MobileFace Detection V1 is a Light and Fast Face Detector for Edge Devices (LFFD) model based on Yolo V3 architecture and trained with MXNet*. For details see the repository and paper.
Metric | Value |
---|---|
Type | Detection |
GFLOPs | 3.5456 |
MParams | 7.6828 |
Source framework | MXNet* |
Metric | Value |
---|---|
mAP | 78.7488% |
Image, name - data
, shape - 1, 256, 256, 3
, format -B, H, W, C
, where:
B
- batch sizeH
- heightW
- widthC
- channel
Expected color order - BGR
.
The converted model has the same parameters as the original model.
WARNING: Please note that the input layout of the converted model is
B, H, W, C
.
-
The array of detection summary info, name -
yolov30_slice_axis1
, shape -1, 18, 8, 8
. The anchor values are118,157, 186,248, 285,379
. -
The array of detection summary info, name -
yolov30_slice_axis2
, shape -1, 18, 16, 16
. The anchor values are43,54, 60,75, 80,106
. -
The array of detection summary info, name -
yolov30_slice_axis3
, shape -1, 18, 32, 32
. The anchor values are10,12, 16,20, 23,29
.
For each case format is B, N*DB, Cx, Cy
, where:
B
- batch sizeN
- number of detection boxes for cellDB
- size of each detection boxCx
,Cy
- cell index
Detection box has format [x
, y
, h
, w
, box_score
, face_score
], where:
- (
x
,y
) - raw coordinates of box center, apply sigmoid function to get coordinates relative to the cell h
,w
- raw height and width of box, apply exponential function and multiply by corresponding anchors to get height and width values relative to cellbox_score
- confidence of detection box, apply sigmoid function to get confidence in [0, 1] rangeface_score
- probability that detected object belongs to face class, apply sigmoid function to get confidence in [0, 1] range
-
The array of detection summary info, name -
yolov30_yolooutputv30_conv0_fwd/YoloRegion
, shape -1, 18, 8, 8
. The anchor values are118,157, 186,248, 285,379
. -
The array of detection summary info, name -
yolov30_yolooutputv31_conv0_fwd/YoloRegion
, shape -1, 18, 16, 16
. The anchor values are43,54, 60,75, 80,106
. -
The array of detection summary info, name -
yolov30_yolooutputv32_conv0_fwd/YoloRegion
, shape -1, 18, 32, 32
. The anchor values are10,12, 16,20, 23,29
.
For each case format is B, N*DB, Cx, Cy
, where:
B
- batch sizeN
- number of detection boxes for cellDB
- size of each detection boxCx
,Cy
- cell index
Detection box has format [x
, y
, h
, w
, box_score
, face_score
], where:
- (
x
,y
) - raw coordinates of box center to the cell h
,w
- raw height and width of box, apply exponential function and multiply by corresponding anchors to get height and width values relative to cellbox_score
- confidence of detection box in [0, 1] rangeface_score
- probability that detected object belongs to face class in [0, 1] range
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:
omz_downloader --name <model_name>
An example of using the Model Converter:
omz_converter --name <model_name>
The original model is distributed under the following license:
MIT License
Copyright (c) 2018
Permission is hereby granted, free of charge, to any person obtaining a copy
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in the Software without restriction, including without limitation the rights
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copies or substantial portions of the Software.
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SOFTWARE.