mobilefacedet-v1-mxnet

## Use Case and High-Level Description

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.

## Specification

Metric Value
Type Detection
GFLOPs 3.5456
MParams 7.6828
Source framework MXNet*

Metric Value
mAP 78.7488%

## Input

### Original model

Image, name - data, shape - [1x256x256x3], format -[BxHxWxC] where:

• B - batch size
• H - height
• W - width
• C - channel

Expected color order - BGR.

### Converted model

The converted model has the same parameters as the original model.

WARNING: Please note that the input layout of the converted model is [BxHxWxC].

## Output

### Original model

1. The array of detection summary info, name - yolov30_slice_axis1, shape - 1,18,8,8. The anchor values are 118,157, 186,248, 285,379.
2. The array of detection summary info, name - yolov30_slice_axis2, shape - 1,18,16,16. The anchor values are 43,54, 60,75, 80,106.
3. The array of detection summary info, name - yolov30_slice_axis3, shape - 1,18,32,32. The anchor values are 10,12, 16,20, 23,29.

For each case format is B,N*DB,Cx,Cy, where

• B - batch size
• N - number of detection boxes for cell
• DB - size of each detection box
• Cx, 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 cell
• box_score - confidence of detection box, apply sigmoid function to get confidence in [0,1] range
• face_score - probability that detected object belongs to face class, apply sigmoid function to get confidence in [0,1] range

### Converted model

1. The array of detection summary info, name - yolov30_yolooutputv30_conv0_fwd/YoloRegion, shape - 1,18,8,8. The anchor values are 118,157, 186,248, 285,379.
2. The array of detection summary info, name - yolov30_yolooutputv31_conv0_fwd/YoloRegion, shape - 1,18,16,16. The anchor values are 43,54, 60,75, 80,106.
3. The array of detection summary info, name - yolov30_yolooutputv32_conv0_fwd/YoloRegion, shape - 1,18,32,32. The anchor values are 10,12, 16,20, 23,29.

For each case format is B,N*DB,Cx,Cy, where

• B - batch size
• N - number of detection boxes for cell
• DB - size of each detection box
• Cx, 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 cell
• box_score - confidence of detection box in [0,1] range
• face_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 Converter:

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