Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems

This project contains the implementation of learning common rationale to improve self-supervised representation for fine-grained visual recognition, as presented in our paper

Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems,
Yangyang Shu, Anton van den Hengel and Lingqiao Liu*
CVPR 2023

Datasets

Dataset	Download Link
CUB-200-2011	https://paperswithcode.com/dataset/cub-200-2011
Stanford Cars	http://ai.stanford.edu/~jkrause/cars/car_dataset.html
FGVC Aircraft	http://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/

Please download and organize the datasets in this structure:

LCR
├── CUB200/
│   ├── train/ 
    ├── test/
├── StanfordCars/
│   ├── train/ 
    ├── test/
├── Aircraft/
│   ├── train/ 
    ├── test/

For byol

Install the required packages:

pip install -r requirements.txt

• The running commands for several datasets are shown below. You can also refer to run_all.sh.

python main.py --data_dir ./CUB200 --log_dir ./logs/ -c configs/byol_cub200.yaml --ckpt_dir ./.cache/ --hide_progress
python main.py --data_dir ./StanfordCars --log_dir ./logs/ -c configs/byol_stanfordcars.yaml --ckpt_dir ./.cache/ --hide_progress
python main.py --data_dir ./Aircraft --log_dir ./logs/ -c configs/byol_aircrafts.yaml --ckpt_dir ./.cache/ --hide_progress

For moco v2

• The running commands for pre-training and retrieval

python main_moco.py --epochs 100 -a resnet50 --lr 0.03 --batch-size 128 --multiprocessing-distributed --world-size 1 --rank 0 Aircraft --mlp --moco-t 0.2 --aug-plus --cos 
python main_moco.py --epochs 100 -a resnet50 --lr 0.03 --batch-size 128 --multiprocessing-distributed --world-size 1 --rank 0 StanfordCars --mlp --moco-t 0.2 --aug-plus --cos 
python main_moco.py --epochs 100 -a resnet50 --lr 0.03 --batch-size 128 --multiprocessing-distributed --world-size 1 --rank 0 CUB200 --mlp --moco-t 0.2 --aug-plus --cos 

• The running commands for linear probing

python main_lincls.py -a resnet50 --lr 30.0 --batch-size 256 --pretrained [your checkpoint path]/checkpoint_****.pth.tar --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0   Aircraft --class_num 100
python main_lincls.py -a resnet50 --lr 30.0 --batch-size 256 --pretrained [your checkpoint path]/checkpoint_****.pth.tar --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0   StanfordCars --class_num 196
python main_lincls.py -a resnet50 --lr 30.0 --batch-size 256 --pretrained [your checkpoint path]/checkpoint_****.pth.tar --dist-url 'tcp://localhost:10001' --multiprocessing-distributed --world-size 1 --rank 0   CUB200 --class_num 200

Citation If you find this code or idea useful, please cite our work:

@inproceedings{shu2023learning,
  title={Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems},
  author={Shu, Yangyang and van den Hengel, Anton and Liu, Lingqiao},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11392--11401},
  year={2023}
}