Collections of self-supervised methods (MoCo series, SimCLR, SiMo, BYOL, SimSiam, SwAV, PointContrast, etc.).

Install cvpods from https://github.com/Megvii-BaseDetection/cvpods.git .

cd cvpods
ln -s /path/to/your/ImageNet datasets/imagenet

cd /path/to/your/SelfSup/examples/simclr/simclr.res50.scratch.imagenet.224size.256bs.200e
# pre-train
pods_train --num-gpus 8
# convert to weights
python convert.py simclr.res50.scratch.imagenet.224size.256bs.200e/log/model_final.pth weights.pkl
# downstream evaluation
cd /path/to/your/simclr.res50.scratch.imagenet.224size.256bs.200e.lin_cls
pods_train --num-gpus 8 MODEL.WEIGHTS /path/to/your/weights.pkl

All results in the below table are trained using resnet-50 and reported on the ILSVRC2012 dataset.

All the results reported below are trained on ILSVRC2012 and evaluated on MS COCO using Faster-RCNN-FPN and resnet-50.

SelfSup is a part of cvpods, so if you find this repo useful in your research, or if you want to refer the implementations in this repo, please consider cite:

@article{zhu2020eqco,
  title={EqCo: Equivalent Rules for Self-supervised Contrastive Learning},
  author={Zhu, Benjin and Huang, Junqiang and Li, Zeming and Zhang, Xiangyu and Sun, Jian},
  journal={arXiv preprint arXiv:2010.01929},
  year={2020}
}

@misc{zhu2020cvpods,
  title={cvpods: All-in-one Toolbox for Computer Vision Research},
  author={Zhu*, Benjin and Wang*, Feng and Wang, Jianfeng and Yang, Siwei and Chen, Jianhu and Li, Zeming},
  year={2020}
}