https://github.com/thevarunsharma/Image-Colorization

Automatic Image Colorization using a Convolutional Network (U-Net)
https://github.com/thevarunsharma/Image-Colorization

convolutional-neural-networks deep-learning flask-application image-colorization keras machine-learning python tensorflow

Last synced: 3 months ago
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Automatic Image Colorization using a Convolutional Network (U-Net)

• Host: GitHub
• URL: https://github.com/thevarunsharma/Image-Colorization
• Owner: thevarunsharma
• Created: 2019-01-04T06:56:18.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2020-04-29T07:46:22.000Z (over 4 years ago)
• Last Synced: 2024-05-09T11:32:04.922Z (6 months ago)
• Topics: convolutional-neural-networks, deep-learning, flask-application, image-colorization, keras, machine-learning, python, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 415 KB
• Stars: 11
• Watchers: 2
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
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