readme: ---
  tags:
  - image-classification
  - timm
  library_tag: timm
  license: apache-2.0
  datasets:
  - imagenet-1k
  ---
  # Model card for levit_192.fb_dist_in1k
  
  A LeViT image classification model using convolutional mode (using nn.Conv2d and nn.BatchNorm2d). Pretrained on ImageNet-1k using distillation by paper authors.
  
  
  ## Model Details
  - **Model Type:** Image classification / feature backbone
  - **Model Stats:**
    - Params (M): 10.9
    - GMACs: 0.7
    - Activations (M): 3.2
    - Image size: 224 x 224
  - **Papers:**
    - LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference: https://arxiv.org/abs/2104.01136
  - **Original:** https://github.com/facebookresearch/LeViT
  - **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('levit_192.fb_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(
      'levit_192.fb_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|
  |-----------------------------------|------|------|-----------|--------|
  |levit_384.fb_dist_in1k             |82.596|96.012|39.13      |224     |
  |levit_conv_384.fb_dist_in1k        |82.596|96.012|39.13      |224     |
  |levit_256.fb_dist_in1k             |81.512|95.48 |18.89      |224     |
  |levit_conv_256.fb_dist_in1k        |81.512|95.48 |18.89      |224     |
  |levit_conv_192.fb_dist_in1k        |79.86 |94.792|10.95      |224     |
  |levit_192.fb_dist_in1k             |79.858|94.792|10.95      |224     |
  |levit_128.fb_dist_in1k             |78.474|94.014|9.21       |224     |
  |levit_conv_128.fb_dist_in1k        |78.474|94.02 |9.21       |224     |
  |levit_128s.fb_dist_in1k            |76.534|92.864|7.78       |224     |
  |levit_conv_128s.fb_dist_in1k       |76.532|92.864|7.78       |224     |
  
  ## Citation
  ```bibtex
  @InProceedings{Graham_2021_ICCV,
    author    = {Graham, Benjamin and El-Nouby, Alaaeldin and Touvron, Hugo and Stock, Pierre and Joulin, Armand and Jegou, Herve and Douze, Matthijs},
    title     = {LeViT: A Vision Transformer in ConvNet's Clothing for Faster Inference},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {12259-12269}
  }
  ```
  ```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}}
  }
  ```
  