Awesome

Current applications

Classification: Hierarchical models has been released; Plain ones will be released soon.

Object Detection: Will be released soon;

Semantic Segmentation: Will be released soon;

Human Pose: Will be released soon

News

24/01/2024

• The code of hierarchical models on classification has been released.

30/12/2023

• The paper is accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) with IF 24.314.

27/03/2023

• The paper is post on arxiv! The code will be made public available once cleaned up.

Abstract

Method

Usage

Requirements

• PyTorch==1.7.1
• torchvision==0.8.2
• timm==0.3.2

The Apex is optional for faster training speed.

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" ./

Other Requirements

pip install opencv-python==4.4.0.46 termcolor==1.1.0 yacs==0.1.8 timm==0.4.9
pip install einops

Train & Eval

For classification on ImageNet-1K, to train from scratch, run:

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
python -m torch.distributed.launch \
  --nnodes ${NNODES} \
  --node_rank ${SLURM_NODEID} \
  --master_addr ${MHOST} \
  --master_port 25901 \
  --nproc_per_node 8 \
  ./main.py \
  --cfg configs/swin/qformer_tiny_patch4_window7_224.yaml \
  --data-path ${IMAGE_PATH} \
  --batch-size 128 \
  --tag 1024-dpr20-coords_lambda1e-1 \
  --distributed \
  --coords_lambda 1e-1 \
  --drop_path_rate 0.2 \

For single GPU training, run

python ./main.py \
  --cfg configs/swin/qformer_tiny_patch4_window7_224.yaml \
  --data-path ${IMAGE_PATH} \
  --batch-size 128 \
  --tag 1024-dpr20-coords_lambda1e-1 \
  --coords_lambda 1e-1 \
  --drop_path_rate 0.2 \

To evaluate, run:

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
python -m torch.distributed.launch \
  --nnodes ${NNODES} \
  --node_rank ${SLURM_NODEID} \
  --master_addr ${MHOST} \
  --master_port 25901 \
  --nproc_per_node 8 \
  ./main.py \
  --cfg configs/swin/qformer_tiny_patch4_window7_224.yaml \
  --data-path ${IMAGE_PATH} \
  --batch-size 128 \
  --tag eval \
  --distributed \
  --resume ${MODEL PATH} \
  --eval

For single GPU evaluation, run

python ./main.py \
  --cfg configs/swin/qformer_tiny_patch4_window7_224.yaml \
  --data-path ${IMAGE_PATH} \
  --batch-size 128 \
  --tag eval \
  --resume ${MODEL PATH} \
  --eval

Results

Results on plain models

Classification results on ImageNet-1K with MAE pretrained models

Detection results on COCO with MAE pretrained models and the Mask RCNN detector, following <a href="https://arxiv.org/abs/2203.16527">ViTDet</a>

Semantic segmentation results on ADE20k with MAE pretrained models and the UPerNet segmentor

Human pose estimation results on COCO with MAE pretrained models, following <a href="https://arxiv.org/abs/2204.12484">ViTPose</a>

Results on hierarchical models

Main Results on ImageNet-1K

Object Detection Results

Mask R-CNN

Cascade Mask R-CNN

Semantic Segmentation Results for ADE20k

UperNet

Statement

This project is for research purpose only. For any other questions please contact qmzhangzz at hotmail.com.

The code base is borrowed from Swin.

Citing QFormer, VSA and ViTAE

@article{zhang2023vision,
  title={Vision Transformer with Quadrangle Attention},
  author={Zhang, Qiming and Zhang, Jing and Xu, Yufei and Tao, Dacheng},
  journal={arXiv preprint arXiv:2303.15105},
  year={2023}
}
@inproceedings{zhang2022vsa,
  title={VSA: learning varied-size window attention in vision transformers},
  author={Zhang, Qiming and Xu, Yufei and Zhang, Jing and Tao, Dacheng},
  booktitle={Computer Vision--ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23--27, 2022, Proceedings, Part XXV},
  pages={466--483},
  year={2022},
  organization={Springer}
}
@article{zhang2023vitaev2,
  title={Vitaev2: Vision transformer advanced by exploring inductive bias for image recognition and beyond},
  author={Zhang, Qiming and Xu, Yufei and Zhang, Jing and Tao, Dacheng},
  journal={International Journal of Computer Vision},
  pages={1--22},
  year={2023},
  publisher={Springer}
}
@article{xu2021vitae,
  title={Vitae: Vision transformer advanced by exploring intrinsic inductive bias},
  author={Xu, Yufei and Zhang, Qiming and Zhang, Jing and Tao, Dacheng},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
}

Our other Transformer works

ViTPose: Please see <a href="https://github.com/ViTAE-Transformer/ViTPose">Baseline model ViTPose for human pose estimation</a>;

VSA: Please see <a href="https://github.com/ViTAE-Transformer/ViTAE-VSA">ViTAE-Transformer for Image Classification and Object Detection</a>;

ViTAE & ViTAEv2: Please see <a href="https://github.com/ViTAE-Transformer/ViTAE-Transformer">ViTAE-Transformer for Image Classification, Object Detection, and Sementic Segmentation</a>;

Matting: Please see <a href="https://github.com/ViTAE-Transformer/ViTAE-Transformer-Matting">ViTAE-Transformer for matting</a>;

Remote Sensing: Please see <a href="https://github.com/ViTAE-Transformer/ViTAE-Transformer-Remote-Sensing">ViTAE-Transformer for Remote Sensing</a>; <a href="https://github.com/ViTAE-Transformer/Remote-Sensing-RVSA">Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model </a>;