# cross-domain-detection

**Repository Path**: zhanbiao2023/cross-domain-detection

## Basic Information

- **Project Name**: cross-domain-detection
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-11-09
- **Last Updated**: 2023-11-09

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

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

This page is for the [paper](http://openaccess.thecvf.com/content_cvpr_2018/html/Inoue_Cross-Domain_Weakly-Supervised_Object_CVPR_2018_paper.html) appeared in CVPR2018.
You can also find [project page](https://naoto0804.github.io/cross_domain_detection/) for the paper.

Here is the example of our results in watercolor images.

![fig](dets_watercolor.png)

## 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.

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`

### 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}
}
```