A Submission for LMNL 2022 (1st Learning and Mining with Noisy Labels Challenge IJCAI-ECAI 2022).
> pip install -r requirements.txt
- Clone to the local.
> git clone https://github.com/chenchenzong/BLDR.git BLDR
- Install required packages.
> cd BLDR
> mkdir ckpts
> mkdir dataset
> pip install requirements.txt
- Set parameters.
- Edit run.sh
- For cifar 10 aggre/rand1/worst
> nohup python3 train.py --dataset cifar10 --noise_type worst > c10_worst.log
> nohup python3 train.py --dataset cifar10 --noise_type rand1 > c10_rand1.log
> nohup python3 train.py --dataset cifar10 --noise_type aggre > c10_aggre.log
- For cifar 100 noisy
> nohup python3 train.py --dataset cifar100 --noise_type noisy100 --updateW_epochs 40 > c100.log
- Run the code. (The recognition results are named detection.npy, attention to file rewriting!)
> sh test.sh # train the model
> python learning.py --dataset dataset --noise_type noise_type # for image classification
> python detection.py --dataset dataset --noise_type noise_type # for label noise detections