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Automatic Image Colorization using a Convolutional Network (U-Net)

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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

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Automatic Image Colorization using a Convolutional Network (U-Net)

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