Cross-Domain Adaptive Teacher for Object Detection
This is the PyTorch implementation of our paper:
Cross-Domain Adaptive Teacher for Object Detection
Yu-Jhe Li, Xiaoliang Dai, Chih-Yao Ma, Yen-Cheng Liu, Kan Chen, Bichen Wu, Zijian He, Kris Kitani, Peter Vajda
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022
Installation
Prerequisites
- Python ≥ 3.6
- PyTorch ≥ 1.5 and torchvision that matches the PyTorch installation.
- Detectron2 == 0.3 (The version I used to run my code)
Install python env
To install required dependencies on the virtual environment of the python (e.g., virtualenv for python3), please run the following command at the root of this code:
$ python3 -m venv /path/to/new/virtual/environment/.
$ source /path/to/new/virtual/environment/bin/activate
For example:
$ mkdir python_env
$ python3 -m venv python_env/
$ source python_env/bin/activate
Build Detectron2 from Source
Follow the INSTALL.md to install Detectron2.
Dataset download
-
Download the datasets
-
Organize the dataset as the Cityscapes and PASCAL VOC format following:
adaptive_teacher/
└── datasets/
└── cityscapes/
├── gtFine/
├── train/
└── test/
└── val/
├── leftImg8bit/
├── train/
└── test/
└── val/
└── cityscapes_foggy/
├── gtFine/
├── train/
└── test/
└── val/
├── leftImg8bit/
├── train/
└── test/
└── val/
└── VOC2012/
├── Annotations/
├── ImageSets/
└── JPEGImages/
└── clipark/
├── Annotations/
├── ImageSets/
└── JPEGImages/
└── watercolor/
├── Annotations/
├── ImageSets/
└── JPEGImages/
Training
- Train the Adaptive Teacher under PASCAL VOC (source) and Clipart1k (target)
python train_net.py \
--num-gpus 8 \
--config configs/faster_rcnn_R101_cross_clipart.yaml\
- Train the Adaptive Teacher under cityscapes (source) and foggy cityscapes (target)
python train_net.py\
--num-gpus 8\
--config configs/faster_rcnn_VGG_cross_city.yaml\
OUTPUT_DIR output/exp_city
Resume the training
python train_net.py \
--resume \
--num-gpus 8 \
--config configs/faster_rcnn_R101_cross_clipart.yaml MODEL.WEIGHTS <your weight>.pth
Evaluation
python train_net.py \
--eval-only \
--num-gpus 8 \
--config configs/faster_rcnn_R101_cross_clipart.yaml \
MODEL.WEIGHTS <your weight>.pth
Results and Model Weights
Real to Artistic Adaptation:
| Backbone | Source set (labeled) | Target set (unlabeled) | Batch size | [email protected] | Model Weights | Comment |
|---|---|---|---|---|---|---|
| R101 | VOC12 | Clipark1k | 16 labeled + 16 unlabeled | 40.6 | link (coming soon) | Ours w/o discriminator |
| R101 | VOC12 | Clipark1k | 16 labeled + 16 unlabeled | 49.3 | link (coming soon) | Ours in the paper |
| R101+FPN | VOC12 | Clipark1k | 16 labeled + 16 unlabeled | 51.2 | link (coming soon) | For future work |
Weather Adaptation:
| Backbone | Source set (labeled) | Target set (unlabeled) | Batch size | [email protected] | Model Weights | Comment |
|---|---|---|---|---|---|---|
| VGG16 | Cityscapes | Foggy Cityscapes | 16 labeled + 16 unlabeled | 48.7 | link (coming soon) | Ours w/o discriminator |
| VGG16 | Cityscapes | Foggy Cityscapes | 16 labeled + 16 unlabeled | 50.9 | link (coming soon) | Ours in the paper |
| VGG16+FPN | Cityscapes | Foggy Cityscapes | 16 labeled + 16 unlabeled | 57.4 | link (coming soon) | For future work |
Citation
If you use Adaptive Teacher in your research or wish to refer to the results published in the paper, please use the following BibTeX entry.
@inproceedings{li2022cross,
title={Cross-Domain Adaptive Teacher for Object Detection},
author={Li, Yu-Jhe and Dai, Xiaoliang and Ma, Chih-Yao and Liu, Yen-Cheng and Chen, Kan and Wu, Bichen and He, Zijian and Kitani, Kris and Vajda, Peter},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2022}
}
Also, if you use Detectron2 in your research, please use the following BibTeX entry.
@misc{wu2019detectron2,
author = {Yuxin Wu and Alexander Kirillov and Francisco Massa and
Wan-Yen Lo and Ross Girshick},
title = {Detectron2},
howpublished = {\url{https://github.com/facebookresearch/detectron2}},
year = {2019}
}
License
This project is licensed under CC-BY-NC 4.0 License, as found in the LICENSE file.
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