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https://github.com/PatrickHua/SimSiam
A pytorch implementation for paper 'Exploring Simple Siamese Representation Learning'
https://github.com/PatrickHua/SimSiam
Last synced: 3 months ago
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A pytorch implementation for paper 'Exploring Simple Siamese Representation Learning'
- Host: GitHub
- URL: https://github.com/PatrickHua/SimSiam
- Owner: PatrickHua
- License: mit
- Created: 2020-11-27T13:23:22.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2022-07-14T05:55:25.000Z (over 2 years ago)
- Last Synced: 2024-07-05T20:00:47.079Z (4 months ago)
- Language: Python
- Size: 235 KB
- Stars: 807
- Watchers: 9
- Forks: 137
- Open Issues: 30
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# News
It's been two months and I think I've finally discovered the **True** reasons why Simsiam/BYOL avoids collapsed solutions using stop gradient and predictor!!! Follow me on [twitter](https://twitter.com/tianyu_hua) and stay tuned!
# SimSiam
A PyTorch implementation for the paper [**Exploring Simple Siamese Representation Learning**](https://arxiv.org/abs/2011.10566) by Xinlei Chen & Kaiming He
### Dependencies
If you don't have python 3 environment:
```
conda create -n simsiam python=3.8
conda activate simsiam
```
Then install the required packages:
```
pip install -r requirements.txt
```
### Run SimSiam
```
CUDA_VISIBLE_DEVICES=0 python main.py --data_dir ../Data/ --log_dir ../logs/ -c configs/simsiam_cifar.yaml --ckpt_dir ~/.cache/ --hide_progress
```
The data folder `../Data/` should look like this:
```
➜ ~ tree ../Data/
├── cifar-10-batches-py
│ ├── batches.meta
│ ├── data_batch_1
│ ├── ...
└── stl10_binary
├── ...
```
```
Training: 100%|#################################################################| 800/800 [11:46:06<00:00, 52.96s/it, epoch=799, accuracy=90.3]
Model saved to /root/.cache/simsiam-cifar10-experiment-resnet18_cifar_variant1.pth
Evaluating: 100%|##########################################################################################################| 100/100 [08:29<00:00, 5.10s/it]
Accuracy = 90.83
Log file has been saved to ../logs/completed-simsiam-cifar10-experiment-resnet18_cifar_variant1(2)
```
To evaluate separately:
```
CUDA_VISIBLE_DEVICES=4 python linear_eval.py --data_dir ../Data/ --log_dir ../logs/ -c configs/simsiam_cifar_eval.yaml --ckpt_dir ~/.cache/ --hide_progress --eval_from ~/simsiam-cifar10-experiment-resnet18_cifar_variant1.pth
creating file ../logs/in-progress_0111061045_simsiam-cifar10-experiment-resnet18_cifar_variant1
Evaluating: 100%|##########################################################################################################| 200/200 [16:52<00:00, 5.06s/it]
Accuracy = 90.87
```
>`export DATA="/path/to/your/datasets/"` and `export LOG="/path/to/your/log/"` will save you the trouble of entering the folder name every single time!
### Run SimCLR
```
CUDA_VISIBLE_DEVICES=1 python main.py --data_dir ../Data/ --log_dir ../logs/ -c configs/simclr_cifar.yaml --ckpt_dir ~/.cache/ --hide_progress
```
### Run BYOL
```
CUDA_VISIBLE_DEVICES=2 python main.py --data_dir ../Data/ --log_dir ../logs/ -c configs/byol_cifar.yaml --ckpt_dir ~/.cache/ --hide_progress
```
### TODO
- convert from data-parallel (DP) to distributed data-parallel (DDP)
- create PyPI package `pip install simsiam-pytorch`
If you find this repo helpful, please consider star so that I have the motivation to improve it.