| --- |
| tags: |
| - image-classification |
| - timm |
| - transformers |
| pipeline_tag: image-classification |
| library_name: timm |
| license: mit |
| datasets: |
| - imagenet-1k |
| --- |
| # Model card for efficientvim_m2_dist.in1k |
|
|
| An EfficientViM image classification model. Trained on ImageNet-1k with distillation by paper authors. |
|
|
| ## Model Details |
| - **Model Type:** Image Classification / Feature Encoder |
| - **Model Stats:** |
| - Params (M): 15.3 |
| - GMACs: 0.4 |
| - Activations (M): 1.9 |
| - Image size: 224 x 224 |
| - **Dataset:** ImageNet-1k |
| - **Papers:** |
| - EfficientViM: Efficient Vision Mamba with Hidden State Mixer based State Space Duality: https://arxiv.org/abs/2411.15241 |
| - **Original:** https://github.com/mlvlab/EfficientViM |
|
|
| ## Model Usage |
| ### Image Classification |
| ```python |
| from urllib.request import urlopen |
| from PIL import Image |
| import torch |
| import timm |
| |
| img = Image.open(urlopen( |
| 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' |
| )) |
| |
| model = timm.create_model('efficientvim_m2_dist.in1k', pretrained=True) |
| model = model.eval() |
| |
| # get model specific transforms (normalization, resize) |
| data_config = timm.data.resolve_model_data_config(model) |
| transforms = timm.data.create_transform(**data_config, is_training=False) |
| |
| output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 |
| |
| top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) |
| ``` |
|
|
| ### Feature Map Extraction |
| ```python |
| from urllib.request import urlopen |
| from PIL import Image |
| import timm |
| |
| img = Image.open(urlopen( |
| 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' |
| )) |
| |
| model = timm.create_model( |
| 'efficientvim_m2_dist.in1k', |
| pretrained=True, |
| features_only=True, |
| ) |
| model = model.eval() |
| |
| # get model specific transforms (normalization, resize) |
| data_config = timm.data.resolve_model_data_config(model) |
| transforms = timm.data.create_transform(**data_config, is_training=False) |
| |
| output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 |
| |
| for o in output: |
| # print shape of each feature map in output |
| # e.g.: |
| # torch.Size([1, 128, 14, 14]) |
| # torch.Size([1, 256, 7, 7]) |
| # torch.Size([1, 512, 4, 4]) |
| |
| print(o.shape) |
| ``` |
|
|
| ### Image Embeddings |
| ```python |
| from urllib.request import urlopen |
| from PIL import Image |
| import timm |
| |
| img = Image.open(urlopen( |
| 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' |
| )) |
| |
| model = timm.create_model( |
| 'efficientvim_m2_dist.in1k', |
| pretrained=True, |
| num_classes=0, # remove classifier nn.Linear |
| ) |
| model = model.eval() |
| |
| # get model specific transforms (normalization, resize) |
| data_config = timm.data.resolve_model_data_config(model) |
| transforms = timm.data.create_transform(**data_config, is_training=False) |
| |
| output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor |
| |
| # or equivalently (without needing to set num_classes=0) |
| |
| output = model.forward_features(transforms(img).unsqueeze(0)) |
| # output is a list of unpooled tensors with shapes [(1, 128, 49), (1, 256, 25), (1, 512, 9), (1, 512, 16)] |
| |
| output = model.forward_head(output, pre_logits=True) |
| # output is a (1, num_features) shaped tensor |
| ``` |
|
|
| ## Model Comparison |
| Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). |
|
|
| ## Citation |
| ```bibtex |
| @inproceedings{lee2025efficientvim, |
| title={EfficientViM: Efficient Vision Mamba with Hidden State Mixer based State Space Duality}, |
| author={Lee, Sanghyeok and Choi, Joonmyung and Kim, Hyunwoo J.}, |
| booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, |
| year={2025} |
| } |
| ``` |
| ```bibtex |
| @misc{rw2019timm, |
| author = {Ross Wightman}, |
| title = {PyTorch Image Models}, |
| year = {2019}, |
| publisher = {GitHub}, |
| journal = {GitHub repository}, |
| doi = {10.5281/zenodo.4414861}, |
| howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} |
| } |
| ``` |
|
|
