BentengMa / cross-domain-detection

Codes and datasets for 'Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation' [Inoue+, CVPR2018].

Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation

This page is for a paper which is to appear in CVPR2018 [1]. You can also find project page for the paper in [2].

Here is the example of our results in watercolor images.

Requirements

• Python 3.5+
• Chainer 3.0+
• ChainerCV 0.8+
• Cupy 2.0+
• Matplotlib

Download models

Please go to both models and datasets directory and follow the instructions.

Usage

For more details about arguments, please refer to -h option or the actual codes.

Demo using trained models

python demo.py input/watercolor_142090457.jpg output.jpg --gpu 0 --load models/watercolor_ssd300

Evaluation of trained models

python eval_model.py --root datasets/clipart --data_type clipart --det_type ssd300 --gpu 0 --load models/clipart_ssd300

Training using clean instance-level annotations (ideal case)

python train_model.py --root datasets/clipart --subset train --result result --det_type ssd300 --data_type clipart --gpu 0

Training using virtually created instance-level annotations

Work in progress..

Citation

If you find this code useful for your research, please cite our paper:

@inproceedings{InoueCVPR2018,
  title={{Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation}},
  author={Naoto Inoue and Ryosuke Furuta and Toshihiko Yamasaki and Kiyoharu Aizawa},
  booktitle={CVPR},
   year={2018},
}

References

• [1]: N. Inoue et al. "Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation", in CVPR, 2018.
• [2]: Project page