This is an unofficial implementation of SimSiam (Exploring Simple Siamese Representation Learning, CVPR 2021.).
- python >= 3.6
- tensorflow >= 2.2
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
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
| Model | batch | Accuracy (paper) | Accuracy (ours) |
|---|---|---|---|
| ResNet50 (200 epochs) | 256 | 68.1 | - |
| Model | batch | Accuracy (paper) | Accuracy (ours) |
|---|---|---|---|
| ResNet50 (800 epochs) | 256 | 91.1 | 90.7 |
@article{Chen2020ExploringSS,
title={Exploring Simple Siamese Representation Learning},
author={Xinlei Chen and Kaiming He},
journal={ArXiv},
year={2020},
volume={abs/2011.10566}
}