https://github.com/zhmiao/opencompounddomainadaptation-ocda
Pytorch implementation for "Open Compound Domain Adaptation" (CVPR 2020 ORAL)
https://github.com/zhmiao/opencompounddomainadaptation-ocda
computer-vision cvpr2020 deep-learning domain-adaptation ocda open-compound-domain-adaptation pytorch-implementation
Last synced: about 1 month ago
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Pytorch implementation for "Open Compound Domain Adaptation" (CVPR 2020 ORAL)
- Host: GitHub
- URL: https://github.com/zhmiao/opencompounddomainadaptation-ocda
- Owner: zhmiao
- License: bsd-3-clause
- Created: 2020-03-29T05:13:35.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2021-09-19T07:16:36.000Z (over 3 years ago)
- Last Synced: 2025-02-27T16:17:59.724Z (about 2 months ago)
- Topics: computer-vision, cvpr2020, deep-learning, domain-adaptation, ocda, open-compound-domain-adaptation, pytorch-implementation
- Language: Python
- Homepage: https://liuziwei7.github.io/projects/CompoundDomain.html
- Size: 1.53 MB
- Stars: 139
- Watchers: 7
- Forks: 15
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Open Compound Domain Adaptation
[[Project]](https://liuziwei7.github.io/projects/CompoundDomain.html) [[Paper]](https://arxiv.org/abs/1909.03403) [[Demo]](https://www.youtube.com/watch?v=YcmgCCRA1qc) [[Blog]](https://bair.berkeley.edu/blog/2020/06/14/ocda/)
## Overview
`Open Compound Domain Adaptation (OCDA)` is the author's re-implementation of the compound domain adaptator described in:
"[Open Compound Domain Adaptation](https://arxiv.org/abs/1909.03403)"
[Ziwei Liu](https://liuziwei7.github.io/)\*, [Zhongqi Miao](https://github.com/zhmiao)\*, [Xingang Pan](https://xingangpan.github.io/), [Xiaohang Zhan](https://xiaohangzhan.github.io/), [Dahua Lin](http://dahua.me/), [Stella X. Yu](https://www1.icsi.berkeley.edu/~stellayu/), [Boqing Gong](http://boqinggong.info/) (CUHK & Berkeley & Google)
in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020, **Oral Presentation**
Further information please contact [Zhongqi Miao](mailto:[email protected]) and [Ziwei Liu](https://liuziwei7.github.io/).
## Requirements
* [PyTorch](https://pytorch.org/) (version >= 0.4.1)
* [scikit-learn](https://scikit-learn.org/stable/)
## Updates:
* 11/09/2020: We have uploaded C-Faces dataset. Corresponding codes will be updated shortly. Please be patient. Thank you very much!
* 06/16/2020: We have released C-Digits dataset and corresponding weights.
## Data Preparation
[[OCDA Datasets]](https://drive.google.com/drive/folders/1_uNTF8RdvhS_sqVTnYx17hEOQpefmE2r?usp=sharing)
First, please download [C-Digits](https://drive.google.com/file/d/1ro-up5YDq1Cm9n_JaOG9pRbfPYVxcV8P/view?usp=sharing), save it to a directory, and change the dataset root in the config file accordingly.
The file contains MNIST, MNIST-M, SVHN, SVHN-bal, and SynNum.
For C-Faces, please download [Multi-PIE](http://www.cs.cmu.edu/afs/cs/project/PIE/MultiPie/Multi-Pie/Home.html) first. Since it is a proprietary dataset, we can only privide the data list we used during training [here](https://drive.google.com/file/d/1OGPAJz5OXelzRE0kEhyU8h4cqgbewj_r/view?usp=sharing). We will update the dataset function accordingly.
## Getting Started (Training & Testing)
### C-Digits
To run experiments for both training and evaluation on the C-Digits datasets (SVHN -> Multi):
```bash
python main.py --config ./config svhn_bal_to_multi.yaml
```
After training is completed, the same command will automatically evaluate the trained models.
### C-Faces
* We will be releasing code for C-Faces experiements very soon.
### C-Driving
* Please refer to: https://github.com/XingangPan/OCDA-Driving-Example .
## Reproduced Benchmarks and Model Zoo
NOTE: All reproduced weights need to be decompressed into results directory:
```
OpenCompoundedDomainAdaptation-OCDA
|--results
```
### C-Digits (Results may currently have variations.)
| Source | MNIST (C) | MNIST-M (C) | USPS (C) | SymNum (O) | Avg. Acc | Download |
| :------: | :------------: | :-----------: | :---------: | :----------: | :----------: | :----------------: |
| SVHN | 89.62 | 64.53 | 81.17 | 87.86 | 80.80 | [model](https://drive.google.com/file/d/1RCMYC-NBwZQnPcDXIEIqn_z8EsDqv1a2/view?usp=sharing) |
## License and Citation
The use of this software is released under [BSD-3](https://github.com/zhmiao/OpenCompoundDomainAdaptation-OCDA/blob/master/LICENSE).
```
@inproceedings{compounddomainadaptation,
title={Open Compound Domain Adaptation},
author={Liu, Ziwei and Miao, Zhongqi and Pan, Xingang and Zhan, Xiaohang and Lin, Dahua and Yu, Stella X. and Gong, Boqing},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2020}
}
```