Avatar Knowledge Distillation: Self-ensemble Teacher Paradigm with Uncertainty (AvatarKD)

🔥 Official implementation of paper "Avatar Knowledge Distillation: Self-ensemble Teacher Paradigm with Uncertainty" (AvatarKD), ACM MM 2023.

By Yuan Zhang, Weihua Chen, Yichen Lu, Tao Huang, Xiuyu Sun and Jian Cao.

Installation

Install MMRazor 0.x

git clone -b 0.x https://github.com/open-mmlab/mmrazor.git
cd mmrazor

pip install -v -e .

Prepare Data Set

Download on https://opendatalab.com

Note

If you want to distill on detection and segmentation, you should install mmdetection and mmsegmentation, respectively.

Reproducing our results

Train students with Avatars

This repo uses MMRazor as the knowledge distillation toolkit. For environment setup, please see docs/en/get_started.md.

Train student:

cd mmrazor
sh tools/mmdet/dist_train_mmdet.sh ${CONFIG} 8 ${WORK_DIR}

Example for reproducing our reppoints_x101-reppoints-r50_coco result:

sh tools/mmdet/dist_train_mmdet.sh akd_cwd_reppoints_x101-reppoints-r50_coco.py 8 work_dirs/akd_rep_x101-fpn_x50

Results

• Baseline settings:

  Student	Teacher	AvatarKD
  Faster RCNN-R50 (38.4)	Faster RCNN-R101 (39.8)	40.9
  RetinaNet-R50 (37.4)	RetinaNet-R101 (38.9)	40.3
  FCOS-R50 (38.5)	FCOS-R101 (40.8)	42.9

• Stronger teachers:

  Student	Teacher	AvatarKD
  Faster RCNN-R50 (38.4)	Cascade Mask RCNN-X101 (45.6)	42.4
  RetinaNet-R50 (37.4)	RetinaNet-X101 (41.0)	41.5
  RepPoints-R50 (38.6)	RepPoints-R101 (44.2)	42.8

Visualization

License

This project is released under the Apache 2.0 license.

Citation

@article{zhang2023avatar,
  title={Avatar Knowledge Distillation: Self-ensemble Teacher Paradigm with Uncertainty},
  author={Zhang, Yuan and Chen, Weihua and Lu, Yichen and Huang, Tao and Sun, Xiuyu and Cao, Jian},
  journal={arXiv preprint arXiv:2305.02722},
  year={2023}
}