goelbiju / convit

Code for the Convolutional Vision Transformer (ConViT)

ConViT : Vision Transformers with Convolutional Inductive Biases

This repository contains PyTorch code for ConViT. It builds on code from the Data-Efficient Vision Transformer and from timm.

For details see the ConViT paper by Stéphane d'Ascoli, Hugo Touvron, Matthew Leavitt, Ari Morcos, Giulio Biroli and Levent Sagun.

If you use this code for a paper please cite:

@article{d2021convit,
  title={ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases},
  author={d'Ascoli, St{\'e}phane and Touvron, Hugo and Leavitt, Matthew and Morcos, Ari and Biroli, Giulio and Sagun, Levent},
  journal={arXiv preprint arXiv:2103.10697},
  year={2021}
}

Usage

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 ConViT-Ti on ImageNet test set, run:

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

This should give

Acc@1 73.116 Acc@5 91.710 loss 1.172

Training

To train ConViT-Ti on ImageNet on a single node with 4 gpus for 300 epochs run:

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

To train the same model on a subsampled version of ImageNet where we only use 10% of the images of each class, add --sampling_ratio 0.1

Multinode training

Distributed training is available via Slurm and submitit:

pip install submitit

To train ConViT-base on ImageNet on 2 nodes with 8 gpus each for 300 epochs:

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

License

The majority of this repository is released under the Apache 2.0 license as found in the LICENSE file.