colorization and ImageColorization

Given their similar descriptions, these two tools are competitors, with richzhang/colorization being a more established and recognized deep neural network approach as evidenced by its higher star count and publication in a top-tier computer vision conference.

colorization
51
Established
ImageColorization
26
Experimental
Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 25/25
Maintenance 0/25
Adoption 7/25
Maturity 1/25
Community 18/25
Stars: 3,465
Forks: 933
Downloads:
Commits (30d): 0
Language: Python
License: BSD-2-Clause
Stars: 29
Forks: 15
Downloads:
Commits (30d): 0
Language: Python
License:
Stale 6m No Package No Dependents
No License Stale 6m No Package No Dependents

About colorization

richzhang/colorization

Automatic colorization using deep neural networks. "Colorful Image Colorization." In ECCV, 2016.

Uses a CNN trained on Lab color space to predict chrominance from grayscale luminance, with PyTorch inference optimized for minimal dependencies. Includes both fully automatic colorization and an interactive variant enabling user-guided hints via the SIGGRAPH 2017 model. Provides pretrained weights and preprocessing/postprocessing pipelines (Lab conversion, 256×256 resizing, full-resolution reconstruction) for straightforward integration into Python workflows.

About ImageColorization

PrimozGodec/ImageColorization

Image and video colorizer is package for automatic image and video colorization. Models are allready trained

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