• Better image quality

This project uses pipenv for dependency management. You need to ensure that you have pipenv installed.

Here are the commands to facilitate using this project.

git clone https://github.com/Rohith04MVK/Recolor
cd Recolor

# Install dependencies
pipenv sync -d

# Open the venv shell
pipenv shell

python3 main.py --train-type general \
  --save-path /kaggle/working/output \
  --pretrain y \
  --epochs 25 \
  --use-gpu y

python infer.py --model-path path/to/model \
  --input-img path/to/input/image \
  --output path/to/save/output/image \

You will need the following to run the above:

• Torch 1.9.1
• Python 2.8.5, Pillow 8.2.0, numpy 1.20.3, fastai 2.4
• If you want to train (and don't want to wait for 4 months):
  • A decent GPU
  • All the required NVIDIA software to run Torch on a GPU (cuda, etc)

Tested on:

main.py trains networks that can transform black and white images into color images.

Flags

• --train-type: What type of model to train, we support a face recolor model and a general recolor model. Required
• --save-path: Path to save the models too. Required
• --pretrain: If to pre-train the gan, gives better results. Required
• --epochs: Number of times to train the model for. Required
• --use-gpu: If to use a GPU to train the model. Required

Flags

• --checkpoint-dir: Path to the trained model. Required
• --input-path: Path to image to recolor. Required
• --output: Path to save the generated image to. Required

• Image-to-Image Translation with Conditional Adversarial Networks paper, which you may know by the name pix2pix.
• Colorful Image Colorization which tackled this problem like a classification problem but had its pros and cons.