colorization-siggraph

## Use Case and High-Level Description

The colorization-siggraph model is one of the colorization group of models designed to real-time user-guided image colorization. Model was trained on ImageNet dataset with synthetically generated user interaction. For details about this family of models, check out the repository.

Model consumes as input L-channel of LAB-image (also user points and binary mask as optional inputs). Model give as output predict A- and B-channels of LAB-image.

## Specification

Metric Value
Type Colorization
GFLOPs 150.5441
MParams 34.0511
Source framework PyTorch*

## Accuracy

The accuracy metrics were calculated on the ImageNet validation dataset using VGG16 Caffe model and colorization as preprocessing.

For preprocessing rgb -> gray -> colorization recieved values:

Metric Value with preprocessing Value without preprocessing
Accuracy top-1 58.25% 70.96%
Accuracy top-5 81.78% 89.88%

## Input

1. Image, name - data_l, shape - 1,1,256,256, format is B,C,H,W where:

• B - batch size
• C - channel
• H - height
• W - width

L-channel of LAB-image.

2. Image, name - user_ab, shape - 1,2,256,256, format is B,C,H,W where:

• B - batch size
• C - channel
• H - height
• W - width

Channel order is AB channels of LAB-image. Input for user points.

3. Mask, name - user_map, shape - 1,1,256,256, format is B,C,H,W where:

• B - batch size
• C - number of flags for pixel
• H - height
• W - width

This input is a binary mask indicating which points are provided by the user. The mask differentiates unspecified points from user-specified gray points with (a,b) = 0. If point(pixel) was specified the flag will be equal to 1.

NOTE: You don't need to specify all 3 inputs to use the model. If you dont't want to use local user hints (user points), you can use only data_l input.

## Output

Image, name - color_ab, shape - 1,2,256,256, format is B,C,H,W where:

• B - batch size
• C - channel
• H - height
• W - width

Channel order is AB channels of LAB-image.

## Legal Information

Copyright (c) 2016, Richard Zhang, Phillip Isola, Alexei A. Efros
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
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this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
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