An end-to-end image translation model with weight-map for color constancy

Related tags

Deep LearningCCUnet
Overview

CCUnet

An end-to-end image translation model with weight-map for color constancy

1. Download the dataset (take Colorchecker_recommended dataset as an example)

Place RCC dataset in this directory:

./datasets/ColorChecker_ Recommended

image-20210811123152958

Place the mask image data in this directory:

./datasets/masks

image-20210811123212246

2. Install required toolkits

The program needs to use tensorflow and opencv toolkit. It is recommended to install in the conda environment.

And here we create a new conda environment as follow:

image-20210811122116563

Install the latest version of tensorflow-gpu using the following command:

conda install tensorflow-gpu

image-20210811122257651

install opencv:

pip install opencv-python

image-20210811122446660

3. Training and testing

Enter the folder where CCUnet.py and dataset are located, and use the following command under the created conda environment to start training:

python CCUnet.py fold1

Here fold1 means that it is the first fold of the dataset used for this training, and can be replaced by fold2 or fold3, which means the second or the third fold used for training.

image-20210811123617043

After each epoch of training, the testing process is automatically executed.

4. Training and testing results

The train folder is used to save the image results during the training process.

image-20210811125431864

The test folder is used to save the image results during the testing process.

image-20210811124041686

The model folder is used to save the network model which achieves the best results.

image-20210811124254944

And records.txt is the log file, which records the results of the experiment.

image-20210811124356113

Owner
Jianhui Qiu
Jianhui Qiu
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