This is a official implementation of the CycleContrast introduced in the paper:Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency

If you find our work useful, please cite:

@article{wu2021contrastive,
  title={Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency},
  author={Wu, Haiping and Wang, Xiaolong},
  journal={arXiv preprint arXiv:2105.06463},
  year={2021}
}

Our code is tested on Python 3.7 and Pytorch 1.3.0, please install the environment via

pip install -r requirements.txt

We provide the model pretrained on R2V2 for 200 epochs.

Download R2V2 (Random Related Video Views) dataset according to https://github.com/danielgordon10/vince.

The direction structure should be as followed:

CycleContrast
├── cycle_contrast 
├── scripts 
├── utils 
├── data
│   ├── r2v2_large_with_ids 
│   │   ├── train 
│   │   │   ├── --/
│   │   │   ├── -_/
│   │   │   ├── _-/
│   │   │   ├── __/
│   │   │   ├── -0/
│   │   │   ├── _0/
│   │   │   ├── ...
│   │   │   ├── zZ/
│   │   │   ├── zz/
│   │   ├── val
│   │   │   ├── --/
│   │   │   ├── -_/
│   │   │   ├── _-/
│   │   │   ├── __/
│   │   │   ├── -0/
│   │   │   ├── _0/
│   │   │   ├── ...
│   │   │   ├── zZ/
│   │   │   ├── zz/

./scripts/train_cycle.sh

Prepare ImageNet dataset according to pytorch ImageNet training code.

MODEL_DIR=output/cycle_res50_r2v2_ep200
IMAGENET_DATA=data/ILSVRC/Data/CLS-LOC
./scripts/eval_ImageNet.sh $MODEL_DIR $IMAGENET_DATA

Transfer to OTB tracking evaluation is based on SiamFC-Pytorch. Please prepare environment and data according to SiamFC-Pytorch

git clone https://github.com/happywu/mmaction2-CycleContrast
# path to your pretrained model, change accordingly
CycleContrast=/home/user/code/CycleContrast
PRETRAIN=${CycleContrast}/output/cycle_res50_r2v2_ep200/checkpoint_0199.pth.tar
cd mmaction2_tracking
./scripts/submit_r2v2_r50_cycle.py ${PRETRAIN}

Transfer to UCF action recognition evaluation is based on AVID-CMA, prepare data and env according to AVID-CMA.

git clone https://github.com/happywu/AVID-CMA-CycleContrast
# path to your pretrained model, change accordingly
CycleContrast=/home/user/code/CycleContrast
PRETRAIN=${CycleContrast}/output/cycle_res50_r2v2_ep200/checkpoint_0199.pth.tar
cd AVID-CMA-CycleContrast 
./scripts/submit_r2v2_r50_cycle.py ${PRETRAIN}

The codebase is based on FAIR-MoCo. The OTB tracking evaluation is based on MMAction2, SiamFC-PyTorch and vince. The UCF classification evaluation follows AVID-CMA.

Thank you all for the great open source repositories!