SimSiam: Exploring Simple Siamese Representation Learning

This is a PyTorch implementation of the SimSiam paper:

@Article{chen2020simsiam,
  author  = {Xinlei Chen and Kaiming He},
  title   = {Exploring Simple Siamese Representation Learning},
  journal = {arXiv preprint arXiv:2011.10566},
  year    = {2020},
}

Preparation

Install PyTorch and download the ImageNet dataset following the official PyTorch ImageNet training code. Similar to MoCo, the code release contains minimal modifications for both unsupervised pre-training and linear classification to that code.

In addition, install apex for the LARS implementation needed for linear classification.

Unsupervised Pre-Training

Only multi-gpu, DistributedDataParallel training is supported; 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_simsiam.py \
  -a resnet50 \
  --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 \
  --fix-pred-lr \
  [your imagenet-folder with train and val folders]

The script uses all the default hyper-parameters as described in the paper, and uses the default augmentation recipe from MoCo v2.

The above command performs pre-training with a non-decaying predictor learning rate for 100 epochs, corresponding to the last row of Table 1 in the paper.

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 \
  --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0 \
  --pretrained [your checkpoint path]/checkpoint_0099.pth.tar \
  --lars \
  [your imagenet-folder with train and val folders]

The above command uses LARS optimizer and a default batch size of 4096.

Models and Logs

Our pre-trained ResNet-50 models and logs:

Settings for the above: 8 NVIDIA V100 GPUs, CUDA 10.1/CuDNN 7.6.5, PyTorch 1.7.0.

Transferring to Object Detection

Same as MoCo for object detection transfer, please see moco/detection.

License

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