# 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.

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.

python3 <omz_dir>/tools/downloader/downloader.py --name <model_name>

An example of using the Model Converter:

python3 <omz_dir>/tools/downloader/converter.py --name <model_name>

## 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.

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.

python3 <omz_dir>/tools/downloader/downloader.py --name <model_name>

An example of using the Model Converter:

python3 <omz_dir>/tools/downloader/converter.py --name <model_name>

## 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.

* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.