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--- |
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tags: |
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- image-classification |
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- birder |
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- pytorch |
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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 convnext_v2_tiny_intermediate-il-common |
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A ConvNext v2 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 `il-common` dataset containing common bird species found in Israel. |
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The species list is derived from data available at <https://www.israbirding.com/checklist/>. |
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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): 28.2 |
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- Input image size: 256 x 256 |
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- **Dataset:** il-common (371 classes) |
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- Intermediate training involved ~4000 species from asia, europe and eastern africa |
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- **Papers:** |
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- ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders: <https://arxiv.org/abs/2301.00808> |
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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, model_info) = birder.load_pretrained_model("convnext_v2_tiny_intermediate-il-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(model_info.signature) |
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# Create an inference transform |
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transform = birder.classification_transform(size, model_info.rgb_stats) |
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image = "path/to/image.jpeg" # or a PIL image, must be loaded in RGB format |
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(out, _) = infer_image(net, image, transform) |
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# out is a NumPy array with shape of (1, 371), representing class probabilities. |
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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, model_info) = birder.load_pretrained_model("convnext_v2_tiny_intermediate-il-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(model_info.signature) |
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# Create an inference transform |
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transform = birder.classification_transform(size, model_info.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, 768) |
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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, model_info) = birder.load_pretrained_model("convnext_v2_tiny_intermediate-il-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(model_info.signature) |
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# Create an inference transform |
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transform = birder.classification_transform(size, model_info.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, 64, 64])), |
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# ('stage2', torch.Size([1, 192, 32, 32])), |
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# ('stage3', torch.Size([1, 384, 16, 16])), |
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# ('stage4', torch.Size([1, 768, 8, 8]))] |
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``` |
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## Citation |
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```bibtex |
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@misc{woo2023convnextv2codesigningscaling, |
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title={ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders}, |
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author={Sanghyun Woo and Shoubhik Debnath and Ronghang Hu and Xinlei Chen and Zhuang Liu and In So Kweon and Saining Xie}, |
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year={2023}, |
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eprint={2301.00808}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2301.00808}, |
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} |
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``` |
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