Official implementation for paper: Masked Retraining Teacher-student Framework for Domain Adaptive Object Detection

By Zijing Zhao, Sitong Wei, Qingchao Chen, Dehui Li, Yifan Yang, Yuxin Peng and Yang Liu

The paper has been accepted by IEEE/CVF International Conference on Computer Vision (ICCV), 2023

Domain adaptive Object Detection (DAOD) leverages a labeled domain (source) to learn an object detector generalizing to a novel domain without annotation (target). Recent advances use a teacher-student framework, i.e., a student model is supervised by the pseudo labels from a teacher model. Though great success, they suffer from the limited number of pseudo boxes with incorrect predictions caused by the domain shift, misleading the student model to get sub-optimal results. To mitigate this problem, we propose Masked Retraining Teacher-student framework (MRT) which leverages masked autoencoder and selective retraining mechanism on detection transformer. Experiments show that our method outperforms existing approaches and achieves state-of-the-art on three domain adaptive object detection benchmarks.

We use Deformable DETR as the base detector. This code is built upon the original repository: https://github.com/fundamentalvision/Deformable-DETR, we thank for their excellent work.

• Linux, CUDA >= 11.1, GCC >= 8.4

• Python >= 3.8

• torch >= 1.10.1, torchvision >= 0.11.2

• Other requirements

  pip install -r requirements.txt

cd ./models/ops
sh ./make.sh
# unit test (should see all checking is True)
python test.py

We provide the 2 benchmarks in our paper:

• city2foggy: cityscapes dataset is used as source domain, and foggy_cityscapes(0.02) is used as target domain.
• sim2city: sim10k dataset is used as source domain, and cityscapes which only record AP of cars is used as target domain.
• city2bdd: cityscapes dataset is used as source domain, and bdd100k-daytime is used as target domain.

You can download the raw data from the official websites: cityscapes, foggy_cityscapes, sim10k, bdd100k. We provide the annotations that are converted into coco style, download from here and organize the datasets and annotations as following:

[data_root]
└─ cityscapes
	└─ annotations
		└─ cityscapes_train_cocostyle.json
		└─ cityscapes_train_caronly_cocostyle.json
		└─ cityscapes_val_cocostyle.json
		└─ cityscapes_val_caronly_cocostyle.json
	└─ leftImg8bit
		└─ train
		└─ val
└─ foggy_cityscapes
	└─ annotations
		└─ foggy_cityscapes_train_cocostyle.json
		└─ foggy_cityscapes_val_cocostyle.json
	└─ leftImg8bit_foggy
		└─ train
		└─ val
└─ sim10k
	└─ annotations
		└─ sim10k_train_cocostyle.json
		└─ sim10k_val_cocostyle.json
	└─ JPEGImages
└─ bdd10k
	└─ annotations
		└─ bdd100k_daytime_train_cocostyle.json
		└─ bdd100k_daytime_val_cocostyle.json
	└─ JPEGImages

To use additional datasets, you can edit datasets/coco_style_dataset.py and add key-value pairs to CocoStyleDataset.img_dirs and CocoStyleDataset.anno_files .

As has been discussed in implementation details in the paper, to save computation cost, our method is designed as a three-stage paradigm. We first perform source_only training which is trained standardly by labeled source domain. Then, we perform cross_domain_mae to train the model with MAE branch. Finally, we perform teaching which utilize a teacher-student framework with MAE branch and selective retraining.

For example, for city2foggy benchmark, first edit the files in configs/def-detr-base/city2foggy/ to specify your own DATA_ROOT and OUTPUT_DIR, then run:

sh configs/def-detr-base/city2foggy/source_only.sh
sh configs/def-detr-base/city2foggy/cross_domain_mae.sh
sh configs/def-detr-base/city2foggy/teaching.sh

We use tensorboard to record the loss and results. Run the following command to see the curves during training:

tensorboard --logdir=<YOUR/LOG/DIR>

To evaluate the trained model and get the predicted results, run:

sh configs/def-detr-base/city2foggy/evaluation.sh

We conduct all experiments with batch size 8 (for source_only stage, 8 labeled samples; for cross_domain_mae and MRT teaching stage, 8 labeled samples and 8 unlabeled samples), on 2 NVIDIA A100 GPUs.

city2foggy: cityscapes → foggy cityscapes(0.02)

sim2city: sim10k → cityscapes(car only)

city2bdd: cityscapes → bdd100k(daytime)

This repository is constructed and maintained by Zijing Zhao and Sitong Wei.

If you find our paper or project useful, please cite our work by the following BibTeX:

@inproceedings{zhao2023masked,
  title={Masked retraining teacher-student framework for domain adaptive object detection},
  author={Zhao, Zijing and Wei, Sitong and Chen, Qingchao and Li, Dehui and Yang, Yifan and Peng, Yuxin and Liu, Yang},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={19039--19049},
  year={2023}
}

Thanks for your attention.