DeiT: Data-efficient Image Transformers

This repository contains PyTorch evaluation code, training code and pretrained models for the following projects:

  • DeiT (Data-Efficient Image Transformers)
  • CaiT (Going deeper with Image Transformers)
  • ResMLP (ResMLP: Feedforward networks for image classification with data-efficient training)

They obtain competitive tradeoffs in terms of speed / precision:

DeiT

For details see Training data-efficient image transformers & distillation through attention by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles and Hervé Jégou.

If you use this code for a paper please cite:

@InProceedings{pmlr-v139-touvron21a,
  title =     {Training data-efficient image transformers & distillation through attention},
  author =    {Touvron, Hugo and Cord, Matthieu and Douze, Matthijs and Massa, Francisco and Sablayrolles, Alexandre and Jegou, Herve},
  booktitle = {International Conference on Machine Learning},
  pages =     {10347--10357},
  year =      {2021},
  volume =    {139},
  month =     {July}
}

Model Zoo

We provide baseline DeiT models pretrained on ImageNet 2012.

name [email protected] [email protected] #params url
DeiT-tiny 72.2 91.1 5M model
DeiT-small 79.9 95.0 22M model
DeiT-base 81.8 95.6 86M model
DeiT-tiny distilled 74.5 91.9 6M model
DeiT-small distilled 81.2 95.4 22M model
DeiT-base distilled 83.4 96.5 87M model
DeiT-base 384 82.9 96.2 87M model
DeiT-base distilled 384 (1000 epochs) 85.2 97.2 88M model
CaiT-S24 distilled 384 85.1 97.3 47M model
CaiT-M48 distilled 448 86.5 97.7 356M model

The models are also available via torch hub.
Before using it, make sure you have the pytorch-image-models package timm==0.3.2 by Ross Wightman installed. Note that our work relies of the augmentations proposed in this library. In particular, the RandAugment and RandErasing augmentations that we invoke are the improved versions from the timm library, which already led the timm authors to report up to 79.35% top-1 accuracy with Imagenet training for their best model, i.e., an improvement of about +1.5% compared to prior art.

To load DeiT-base with pretrained weights on ImageNet simply do:

import torch
# check you have the right version of timm
import timm
assert timm.__version__ == "0.3.2"

# now load it with torchhub
model = torch.hub.load('facebookresearch/deit:main', 'deit_base_patch16_224', pretrained=True)

Additionnally, we provide a Colab notebook which goes over the steps needed to perform inference with DeiT.

Usage

First, clone the repository locally:

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

Then, install PyTorch 1.7.0+ and torchvision 0.8.1+ and pytorch-image-models 0.3.2:

conda install -c pytorch pytorch torchvision
pip install timm==0.3.2

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 DeiT-base on ImageNet val with a single GPU run:

python main.py --eval --resume https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth --data-path /path/to/imagenet

This should give

* [email protected] 81.846 [email protected] 95.594 loss 0.820

For Deit-small, run:

python main.py --eval --resume https://dl.fbaipublicfiles.com/deit/deit_small_patch16_224-cd65a155.pth --model deit_small_patch16_224 --data-path /path/to/imagenet

giving

* [email protected] 79.854 [email protected] 94.968 loss 0.881

Note that Deit-small is not the same model as in Timm.

And for Deit-tiny:

python main.py --eval --resume https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth --model deit_tiny_patch16_224 --data-path /path/to/imagenet

which should give

* [email protected] 72.202 [email protected] 91.124 loss 1.219

Here you’ll find the command-lines to reproduce the inference results for the distilled and finetuned models

deit_base_distilled_patch16_224

python main.py --eval --model deit_base_distilled_patch16_224 --resume https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_224-df68dfff.pth

giving

* [email protected] 83.372 [email protected] 96.482 loss 0.685
deit_small_distilled_patch16_224

python main.py --eval --model deit_small_distilled_patch16_224 --resume https://dl.fbaipublicfiles.com/deit/deit_small_distilled_patch16_224-649709d9.pth

giving

* [email protected] 81.164 [email protected] 95.376 loss 0.752
deit_tiny_distilled_patch16_224

python main.py --eval --model deit_tiny_distilled_patch16_224 --resume https://dl.fbaipublicfiles.com/deit/deit_tiny_distilled_patch16_224-b40b3cf7.pth

giving

* [email protected] 74.476 [email protected] 91.920 loss 1.021
deit_base_patch16_384

python main.py --eval --model deit_base_patch16_384 --input-size 384 --resume https://dl.fbaipublicfiles.com/deit/deit_base_patch16_384-8de9b5d1.pth

giving

* [email protected] 82.890 [email protected] 96.222 loss 0.764
deit_base_distilled_patch16_384

python main.py --eval --model deit_base_distilled_patch16_384 --input-size 384 --resume https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_384-d0272ac0.pth

giving

* [email protected] 85.224 [email protected] 97.186 loss 0.636

Training

To train DeiT-small and Deit-tiny on ImageNet on a single node with 4 gpus for 300 epochs run:

DeiT-small

python -m torch.distributed.launch --nproc_per_node=4 --use_env main.py --model deit_small_patch16_224 --batch-size 256 --data-path /path/to/imagenet --output_dir /path/to/save

DeiT-tiny

python -m torch.distributed.launch --nproc_per_node=4 --use_env main.py --model deit_tiny_patch16_224 --batch-size 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 DeiT-base model on ImageNet on 2 nodes with 8 gpus each for 300 epochs:

python run_with_submitit.py --model deit_base_patch16_224 --data-path /path/to/imagenet

To train DeiT-base with hard distillation using a RegNetY-160 as teacher, on 2 nodes with 8 GPUs with 32GB each for 300 epochs (make sure that the model weights for the teacher have been downloaded before to the correct location, to avoid multiple workers writing to the same file):

python run_with_submitit.py --model deit_base_distilled_patch16_224 --distillation-type hard --teacher-model regnety_160 --teacher-path https://dl.fbaipublicfiles.com/deit/regnety_160-a5fe301d.pth --use_volta32

To finetune a DeiT-base on 384 resolution images for 30 epochs, starting from a DeiT-base trained on 224 resolution images, do (make sure that the weights to the original model have been downloaded before, to avoid multiple workers writing to the same file):

python run_with_submitit.py --model deit_base_patch16_384 --batch-size 32 --finetune https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth --input-size 384 --use_volta32 --nodes 2 --lr 5e-6 --weight-decay 1e-8 --epochs 30 --min-lr 5e-6

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.

GitHub

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