LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference

This repository contains PyTorch evaluation code, training code and pretrained models for LeViT.

They obtain competitive tradeoffs in terms of speed / precision:

For details see LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference by Benjamin Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou and Matthijs Douze.

If you use this code for a paper please cite:

@article{graham2021levit,
  title={LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference},
  author={Benjamin Graham and Alaaeldin El-Nouby and Hugo Touvron and Pierre Stock and Armand Joulin and Herv\'e J\'egou and Matthijs Douze},
  journal={arXiv preprint arXiv:22104.01136},
  year={2021}
}

Model Zoo

We provide baseline LeViT models trained with distllation on ImageNet 2012.

name	acc@1	acc@5	#FLOPs	#params	url
LeViT-128S	76.6	92.9	305M	7.8M	model
LeViT-128	78.6	94.0	406M	9.2M	model
LeViT-192	80.0	94.7	658M	11M	model
LeViT-256	81.6	95.4	1120M	19M	model
LeViT-384	82.6	96.0	2353M	39M	model

Usage

First, clone the repository locally:

git clone https://github.com/facebookresearch/levit.git

Then, install PyTorch 1.7.0+ and torchvision 0.8.1+ and pytorch-image-models:

conda install -c pytorch pytorch torchvision
pip install timm

Data preparation

Download and extract ImageNet train and val images from http://image-net.org/. The directory structure is the standard layout for the torchvision datasets.ImageFolder, and the training and validation data is expected to be in the train/ folder and val folder respectively:

/path/to/imagenet/
  train/
    class1/
      img1.jpeg
    class2/
      img2.jpeg
  val/
    class1/
      img3.jpeg
    class/2
      img4.jpeg

Evaluation

To evaluate a pre-trained LeViT-256 model on ImageNet val with a single GPU run:

python main.py --eval --model LeViT_256 --data-path /path/to/imagenet

This should give

* Acc@1 81.636 Acc@5 95.424 loss 0.750

Training

To train LeViT-256 on ImageNet with hard distillation on a single node with 8 gpus run:

python -m torch.distributed.launch --nproc_per_node=8 --use_env main.py --model LeViT_256 --data-path /path/to/imagenet --output_dir /path/to/save

Multinode training

Distributed training is available via Slurm and submitit:

pip install submitit

To train LeViT-256 model on ImageNet on one node with 8 gpus:

python run_with_submitit.py --model LeViT_256 --data-path /path/to/imagenet

License

This repository is released under the Apache 2.0 license as found in the LICENSE file.

Contributing

We actively welcome your pull requests! Please see CONTRIBUTING.md and CODE_OF_CONDUCT.md for more info.