readme: ---
  tags:
  - image-classification
  - timm
  library_tag: timm
  license: apache-2.0
  datasets:
  - imagenet-1k
  ---
  # Model card for efficientformer_l1.snap_dist_in1k
  
  A EfficientFormer image classification model. Pretrained with distillation on ImageNet-1k.
  
  ## Model Details
  - **Model Type:** Image classification / feature backbone
  - **Model Stats:**
    - Params (M): 12.3
    - GMACs: 1.3
    - Activations (M): 5.5
    - Image size: 224 x 224
  - **Original:** https://github.com/snap-research/EfficientFormer
  - **Papers:**
    - EfficientFormer: Vision Transformers at MobileNet Speed: https://arxiv.org/abs/2206.01191
  - **Dataset:** ImageNet-1k
  
  ## Model Usage
  ### Image Classification
  ```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('efficientformer_l1.snap_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)
  ```
  
  ### 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(
      'efficientformer_l1.snap_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 unpooled (ie.e a (batch_size, num_features, H, W) tensor
  
  output = model.forward_head(output, pre_logits=True)
  # output is (batch_size, num_features) tensor
  ```
  
  ## Model Comparison
  |model                              |top1  |top5  |param_count|img_size|
  |-----------------------------------|------|------|-----------|--------|
  |efficientformerv2_l.snap_dist_in1k |83.628|96.54 |26.32      |224     |
  |efficientformer_l7.snap_dist_in1k  |83.368|96.534|82.23      |224     |
  |efficientformer_l3.snap_dist_in1k  |82.572|96.24 |31.41      |224     |
  |efficientformerv2_s2.snap_dist_in1k|82.128|95.902|12.71      |224     |
  |efficientformer_l1.snap_dist_in1k  |80.496|94.984|12.29      |224     |
  |efficientformerv2_s1.snap_dist_in1k|79.698|94.698|6.19       |224     |
  |efficientformerv2_s0.snap_dist_in1k|76.026|92.77 |3.6        |224     |
  
  ## Citation
  ```bibtex
  @article{li2022efficientformer,
    title={EfficientFormer: Vision Transformers at MobileNet Speed},
    author={Li, Yanyu and Yuan, Geng and Wen, Yang and Hu, Ju and Evangelidis, Georgios and Tulyakov, Sergey and Wang, Yanzhi and Ren, Jian},
    journal={arXiv preprint arXiv:2206.01191},
    year={2022}
  }
  ```
  ```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/rwightman/pytorch-image-models}}
  }
  ```
  