UFG-NCD

Official pytorch implementation of our paper: Novel Class Discovery for Ultra-Fine-Grained Visual Categorization (CVPR2024 Highlight)

Environment 🐍

Our implementation is based on uno, while logging is performed using wandb, we use conda to create the environment and install the dependencies.

Follow the commands below to setup environment.

# create environment
conda create -n rapl python=3.8
conda activate rapl

# choose the cudatoolkit version on your own
conda install pytorch==1.7.1 torchvision==0.8.2 cudatoolkit=11.0 -c pytorch

# install other dependencies
pip install tqdm wandb scikit-learn pandas pytorch-lightning==1.1.3 lightning-bolts==0.3.0 

# create checkpoints directory
mkdir checkpoints

Datasets 💾

We use Ultra-FGVC as our main experiment datasets, specifically the SoyAgeing-{R1, R3, R4, R5, R6}.

You can download the datasets here, also make sure you have changed the datasets root that defined in config.py.

Supervised Learning 🌟

python supervised_learning.py --dataset SoyAgeing-R1 --task ncd

Discovery Learning ✨

python discovery_learning.py --dataset SoyAgeing-R1 --task ncd --pretrained ncd-supervised-SoyAgeing-R1-pc2.0-cra0.6-reg1.0.pth

Citation 📋

If you find our work helpful, please consider citing our paper:

@InProceedings{Liu_2024_CVPR,
    author    = {Liu, Yu and Cai, Yaqi and Jia, Qi and Qiu, Binglin and Wang, Weimin and Pu, Nan},
    title     = {Novel Class Discovery for Ultra-Fine-Grained Visual Categorization},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2024},
    pages     = {17679-17688}
}

Acknowledgements 🎁

• UNO
• Ultra-FGVC