RotNet
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ZJCV
Image rotation correction based on DeepLearning
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«RotNet» realizes image rotation correction based on deep learning
Table of Contents
- Table of Contents
- Background
- Installation
- Usage
- How to use
- How to add dataset
- Maintainers
- Thanks
- Contributing
- License
Background
Looking for information on the Internet, we found that the image rotation angle can be detected by deep learning algorithm. Refer to
- d4nst/RotNet
- Correcting Image Orientation Using Convolutional Neural Networks
- Image Orientation Estimation with Convolutional Networks
- UNSUPERVISED REPRESENTATION LEARNING BY PREDICTING IMAGE ROTATIONS
The corresponding implementation can't meet the current performance requirements, so I implement one myself
Installation
$ pip install -r requirements.txt
Usage
How to use
- train
$ export PYTHONPATH=<root path>
$ CUDA_VISIBLE_DEVICES=0 python tools/train.py -cfg=configs/xxx.yaml
- test
$ export PYTHONPATH=<root path>
$ CUDA_VISIBLE_DEVICES=0 python demo/demo.py -cfg=demo/xxx.yaml
How to add dataset
Suppose your dataset is in the following format
root/dog/xxx.png
root/dog/xxy.png
root/dog/xxz.png
root/cat/123.png
root/cat/nsdf3.png
root/cat/asd932_.png
modify config_file like this
DATASET:
NAME: 'GeneralDataset'
TRAIN_ROOT: /path/to/train_root
TEST_ROOT: /path/to/test_root
TOP_K: (1, 5)
Maintainers
- zhujian - Initial work - zjykzj
Thanks
Contributing
Anyone's participation is welcome! Open an issue or submit PRs.
Small note:
- Git submission specifications should be complied with Conventional Commits
- If versioned, please conform to the Semantic Versioning 2.0.0 specification
- If editing the README, please conform to the standard-readme specification.
License
Apache License 2.0 © 2020 zjykzj
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