Self Supervision to Distillation for Long-Tailed Visual Recognition

This is a PyTorch implementation of the SSD-LT

Requirements

The code is built with following libraries:

• Python==3.6
• PyTorch==1.4.0
• torchvision
• tqdm

DataSet Preparation

Download the ImageNet_2014. Reorganize the dataset into long-tailed distribution according to image id lists in ./data/. The directories for the reorganized dataset should look like:

|--data
|--|--train
|--|--|--n01440764
|--|--|--|--n01440764_10027.JPEG
|--|--|--...
|--|--val
|--|--|--...
|--|--test
|--|--|--...

Training

The training procedure is composed of three stages.

• Stage I: Self-supervised guided feature learning

  python ssd_stage_i.py --cos --dist-url 'tcp://localhost:10712' --multiprocessing-distributed --world-size 1 --rank 0 [your imagenet-LT folder]
  

• Stage II: Intermediate soft labels generation

  python ssd_stage_ii.py --cos --last_stage_ckpt 'weights/stage_i/last_checkpoint.pth.tar' --dist-url 'tcp://localhost:10003' --multiprocessing-distributed --world-size 1 --rank 0 [your imagenet-LT folder]
  

• Stage III: Joint training with self-distillation

  python ssd_stage_iii.py --cos --dist-url 'tcp://localhost:11712' --multiprocessing-distributed --world-size 1 --teacher_ckpt 'weights/stage_ii/last_checkpoint.pth.tar' --rank 0 [your imagenet-LT folder]
  

An extra classifier fine-tuning step is optional after stage III using ssd_stage_ii.py for further improvement.

Evaluation

An evaluation procedure will be automatically executed when the training is finished. Also, we provide the last checkpoint of stage III for evaluation using the following scripts:

python ssd_stage_iii.py --dist-url 'tcp://localhost:10712' --multiprocessing-distributed --world-size 1 --rank 0 --resume [your checkpoint path] --evaluate [your imagenet-LT folder]

The experimental results for stage III on the ImageNet-LT dataset should be like:

	Many	Medium	Few	Overall
hard classifier	71.1	46.2	15.3	51.6
soft classifier	67.3	53.1	30.0	55.4

Acknowledgements

We especially thank the contributors of the Classifier-Balancing and MoCo for providing helpful code.

Citation

If you think our work is helpful, please feel free to cite our paper.

@inproceedings{li2021self,
  title={Self supervision to distillation for long-tailed visual recognition},
  author={Li, Tianhao and Wang, Limin and Wu, Gangshan},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={630--639},
  year={2021}
}

Contact

For any questions, please feel free to reach Tianhaolee@outlook.com.