Awesome

Environmnet installation

This project is built upon the following environment:

• Python 3.10
• CUDA 11.7
• PyTorch 1.13.1

The package requirements can be installed via requirements.txt,

pip install -r requirements.txt

Datasets

We use fine-grained benchmarks in this paper, including:

• The Semantic Shift Benchmark (SSB) and Herbarium19

We also use generic object recognition datasets, including:

• CIFAR100 and ImageNet

Please follow this repo to set up the data.

Download the datasets, ssb splits, and pretrained backbone by following the file structure below and set DATASET_ROOT={YOUR DIRECTORY} in config.py.

    DATASET_ROOT/
    ├── cifar100/
    │   ├── cifar-100-python\
    │   │   ├── meta/
    │       ├── ...
    ├── CUB_200_2011/
    │   ├── attributes/
    │   ├── ...
    ├── ...

    CMS/
    ├── data/
    │   ├── ssb_splits/
    ├── models/
    │   ├── dino_vitbase16_pretrain.pth
    ├── ...

Training

bash bash_scripts/contrastive_meanshift_training.sh

Example bash commands for training are as follows:

# GCD
python -m methods.contrastive_meanshift_training  \
            --dataset_name 'cub' \
            --lr 0.05 \
            --temperature 0.25 \
            --wandb 

# Inductive GCD
python -m methods.contrastive_meanshift_training  \
            --dataset_name 'cub' \
            --lr 0.05 \
            --temperature 0.25 \
            --inductive \
            --wandb 

Evaluation

bash bash_scripts/meanshift_clustering.sh

Example bash command for evaluation is as follows. It will require changing model_name.

python -m methods.meanshift_clustering \
        --dataset_name 'cub' \
        --model_name 'cub_best' \

Results and checkpoints

Experimental results on GCD task.

Experimental results on inductive GCD task.

Citation

If you find our code or paper useful, please consider citing our paper:

  @inproceedings{choi2024contrastive,
    title={Contrastive Mean-Shift Learning for Generalized Category Discovery},
    author={Choi, Sua and Kang, Dahyun and Cho, Minsu},
    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
    year={2024}
  }

Related Repos

The codebase is largely built on Generalized Category Discovery and PromptCAL.

Acknowledgements

This work was supported by the NRF grant (NRF-2021R1A2C3012728 (50%)) and the IITP grants (2022-0-00113: Developing a Sustainable Collaborative Multi-modal Lifelong Learning Framework (45%), 2019-0-01906: AI Graduate School Program at POSTECH (5%)) funded by Ministry of Science and ICT, Korea.