Coastline2018/ViT

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Vision Transformers

Implementation of Vision Transformer in PyTorch, a new model to achieve SOTA in vision classification with using transformer style encoders. Associated blog article.

Features

• Vanilla ViT
• Hybrid ViT (with support for BiTResNets as backbone)
• Hybrid ViT (with support for AxialResNets as backbone)
• Training Scripts

To Do:

• Training Script
  • Support for linear decay
  • Correct hyper parameters
• Full Axial-ViT
• Results for Imagenet-1K and Imagenet-21K

Installation

Create the environment:

conda env create -f environment.yml

Preparing the dataset:

mkdir data
cd data
ln -s path/to/dataset imagenet

Running the Scripts

For non-distributed training:

python train.py --model ViT --name vit_logs

For distributed training:

CUDA_VISIBLE_DEVICES=0,1,2,3 python dist_train.py --model ViT --name vit_dist_logs

For testing add the --test parameter:

python train.py --model ViT --name vit_logs --test
CUDA_VISIBLE_DEVICES=0,1,2,3 python dist_train.py --model ViT --name vit_dist_logs --test

References

1. BiTResNet: https://github.com/google-research/big_transfer/tree/master/bit_pytorch
2. AxialResNet: https://github.com/csrhddlam/axial-deeplab
3. Training Scripts: https://github.com/csrhddlam/axial-deeplab

Citations

@inproceedings{
    anonymous2021an,
    title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
    author={Anonymous},
    booktitle={Submitted to International Conference on Learning Representations},
    year={2021},
    url={https://openreview.net/forum?id=YicbFdNTTy},
    note={under review}
}