Unsupervised, Semi-Supervised and Full-Supervised Visual Representation Learning

UniMoCo

UniMoCo: Unsupervised, Semi-Supervised and Full-Supervised Visual Representation Learning

This is the official PyTorch implementation for UniMoCo paper:

@article{dai2021unimoco,
  author  = {Zhigang Dai and Bolun Cai and Yugeng Lin and Junying Chen},
  title   = {UniMoCo: Unsupervised, Semi-Supervised and Full-Supervised Visual Representation Learning},
  journal = {arXiv preprint arXiv:2103.10773},
  year    = {2021},
}

In UniMoCo, we generalize MoCo to a unified contrastive learning framework, which supports unsupervised, semi-supervised and full-supervised visual representation learning. Based on MoCo, we maintain a label queue to store supervised labels. With the label queue, we can construct the multi-hot target on-the-fly, which represents postives and negatives of the given query. Besides, we propose a unified contrastive loss to deal with arbitrary number of positives and negatives. There is a comparison between MoCo and UniMoCo.

moco-unimoco

ImageNet Pre-training

Data Preparation

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

Pre-training

To perform supervised contrastive learning of ResNet-50 model on ImageNet with 8 gpus for 800 epochs, run:

python main_unimoco.py \
  -a resnet50 \
  --lr 0.03 \
  --batch-size 256 \
  --epochs 800 \
  --dist-url 'tcp://localhost:10001' \
  --multiprocessing-distributed --world-size 1 --rank 0 \
  --mlp \
  --moco-t 0.2 \
  --aug-plus \
  --cos \
  [your imagenet-folder with train and val folders]

By default, the script performs full-supervised contrasitve learning.

Set --supervised-list to perform semi-supervised contrastive learning with different label ratios. For exmaple, 60% labels: --supervised-list ./label_info/60percent.txt.

This script uses all the default hyper-parameters as described in the MoCo v2.