Pytorch implementation of Zero-DCE++

Related tags

Deep Learning Zero-DCE_extension
Overview

Zero-DCE++

You can find more details here: https://li-chongyi.github.io/Proj_Zero-DCE++.html.

You can find the details of our CVPR version: https://li-chongyi.github.io/Proj_Zero-DCE.html.

✌ If you use this code, please cite our paper. Please hit the star at the top-right corner. Thanks!

Pytorch

Pytorch implementation of Zero-DCE++

Requirements

  1. Python 3.7
  2. Pytorch 1.0.0
  3. opencv
  4. torchvision 0.2.1
  5. cuda 10.0

Zero-DCE++ does not need special configurations. Just basic environment.

Or you can create a conda environment to run our code like this: conda create --name zerodce++_env opencv pytorch==1.0.0 torchvision==0.2.1 cuda100 python=3.7 -c pytorch

Folder structure

Download the Zero-DCE++ first. The following shows the basic folder structure.


├── data
│   ├── test_data 
│   └── train_data 
├── lowlight_test.py # testing code
├── lowlight_train.py # training code
├── model.py # Zero-DEC++ network
├── dataloader.py
├── snapshots_Zero_DCE++
│   ├── Epoch99.pth #  A pre-trained snapshot (Epoch99.pth)

Test:

cd Zero-DCE++

python lowlight_test.py

The script will process the images in the sub-folders of "test_data" folder and make a new folder "result" in the "data". You can find the enhanced images in the "result" folder.

Train:

cd Zero-DCE++

python lowlight_train.py

License

The code is made available for academic research purpose only. This project is open sourced under MIT license.

Bibtex

@inproceedings{Zero-DCE++,
 author = {Li, Chongyi and Guo, Chunle Guo and Loy, Chen Change},
 title = {Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation},
 booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
 pages    = {},
 month = {},
 year = {2021}
 doi={10.1109/TPAMI.2021.3063604}
}

(Full paper: https://ieeexplore.ieee.org/document/9369102 or arXiv version: https://arxiv.org/abs/2103.00860)

Contact

If you have any questions, please contact Chongyi Li at [email protected] or Chunle Guo at [email protected].

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Comments
  • Why scale factor is 12 in testing but 1 in training?

    Why scale factor is 12 in testing but 1 in training?

    Hello, authors. Thanks for your excellent work. I notice that line 20 of lowlight_test.py defines the scale factor as 12, whereas line 105 of lowlight_train.py assumes that the default scale factor is 1. I would like to know (1) the reason for this inconsistency (2) which one I should adopt for training the model. Thanks!

    opened by ShenZheng2000 2
  • Images are saturated for adobe5k

    Images are saturated for adobe5k

    Hi,

    I trained ZDCE++ in adobe5k for 100 epochs. I had kept 4000 images for training, 500 for validation and 500 for testing. I used the original folder for training. I found the results to be saturated. One more question is on why we are not checking for validation loss. If we monitor validation loss, can this be improved. Any help will be appreciated.

    Thank you

    a0014-WP_CRW_6320

    a0001-jmac_DSC1459

    a0002-dgw_005

    opened by deepakkupanda 1
  • Error while loading the model in macos cpu

    Error while loading the model in macos cpu

    After installing all the module in requirements, I get the following error when loading the model _pickle.UnpicklingError: A load persistent id instruction was encountered but no persistent_load function was specified.

    I checked the forums for this error https://blog.csdn.net/wavehaha/article/details/114900600 which suggests inconsistency pytorch version But i am using pytorch=1.0.0 only

    Has anybody encountered it? How to load the model on cpu ?

    opened by BurakaKrishna 7
  • Where to download DICM dataset?

    Where to download DICM dataset?

    Hello, when I evaluate the code, there is no file containing the dataset DICM、LIME、MEF infered in paper "Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation", can I ask for a help?

    opened by Naturallyyeyeye 0
Owner
Chongyi Li
Chongyi Li
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