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Cross-Attention Multi-Scale Vision Transformer for Image Classification


This repository is the official implementation of CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification. ArXiv

If you use the codes and models from this repo, please cite our work. Thanks!

@inproceedings{
    chen2021crossvit,
    title={{CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification}},
    author={Chun-Fu (Richard) Chen and Quanfu Fan and Rameswar Panda},
    booktitle={International Conference on Computer Vision (ICCV)},
    year={2021}
}

Installation

To install requirements:

pip install -r requirements.txt

With conda:

conda create -n crossvit python=3.8
conda activate crossvit
conda install pytorch=1.7.1 torchvision  cudatoolkit=11.0 -c pytorch -c nvidia
pip install -r requirements.txt

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

Pretrained models

We provide models trained on ImageNet1K. You can find models here. And you can load pretrained weights into models by add --pretrained flag.

Training

To train crossvit_9_dagger_224 on ImageNet on a single node with 8 gpus for 300 epochs run:

python -m torch.distributed.launch --nproc_per_node=8 --use_env main.py --model crossvit_9_dagger_224 --batch-size 256 --data-path /path/to/imagenet

Other model names can be found at models/crossvit.py.

Multinode training

Distributed training is available via Slurm and submitit:

To train a crossvit_9_dagger_224 model on ImageNet on 4 nodes with 8 gpus each for 300 epochs:

python run_with_submitit.py --nodes 4 --model crossvit_9_dagger_224 --data-path /path/to/imagenet --batch-size 128 --warmup-epochs 30

Or you can start process on each machine maunally. E.g. 2 nodes, each with 8 gpus.

Machine 0:

python -m torch.distributed.launch --nproc_per_node=8 --master_addr=MACHINE_0_IP --master_port=AVAILABLE_PORT --nnodes=2 --node_rank=0 main.py --model crossvit_9_dagger_224 --batch-size 256 --data-path /path/to/imagenet

Machine 1:

python -m torch.distributed.launch --nproc_per_node=8 --master_addr=MACHINE_0_IP --master_port=AVAILABLE_PORT --nnodes=2 --node_rank=1 main.py --model crossvit_9_dagger_224 --batch-size 256 --data-path /path/to/imagenet

Note that: some slurm configurations might need to be changed based on your cluster.

Evaluation

To evaluate a pretrained model on crossvit_9_dagger_224:

python -m torch.distributed.launch --nproc_per_node=8 --use_env main.py --model crossvit_9_dagger_224 --batch-size 128 --data-path /path/to/imagenet --eval --pretrained

GitHub

https://github.com/IBM/CrossViT


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