Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency

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Deep LearningCycleContrast
Overview

Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency

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

Citation

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}
}

Preparation

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

pip install -r requirements.txt

Model Zoo

We provide the model pretrained on R2V2 for 200 epochs.

method pre-train epochs on R2V2 dataset ImageNet Top-1 Linear Eval OTB Precision OTB Success UCF Top-1 pretrained model
MoCo 200 53.8 56.1 40.6 80.5 pretrain ckpt
CycleContrast 200 55.7 69.6 50.4 82.8 pretrain ckpt

Run Experiments

Data preparation

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/

Unsupervised Pretrain

./scripts/train_cycle.sh

Downstream task - ImageNet linear eval

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

Downstream task - OTB tracking

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}

Downstream task - UCF classification

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}

Acknowledgements

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!

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