zhfeing / Transformer-KA-PyTorch

Official PyTorch implementation of paper "Knowledge Amalgamation for Object Detection with Transformers" (TIP)

Knowledge Amalgamation for Visual Transformers

Some of the codes are modified from DETR and UP-DETR

Official implementation for paper Knowledge Amalgamation for Object Detection with Transformers

Authors: Haofei Zhang, Feng Mao, Mengqi Xue, Gongfan Fang, Zunlei Feng, Jie Song, Mingli Song

Quick Start

1. Prepare dataset

• VOC-2012: download voc-2007+2012 dataset to folder ~/datasets/voc (you may specify this in configuration files).
• MS-COCO-2017: download MS-COCO-2017 to folder ~/datasets/MS-COCO-2017 (you may specify this in configuration files).

2. Prepare cv-lib-PyTorch

Our code requires cv-lib-PyTorch. You should download this repo and checkout to tag transformer_ka.

cv-lib-PyTorch is an open source repo currently maintained by me.

3. Train teachers

sh 1.train_teacher.sh

4. Train student with KA

Before training the student, you should modify the amalgamation config file (e.g., config/voc/amalgamation/resnet50-amg-seq-task-no_cross.yaml) so that the ckpt of all teachers are valid.

teachers:
  t1:
    cfg_fp: config/voc/multitask/resnet50-t1.yaml
    weights_fp: /path/to/teacher1.pth
  t2:
    cfg_fp: config/voc/multitask/resnet50-t2.yaml
    weights_fp: /path/to/teacher2.pth

Train the student:

sh 2.KA.sh

Citation

If you found this work useful for your research, please cite our paper:

@misc{zhang2022knowledge,
      title={Knowledge Amalgamation for Object Detection with Transformers}, 
      author={Haofei Zhang and Feng Mao and Mengqi Xue and Gongfan Fang and Zunlei Feng and Jie Song and Mingli Song},
      year={2022},
      eprint={2203.03187},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}