Skip to content

Latest commit

 

History

History
53 lines (38 loc) · 1.88 KB

face-detection-0106.md

File metadata and controls

53 lines (38 loc) · 1.88 KB

face-detection-0106

Use Case and High-Level Description

Face detector based on ResNet152 as a backbone with a ATSS head for indoor/outdoor scenes shot by a front-facing camera.

Example

Specification

Metric Value
AP (WIDER) 94.49%
GFlops 339.602
MParams 69.920
Source framework PyTorch*

Average Precision (AP) is defined as an area under the precision/recall curve. All numbers were evaluated by taking into account only faces bigger than 64 x 64 pixels.

Performance

Inputs

  1. name: "input" , shape: [1x3x640x640] - An input image in the format [BxCxHxW], where:

    • B - batch size
    • C - number of channels
    • H - image height
    • W - image width

    Expected color order - BGR.

Outputs

  1. The "boxes" is a blob with shape: [N, 5], where N is the number of detected bounding boxes. For each detection, the description has the format: [x_min, y_min, x_max, y_max, conf], where:
    • (x_min, y_min) - coordinates of the top left bounding box corner
    • (x_max, y_max) - coordinates of the bottom right bounding box corner.
    • conf - confidence for the predicted class
  2. The "labels" is a blob with shape: [N], where N is the number of detected bounding boxes. It contains label per each detected box.

Legal Information

[*] Other names and brands may be claimed as the property of others.