• MoCo: Momentum Contrast for Unsupervised Visual Representation Learning
• • Preparation
  • Unsupervised Training
  • Linear Classification
  • Models
  • Transferring to Object Detection
  • License
  • See Also

MoCo: Momentum Contrast for Unsupervised Visual Representation Learning

This is a PyTorch implementation of the MoCo paper:

@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},
}

It also includes the implementation of the MoCo v2 paper:

@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},
}

Preparation

Install PyTorch and ImageNet dataset following the official PyTorch ImageNet training code.

This repo aims to be minimal modifications on that code. Check the modifications by:

diff main_moco.py <(curl https://raw.githubusercontent.com/pytorch/examples/master/imagenet/main.py)
diff main_lincls.py <(curl https://raw.githubusercontent.com/pytorch/examples/master/imagenet/main.py)

Unsupervised Training

This implementation only supports multi-gpu, DistributedDataParallel training, which is faster and simpler; single-gpu or DataParallel training is not supported.

To do unsupervised pre-training of a ResNet-50 model on ImageNet in an 8-gpu machine, run:

python main_moco.py \
  -a resnet50 \
  --lr 0.03 \
  --batch-size 256 \
  --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 \
  [your imagenet-folder with train and val folders]

This script uses all the default hyper-parameters as described in the MoCo v1 paper. To run MoCo v2, set --mlp --moco-t 0.2 --aug-plus --cos.

Note: for 4-gpu training, we recommend following the linear lr scaling recipe: --lr 0.015 --batch-size 128 with 4 gpus. We got similar results using this setting.

Linear Classification

With a pre-trained model, to train a supervised linear classifier on frozen features/weights in an 8-gpu machine, run:

python main_lincls.py \
  -a resnet50 \
  --lr 30.0 \
  --batch-size 256 \
  --pretrained [your checkpoint path]/checkpoint_0199.pth.tar \
  --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 \
  [your imagenet-folder with train and val folders]

Linear classification results on ImageNet using this repo with 8 NVIDIA V100 GPUs :

	pre-train epochs	pre-train time	MoCo v1 top-1 acc.	MoCo v2 top-1 acc.
ResNet-50	200	53 hours	60.8±0.2	67.5±0.1

Here we run 5 trials (of pre-training and linear classification) and report mean±std: the 5 results of MoCo v1 are {60.6, 60.6, 60.7, 60.9, 61.1}, and of MoCo v2 are {67.7, 67.6, 67.4, 67.6, 67.3}.

Models

Our pre-trained ResNet-50 models can be downloaded as following:

	epochs	mlp	aug+	cos	top-1 acc.	model	md5
MoCo v1	200				60.6	download	b251726a
MoCo v2	200	✓	✓	✓	67.7	download	59fd9945
MoCo v2	800	✓	✓	✓	71.1	download	a04e12f8

Transferring to Object Detection

See ./detection.

License

This project is under the CC-BY-NC 4.0 license. See LICENSE for details.

See Also

• moco.tensorflow: A TensorFlow re-implementation.
• Colab notebook: CIFAR demo on Colab GPU.