SimSiam-TF

This is an unofficial implementation of SimSiam (Exploring Simple Siamese Representation Learning, CVPR 2021.).

Requirements

• python >= 3.6
• tensorflow >= 2.2

Training

To train SimSiam,

python main.py \
    --task pretext \
    --stop_gradient \
    --proj_bn_hidden \
    --proj_bn_output \
    --pred_bn_hidden \
    --weight_decay 0.0001 \
    --batch_size 256 \
    --epochs 200 \
    --lr_mode cosine \
    --data_path /path/of/your/data \
    --gpus gpu id(s) which will be used

Evaluation

To evaluate pre-trained model with linear classification,

python main.py \
    --task lincls \
    --batch_size 256 \
    --epochs 90 \
    --lr 30 \
    --lr_mode cosine \
    --data_path /path/of/your/data \
    --snapshot /path/of/checkpoint \
    --gpus gpu id(s) which will be used

Results

ImageNet

Model	batch	Accuracy (paper)	Accuracy (ours)
ResNet50 (200 epochs)	256	68.1	-

CIFAR10

Model	batch	Accuracy (paper)	Accuracy (ours)
ResNet50 (800 epochs)	256	91.1	90.7

Citation

@article{Chen2020ExploringSS,
  title={Exploring Simple Siamese Representation Learning},
  author={Xinlei Chen and Kaiming He},
  journal={ArXiv},
  year={2020},
  volume={abs/2011.10566}
}