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<h2 align="center"> <a href="https://github.com/nazmul-karim170/UNICON-Noisy-Label">UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning</a></h2> <h5 align="center"> If you like our project, please give us a star ⭐ on GitHub for the latest update. </h2> <h5 align="center">

</h5>

Paper

Training Pipeline

UNICON Framework

Framework

Installation Guide

Create a conda environment

conda create -n unicon 
conda activate unicon

After creating a virtual environment, install the required packages
```
pip install -r requirements.txt
```

Download the Datasets

For adding Synthetic Noise, download these datasets
1. <a href="https://www.kaggle.com/c/cifar-10/data">CIFAR10</a>
2. <a href="https://www.kaggle.com/datasets/melikechan/cifar100">CIFAR100</a>
3. <a href="https://www.kaggle.com/datasets/nikhilshingadiya/tinyimagenet200">Tiny-ImageNet</a>
For Datasets with Real-World Label Noise
1. <a href="https://github.com/Cysu/noisy_label">Clothing1M</a> (Please contact tong.xiao.work[at]gmail[dot]com to get the download link)
2. <a href="https://data.vision.ee.ethz.ch/cvl/webvision/dataset2017.html">WebVision</a>

UNICON Training

Example run (CIFAR10 with 50% symmetric noise)

python Train_cifar.py --dataset cifar10 --num_class 10 --data_path ./data/cifar10 --noise_mode 'sym' --r 0.5

Example run (CIFAR100 with 90% symmetric noise)

python Train_cifar.py --dataset cifar100 --num_class 100 --data_path ./data/cifar100 --noise_mode 'sym' --r 0.9

This will throw an error as downloaded files will not be in the proper folder. That is why they must be manually moved to the "data_path".

Example Run (TinyImageNet with 50% symmetric noise)
```
python Train_TinyImageNet.py --ratio 0.5
```

Example run (Clothing1M)

python Train_clothing1M.py --batch_size 32 --num_epochs 200

Example run (Webvision)
```
python Train_webvision.py 
```

Reference

If you have any questions, do not hesitate to contact nazmul.karim170@gmail.com

Also, if you find our work useful please consider citing our work:

@InProceedings{Karim_2022_CVPR,
    author    = {Karim, Nazmul and Rizve, Mamshad Nayeem and Rahnavard, Nazanin and Mian, Ajmal and Shah, Mubarak},
    title     = {UniCon: Combating Label Noise Through Uniform Selection and Contrastive Learning},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {9676-9686}
}