Coloring Autoencoder

An artificial neural network implemented with Tensorflow 2. Contains only conventional layers. Network trained on 6000 images (75 x 75 px) from Oxford Flowers 102.

Training

To start model training type command:

python train.py 
    -h, --help                                  show this help message and exit
    -e EPOCHS, --epochs EPOCHS                  Number of training epochs
    --path PATH                                 Models directory path
    --name NAME                                 Model name
    -bs BATCH_SIZE, --batch-size BATCH_SIZE     Batch size
    -f {RGB,HSV,LAB}, --format {RGB,HSV,LAB}    Output image format
    -da, --data-ag                              Data agumentation
                

Test

To test pretrained models type command:

python test.py
    -h, --help                                  show this help message and exit
    --path PATH                                 Models directory path
    --name NAME                                 Model name
    -bs BATCH_SIZE, --batch-size BATCH_SIZE     Batch size

During the training open next command line and type, to start Tensorboard:

tensorboard --logdir models/<Your model name><Image format>/logs

Then open url localhost:6006/ to see generated summaries.

Jupyter-notebook

The jupyter-notebook examples.ipynb contains code, that allows to test pretrained model on images given by user.

To start jupyter server type:

jupyter-notebook examples.ipynb

Images examples

Input

Original image	Input Image

Output

RGB	HSV	LAB