Awesome

Vision Transformer with Progressive Sampling

This is the official implementation of the paper Vision Transformer with Progressive Sampling, ICCV 2021.

Visual Parser

Installation Instructions

Clone this repo:

git clone git@github.com:yuexy/PS-ViT.git
cd PS-ViT

Create a conda virtual environment and activate it:

conda create -n ps_vit python=3.7 -y
conda activate ps_vit

Install CUDA==10.1 with cudnn7 following the official installation instructions
Install PyTorch==1.7.1 and torchvision==0.8.2 with CUDA==10.1:

conda install pytorch==1.7.1 torchvision==0.8.2 cudatoolkit=10.1 -c pytorch

Install timm==0.3.4, einops, pyyaml:

pip3 install timm=0.3.4, einops, pyyaml

Install Apex:

git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

Install PS-ViT:

python setup.py build_ext --inplace

Results and Models

All models listed below are evaluated with input size 224x224

Model	Top1 Acc	#params	FLOPS	Download
PS-ViT-Ti/14	75.6	4.8M	1.6G	Coming Soon
PS-ViT-B/10	80.6	21.3M	3.1G	Coming Soon
PS-ViT-B/14	81.7	21.3M	5.4G	Google Drive
PS-ViT-B/18	82.3	21.3M	8.8G	Google Drive

Evaluation

To evaluate a pre-trained PS-ViT on ImageNet val, run:

python3 main.py <data-root> --model <model-name> -b <batch-size> --eval_checkpoint <path-to-checkpoint>

Training from scratch

To train a PS-ViT on ImageNet from scratch, run:

bash ./scripts/train_distributed.sh <job-name> <config-path> <num-gpus>

Citing PS-ViT

@article{psvit,
  title={Vision Transformer with Progressive Sampling},
  author={Yue, Xiaoyu and Sun, Shuyang and Kuang, Zhanghui and Wei, Meng and Torr, Philip and Zhang, Wayne and Lin, Dahua},
  journal={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2021}
}

Contact

If you have any questions, don't hesitate to contact Xiaoyu Yue. You can easily reach him by sending an email to yuexiaoyu002@gmail.com.