Contrast to Divide: self-supervised pre-training for learning with noisy labels
This is an official implementation of "Contrast to Divide: self-supervised pre-training for learning with noisy labels". The code is based on DivideMix implementation.
Results
Following tables summarize main results of the paper:
CIFAR-10:
CIFAR-100:
Clothing1M:
mini-WebVision:
Running the code
First you need to install dependencies by running pip install -r requirements.txt.
You can download pretrained self-supervised models from
Google Drive.
Alternatively, you can train them by yourself, using SimCLR implementation.
Put them into ./pretrained folder.
Then you can run the code for CIFAR
python3 main_cifar.py --r 0.8 --lambda_u 500 --dataset cifar100 --p_threshold 0.03 --data_path ./cifar-100 --experiment-name simclr_resnet18 --method selfsup --net resnet50
for Clothing1M
python3 main_clothing1M.py --data_path /path/to/clothing1m --experiment-name selfsup --method selfsup --p_threshold 0.7 --warmup 5 --num_epochs 120
or for mini-WebVision
python3 Train_webvision.py --p_threshold 0.03 --num_class 50 --data_path /path/to/webvision --imagenet_data_path /path/to/imagenet --method selfsup```
To run C2D with ELR+ just use the self-suprevised pretrained models with the original code.
License
This project is licensed under the terms of the MIT license.