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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 efficientvim_m1_il-common |
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A EfficientViM image classification model. This model was trained on the `il-common` dataset, which contains 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): 6.1 |
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- Input image size: 256 x 256 |
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- **Dataset:** il-common (371 classes) |
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- **Papers:** |
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- EfficientViM: Efficient Vision Mamba with Hidden State Mixer based State Space Duality: <https://arxiv.org/abs/2411.15241> |
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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("efficientvim_m1_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("efficientvim_m1_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, 320) |
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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("efficientvim_m1_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, 128, 16, 16])), |
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# ('stage2', torch.Size([1, 192, 8, 8])), |
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# ('stage3', torch.Size([1, 320, 4, 4]))] |
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``` |
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## Citation |
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```bibtex |
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@misc{lee2025efficientvimefficientvisionmamba, |
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title={EfficientViM: Efficient Vision Mamba with Hidden State Mixer based State Space Duality}, |
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author={Sanghyeok Lee and Joonmyung Choi and Hyunwoo J. Kim}, |
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year={2025}, |
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eprint={2411.15241}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2411.15241}, |
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} |
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``` |
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