Awesome
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
.