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https://github.com/tim-learn/GeoNet23_casia_tim

1st in the ICCV-2023 GeoUniDA challenge
https://github.com/tim-learn/GeoNet23_casia_tim

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1st in the ICCV-2023 GeoUniDA challenge

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README

        

# 1st in the ICCV-2023 GeoUniDA challenge

[[Challenge]](https://geonet-challenge.github.io/ICCV2023/challenge.html) [[Leaderboard]](https://eval.ai/web/challenges/challenge-page/2111/leaderboard/4979) [[Paper]](https://liangjian.xyz/assets/paper/iccvw23.pdf)

Team: CASIA-TIM (Members: Lijun Sheng, Zhengbo Wang, Jian Liang)

### File structure:
```
|–– readme.md
|–– data_list/
| |–– UNIDA/
| | |–– usa_train.txt
| | |–– asia_train.txt
| | |–– asia_test.txt
| | |–– test.txt
| |–– OBJ/
| |–– PLACE/
|
|–– main_unida.py
|–– main_places.py
|–– main_imnet.py
|–– data_list.py
|–– network.py
```

### Prerequisites:
- python == 3.10.6
- torch ==1.12.0
- torchvision == 0.13.0
- numpy, scipy, sklearn, PIL, argparse

### Dataset:
We use the dataset provided by the challenge to generate txt files and place them in the data_list folder according to the names of each dataset (i.e., UNIDA, OBJ, PLACE). If you want to run the code, please **modify the absolute paths** in all files under data_list folder.

### Note:
We integrate the source model training, model adaptation, and test file generation in single python code. The test file of the source model is saved as source_test.txt, and the test file based on the adaptive model is saved as **target_test.txt**.

### Training:

1. #### GeoUniDA
```python
python main_unida.py --dset UNIDA --gpu_id 0
```

2. #### GeoImNet
```python
python main_imnet.py --dset OBJ --gpu_id 1
```

3. #### GeoPlace
```python
python main_place.py --dset PLACE --gpu_id 2
```

### Citation

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

```
@misc{sheng2023self,
title={Self-training solutions for the ICCV 2023 GeoNet Challenge},
author={Sheng, Lijun and Wang, Zhengbo and Liang, Jian},
year={2023}
}
```

### Contact

- [**[email protected]**](mailto:[email protected])

- [[email protected]](mailto:[email protected])

- [[email protected]](mailto:[email protected])