Image-Colorization
Automatic Image Colorization using a Convolutional Network (U-Net)
- Using the U-Net ConvNet Architecture for end-to-end image colorization.
- Takes as input a grayscale 32x32 image and returns a colorized 32x32 version
- The model has been trained on the CIFAR-10 32x32 images for 100 epochs.
- The model achieved an accuracy of 55.14% and a mean absolute error(MAE) of 0.0464 on the test set.
Model Achitecture
The model uses U-Net architecture which uses skip connections to preserve the lower level details and structute of an image, that are lost due to contracting bottle-neck.
The U-Net Architecture
Demo
A web interface has been implemented, where a user uploads a grayscale image as input and gets a colored image displayed as output
Sample Run
Requirements
- NumPy
- Tensorflow
- Keras
- SciPy
- Flask