girshick and Kaiming He and Piotr Dollár}, |
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year={2020}, |
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eprint={2003.13678}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2003.13678}, |
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} |
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``` |
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