The colorization-v2
model is one of the colorization group of models designed to perform image colorization. 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 | - |
MParams | - |
Source framework | Caffe* |
The accuracy metrics calculated on ImageNet validation dataset using VGG16 caffe model and colorization as preprocessing.
For preprocessing rgb -> gray -> coloriaztion
recieved values:
Metric | Value with preprocessing | Value without preprocessing |
---|---|---|
Accuracy top-1 | 55.39% | 70.96% |
Accuracy top-5 | 79.21% | 89.88% |
Image, name - data_l
, shape - 1,1,224,224
, format is B,C,H,W
where:
B
- batch sizeC
- channelH
- heightW
- widthChannel order is L-channel. Mean values - 50.
Image, name - data_l
, shape - 1,1,224,224
, format is B,C,H,W
where:
B
- batch sizeC
- channelH
- heightW
- widthChannel order is L-channel.
Image, name - class8_ab
*, shape - 1,2,56,56
, format is B,C,H,W
where:
B
- batch sizeC
- channelH
- heightW
- widthImage, name - class8_313_rh
*, shape - 1,313,56,56
, format is B,C,H,W
where:
B
- batch sizeC
- channelH
- heightW
- widthNOTE:
class8_313_rh
layer is in front ofclass8_ab
layer,
in order for network to work, you need to reproduce class8_ab
layer with the coefficients that downloaded separately with the model. More detailed information can be found >this.
The original model is distributed under the following license: