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.
Metric | Value |
---|---|
Type | Colorization |
GFLOPs | 150.5441 |
MParams | 34.0511 |
Source framework | PyTorch* |
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% |
Image, name - data_l
, shape - 1,1,256,256
, format is B,C,H,W
where:
B
- batch sizeC
- channelH
- heightW
- widthL-channel of LAB-image.
Image, name - user_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. Input for user points.
Mask, name - user_map
, shape - 1,1,256,256
, format is B,C,H,W
where:
B
- batch sizeC
- number of flags for pixelH
- heightW
- widthThis 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.
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: