IDa-Det: An Information Discrepancy-aware Distillation for 1-bit Detectors

Pytorch implementation of our paper "IDa-Det: An Information Discrepancy-aware Distillation for 1-bit Detectors" accepted by ECCV2022.

Tips

Any problem, please contact the first author (Email: shengxu@buaa.edu.cn).

Our code is heavily borrowed from DeFeat (https://github.com/ggjy/DeFeat.pytorch/) and based on MMDetection (https://github.com/open-mmlab/mmdetection).

Environments

• Python 3.7
• MMDetection 2.x
• This repo uses: mmdet-v2.0 mmcv-0.5.6 cuda 10.1

Get Started

• sh script.sh

Update

We simplify and optimize the code. Now IDa-Det is successfully plugged in the original DeFeat project. The training cost is reduced by about 30% compared with the old version.

VOC Results

Pretrained model is here: GoogleDrive

Notes:

• Faster RCNN based model
• Batch: sample_per_gpu x gpu_num

Model	Batch	Lr schd	box AP	Model	Log
R101	4x2	0.01	81.9	GoogleDrive
R101-BiR18	4x1	0.004	76.9	GoogleDrive

If you find this work useful in your research, please consider to cite:

@inproceedings{xu2022ida,
  title={IDa-Det: An Information Discrepancy-Aware Distillation for 1-Bit Detectors},
  author={Xu, Sheng and Li, Yanjing and Zeng, Bohan and Ma, Teli and Zhang, Baochang and Cao, Xianbin and Gao, Peng and L{\"u}, Jinhu},
  booktitle={European Conference on Computer Vision},
  pages={346--361},
  year={2022},
  organization={Springer}
}