This page is for the paper appeared in CVPR2018. You can also find project page for the paper.

Here is the example of our results in watercolor images.

• Python 3.5+
• Chainer 3.0+
• ChainerCV 0.8
• Cupy 2.0+
• OpenCV 3+
• Matplotlib

Please install all the libraries. We recommend pip install -r requirements.txt.

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

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

python demo.py input/watercolor_142090457.jpg output.jpg --gpu 0 --load models/watercolor_dt_pl_ssd300

python eval_model.py --root datasets/clipart --data_type clipart --det_type ssd300 --gpu 0 --load models/clipart_dt_pl_ssd300

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

Rest of this section shows examples for experiments in clipart dataset.

1. (Preprocess): please follow instructions in ./datasets/README.md to create folders.

2. Domain transfer (DT) step

   1. python train_model.py --root datasets/dt_clipart/VOC2007 --root datasets/dt_clipart/VOC2012 --subset trainval --result result/dt_clipart --det_type ssd300 --data_type clipart --gpu 0 --max_iter 500 --eval_root datasets/clipart

   We provide models obtained in this step at ./models.

3. Pseudo labeling (PL) step

   1. python pseudo_label.py --root datasets/clipart --data_type clipart --det_type ssd300 --gpu 0 --load models/clipart_dt_ssd300 --result datasets/dt_pl_clipart
   2. python train_model.py --root datasets/dt_pl_clipart --subset train --result result/dt_pl_clipart --det_type ssd300 --data_type clipart --gpu 0 --load models/clipart_dt_ssd300 --eval_root datasets/clipart

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

@inproceedings{inoue2018cross,
  title={Cross-domain weakly-supervised object detection through progressive domain adaptation},
  author={Inoue, Naoto and Furuta, Ryosuke and Yamasaki, Toshihiko and Aizawa, Kiyoharu},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={5001--5009},
  year={2018}
}