This is a guide to NAFNet, a machine learning model compatible with the imaged SDK. You can quickly develop AI applications using imaged SDK along with several other pre-built imaged models.



Introduction to Colorful Image Colorization

Developed by researchers Richard Zhang, Phillip Isola, and Alexei A. Efros, the Colorful Image Colorization project is a pioneering endeavor that leverages deep learning to add vibrant colors to black and white images. Initially presented at ECCV in 2016, this technology has evolved, incorporating functionalities from their subsequent work on Real-Time User-Guided Image Colorization with Learned Deep Priors, showcased at SIGGRAPH 2017.

Enhancing Images with Deep Learning

The project offers an automatic colorization tool that transforms monochrome photos into colorful images. It intelligently predicts colors based on the content of the image, learning from a vast dataset of color images to apply realistic hues that bring life to old or originally black and white photos.


The Colorful Image Colorization project not only enhances visual media by adding color but also serves as a significant example of practical applications of deep learning in art and media restoration. It invites both tech enthusiasts and general users to explore the potential of AI in creative industries.

Technical Specs:

Test Environment: MacBook Pro, 2.6 GHz Intel Core i7, 16 GB RAM

  • Model Size: 129 MB
  • CPU Inference Time: ~200 ms
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