| --- |
| tags: |
| - image-classification |
| - birder |
| - pytorch |
| library_name: birder |
| license: apache-2.0 |
| --- |
| |
| # Model Card for efficientvim_m1_il-common |
|
|
| A EfficientViM image classification model. This model was trained on the `il-common` dataset, which contains common bird species found in Israel. |
|
|
| The species list is derived from data available at <https://www.israbirding.com/checklist/>. |
|
|
| ## Model Details |
|
|
| - **Model Type:** Image classification and detection backbone |
| - **Model Stats:** |
| - Params (M): 6.1 |
| - Input image size: 256 x 256 |
| - **Dataset:** il-common (371 classes) |
|
|
| - **Papers:** |
| - EfficientViM: Efficient Vision Mamba with Hidden State Mixer based State Space Duality: <https://arxiv.org/abs/2411.15241> |
|
|
| ## Model Usage |
|
|
| ### Image Classification |
|
|
| ```python |
| import birder |
| from birder.inference.classification import infer_image |
| |
| (net, model_info) = birder.load_pretrained_model("efficientvim_m1_il-common", 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, 371), representing class probabilities. |
| ``` |
|
|
| ### Image Embeddings |
|
|
| ```python |
| import birder |
| from birder.inference.classification import infer_image |
| |
| (net, model_info) = birder.load_pretrained_model("efficientvim_m1_il-common", 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, 320) |
| ``` |
|
|
| ### Detection Feature Map |
|
|
| ```python |
| from PIL import Image |
| import birder |
| |
| (net, model_info) = birder.load_pretrained_model("efficientvim_m1_il-common", 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, 128, 16, 16])), |
| # ('stage2', torch.Size([1, 192, 8, 8])), |
| # ('stage3', torch.Size([1, 320, 4, 4]))] |
| ``` |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{lee2025efficientvimefficientvisionmamba, |
| title={EfficientViM: Efficient Vision Mamba with Hidden State Mixer based State Space Duality}, |
| author={Sanghyeok Lee and Joonmyung Choi and Hyunwoo J. Kim}, |
| year={2025}, |
| eprint={2411.15241}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV}, |
| url={https://arxiv.org/abs/2411.15241}, |
| } |
| ``` |
|
|
