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* |
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% |
Image, name - data_l
, shape - 1,1,256,256
, format is B,C,H,W
where:
B
- batch sizeC
- channelH
- heightW
- widthChannel order is L-channel.
Image, name - data_l
, shape - 1,1,256,256
, format is B,C,H,W
where:
B
- batch sizeC
- channelH
- heightW
- widthChannel order is L-channel.
Image, name - color_ab
, shape - 1,2,256,256
, format is B,C,H,W
where:
B
- batch sizeC
- channelH
- heightW
- widthChannel order is AB channels of LAB-image.
Image, name - color_ab
, shape - 1,2,256,256
, format is B,C,H,W
where:
B
- batch sizeC
- channelH
- heightW
- widthChannel 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 Downloader:
An example of using the Model Converter:
The original model is distributed under the following license: