Awesome
Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation
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.
Requirements
- 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
.
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_dt_pl_ssd300
Evaluation of trained models
python eval_model.py --root datasets/clipart --data_type clipart --det_type ssd300 --gpu 0 --load models/clipart_dt_pl_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
Rest of this section shows examples for experiments in clipart
dataset.
-
(Preprocess): please follow instructions in
./datasets/README.md
to create folders. -
Domain transfer (DT) step
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
. -
Pseudo labeling (PL) step
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
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
Citation
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}
}