The mtcnn-r model is one of the mtcnn group of models designed to perform face detection. Short for "Multi-task Cascaded Convolutional Neural Network", it is implemented using the Caffe* framework. The "r" designation indicates that this model is the "refine" network intended to refine the data returned as output from the "proposal" mtcnn-p network. For details about this family of models, check out the repository.
The model input is a blob with a vector containing the first pass of face data, as returned by the mtcnn-p model. The mean values need to be subtracted as follows: [127.5, 127.5, 127.5] before passing the image blob into the network. In addition, values must be divided by 0.0078125.
The model output is a blob with a vector containing the refined face data. If there are no faces detected by the refine pass, no further processing is needed. Otherwise, you will typically use this output as input to the mtcnn-o model.
| Metric | Value |
|---|---|
| Type | Detection |
| GFLOPs | 0.003 |
| MParams | 0.1 |
| Source framework | Caffe* |
Image, name - data, shape - 1,3,24,24 in B,C,W,H format, where
B- input batch sizeC- number of image channelsW- widthH- height
Expected color order: RGB
Mean values - [127.5, 127.5, 127.5], scale value - 128
Image, name - data, shape - 1,3,24,24 in B,C,W,H format, where
B- input batch sizeC- number of image channelsW- widthH- height
Expected color order: RGB
- Face detection, name -
prob1, shape -1,2,B, contains scores across two classes (0- no face,1- face) for each input in batch. This is necessary to refine face regions frommtcnn-p. - Face location, name -
conv5-2, contains clarifications for boxes produced bymtcnn-p.
- Face detection, name -
prob1, shape -1,2,B, contains scores across two classes (0- no face,1- face) for each input in batch. This is necessary to refine face regions frommtcnn-p. - Face location, name -
conv5-2, contains clarifications for boxes produced bymtcnn-p.
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