• STAC：A Simple Semi-Supervised Learning Framework for Object Detection [Arxiv'20]
• Unbiased Teacher：Unbiased Teacher for Semi-Supervised Object Detection [ICLR'21]
• Soft Teacher：End-to-End Semi-Supervised Object Detection with Soft Teacher [ICCV'21]
• LabelMatch：Label Matching Semi-Supervised Object Detection [CVPR'22]

pip install -r requirements.txt

• Linux with Python >= 3.6
• We use mmdet=2.10.0, pytorch=1.6.0

Please refer to prepare_data.md.

Before training，please download the pretrained backbone (resnet50) to pretrained_model/backbone.

# |---------------------|--------|------|---------|---------|
# | xonsh train_gpu2.sh | config | seed | percent | dataset |
# |---------------------|--------|------|---------|---------|
cd examples/train/xonsh
## ---dataset: coco-standard---
xonsh train_gpu2.sh ./configs/baseline/baseline_ssod.py 1 1 coco-standard
## ---dataset: voc---
# xonsh train_gpu2.sh ./configs/baseline/baseline_ssod.py 1 1 voc
## ---dataset: coco-additional---
# xonsh train_gpu8.sh ./configs/baseline/baseline_ssod.py 1 1 coco-additional

• In our implementation, we use 2-gpus to train except coco-additional.

• After training, we organize the pretrained baseline to pretrained_model/baseline as follows：

  pretrained_model/
  	└── baseline/
          ├── [email protected]
          ├── [email protected]
          ├── ...
          ├── voc.pth
          └── coco.pth

  • You can also change the load_from information in config file in step 2.

## note: dataset is set to none in this step.
cd examples/train/xonsh
xonsh train_gpu8.sh ./configs/labelmatch/labelmatch_standard.py 1 1 none

• In our implementation, we use 8-gpus to train.
• You can also run bash train_ssod.sh in examples/train/bash

# please change "config" and "checkpoint" in 'eval.sh' scripts to support different dataset and trained model
cd examples/eval
xonsh eval.sh

• Please refer to performance.md for more performance presentation.

Please refer to UDA

If you use LabelMatch in your research or wish to refer to the results published in the paper, please consider citing out paper.

@inproceedings{Chen2022LabelMatching,
    title={Label Matching Semi-Supervised Object Detection},
    author={Binbin Chen, Weijie Chen, Shicai Yang, Yunyi Xuan, JieSong, Di Xie, Shiliang Pu, Mingli Song, Yueting Zhuang.},
    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
    year={2022},
}

This project is released under the Apache 2.0 license. Other codes from open source repository follows the original distributive licenses.

If you have any problem about this work, please feel free to contact Binbin Chen (chenbinbin8-at-hikvision.com) and Weijie Chen (chenweijie5-at-hikvision.com).