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 calculated on 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 | 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.
The original model is distributed under the following license: