MoCo-TF

This is an unofficial implementation of Moco v1 (Momentum Contrast for Unsupervised Visual Representation Learning, CVPR 2020.) and Moco v2 (Improved Baselines with Momentum Contrastive Learning).

Requirements

• python >= 3.6
• tensorflow >= 2.2 (2.2 and 2.3)

Training

For training moco v1,

python main.py \
    --task v1 \
    --weight_decay 0.0001 \
    --brightness 0.4 \
    --contrast 0.4 \
    --saturation 0.4 \
    --hue 0.4 \
    --lr_mode exponential \
    --lr_interval 120,160 \
    --data_path /path/of/your/data \
    --gpus gpu id(s) which will be used

or moco v2,

python main.py \
    --task v2 \
    --weight_decay 0.0001 \
    --mlp \
    --brightness 0.4 \
    --contrast 0.4 \
    --saturation 0.4 \
    --hue 0.1 \
    --lr_mode cosine \
    --data_path /path/of/your/data \
    --gpus gpu id(s) which will be used

Evaluation

For training linear classification,

python main.py \
    --task lincls \
    --batch_size 256 \
    --epochs 100 \
    --lr 30 \
    --lr_mode constant \
    --data_path /path/of/your/data \
    --snapshot /path/of/checkpoint \
    --gpus gpu id(s) which will be used

Results

Our model achieves the following performance on :

Image Classification on ImageNet (IN-1M)

MoCo v1

Model	batch	Accuracy (paper)	Accuracy (ours)
ResNet50 (200 epochs)	256	60.6	-

MoCo v2

Model	batch	Accuracy (paper)	Accuracy (ours)
ResNet50 (200 epochs)	256	67.5	-
ResNet50 (800 epochs)	256	71.1	-

Citation

@Article{he2019moco,
  author  = {Kaiming He and Haoqi Fan and Yuxin Wu and Saining Xie and Ross Girshick},
  title   = {Momentum Contrast for Unsupervised Visual Representation Learning},
  journal = {arXiv preprint arXiv:1911.05722},
  year    = {2019},
}

@Article{chen2020mocov2,
  author  = {Xinlei Chen and Haoqi Fan and Ross Girshick and Kaiming He},
  title   = {Improved Baselines with Momentum Contrastive Learning},
  journal = {arXiv preprint arXiv:2003.04297},
  year    = {2020},
}