Yili Wang, Xin Li, Kun Xu, Dongliang He, Qi Zhang, Fu Li, Errui Ding
[Paddle Implementation](Offical)
Pretrain data to initialize our neurOps is hosted on 百度网盘 (code:pld9).
We host all these data in 百度网盘 (code:jvvq)
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There are two preprocessed versions of MIT-Adobe FiveK, in our paper, we refer them as MIT-Adobe FiveK-Dark (originally provided by CSRNet) and MIT-Adobe FiveK-Lite (originally provided by Distort-and-Recover).
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The official PPR10K dataset link is here.
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Clone this repo
git clone https://github.com/amberwangyili/neurop -
Download the Dataset from 百度网盘 (code:jvvq) and unzip in project folder
tree -L 2 neurop/datasets # the output should be like the following: datasets/ ├── dataset-dark │ ├── testA │ ├── testB │ ├── trainA │ └── trainB ├── dataset-init │ ├── BC │ ├── EX │ └── VB ├── dataset-lite │ ├── testA │ ├── testB │ ├── trainA │ └── trainB └── dataset-ppr ├── ppr-a ├── ppr-b ├── ppr-c ├── testA ├── testM ├── trainA └── trainM -
Install Dependencies
cd neurop pip install -r requirements.txt
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We provide pretrained model weights for MIT-Adobe FiveK and PPR10K in
pretrain_models -
Run command:
python test.py -config ./configs/test/<configuaration-name>.yaml
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The evaluation results will be in the
neurop/resultsfolder
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Initialization individual neural color operators:
python train.py -config ./configs/init_neurop.yaml
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Finetune with strength predictors:
python train.py -config ./configs/train/<configuration-name>.yaml
If you find neurOp useful in your research, please use the following BibTeX entry.
@inproceedings{wang2022neurop,
author = {Wang, Yili and Li, Xin and Xu, Kun and He, Dongliang and Zhang, Qi and Li, Fu and Ding, Errui},
title = {Neural Color Operators for Sequential Image Retouching},
year = {2022},
isbn = {978-3-031-19800-7},
publisher = {Springer-Cham},
url = {https://doi.org/10.1007/978-3-031-19800-7_3},
doi = {10.1007/978-3-031-19800-7_3},
booktitle = {Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XIX},
numpages = {14},
}NeurOp is licensed under a MIT License.
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