colorization-v2

## Use Case and High-Level Description

The colorization-v2 model is one of the colorization group of models designed to perform image colorization. Model was trained on ImageNet dataset. For details about this family of models, check out the repository.

Model consumes as input L-channel of LAB-image. Model give as output predict A- and B-channels of LAB-image.

## Specification

Metric Value
Type Colorization
GFLOPs 83.6045
MParams 32.2360
Source framework PyTorch*

## Accuracy

The accuracy metrics were calculated between generated images by model and real validation images from ImageNet dataset. Results are obtained on subset of 2000 images.

Metric Value
PSNR 26.99dB
SSIM 0.90

Also, metrics can be calculated using VGG16 caffe model and colorization as preprocessing. The results below are obtained on the validation images from ImageNet dataset.

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

Metric Value with preprocessing Value without preprocessing
Accuracy top-1 57.75% 70.96%
Accuracy top-5 81.50% 89.88%

## Input

### Original model

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

Channel order is L-channel.

### Converted model

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

Channel order is L-channel.

## Output

### Original model

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.

### Converted model

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.

You can download models and if necessary convert them into Inference Engine format using the Model Downloader and other automation tools as shown in the examples below.

An example of using the Model Converter:

## 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:
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list of conditions and the following disclaimer.
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