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retinaface-resnet50

Use Case and High-Level Description

RetinaFace-R50 is a medium size model with ResNet50 backbone for Face Localization. It can output face bounding boxes and five facial landmarks in a single forward pass. More details provided in the paper and repository

Specification

Metric Value
AP (WIDER) 87.30%
GFLOPs 100.8478
MParams 29.4276
Source framework MXNet*

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.

Accuracy validation approach different from described in the original repo. For details about original WIDER results please see https://github.com/deepinsight/insightface/tree/master/detection/RetinaFace

Input

Original model:

Image, name: data, shape: 1,3,640,640, format: B,C,H,W, where:

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

Channel order is RGB.

Converted model:

Image, name: data, shape: 1,3,640,640, format: B,C,H,W, where:

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

Channel order is BGR.

Output

Original model

Model outputs are floating points tensors:

  1. name: face_rpn_cls_prob_reshape_stride32, shape: 1,4, 20, 20, format: [B, Ax2, H, W], represents detection scores from Feature Pyramid Network (FPN) level with stride 32 for 2 classes: background and face.

  2. name: face_rpn_bbox_stride32, shape: 1,8,20,20, format: [B, Ax4, H, W], represents detection box deltas from Feature Pyramid Network (FPN) level with stride 32

  3. name: face_rpn_landmark_pred_stride32, shape: 1,20,20,20, format: [B, Ax10, H, W], represents facial landmarks from Feature Pyramid Network (FPN) level with stride 32.

  4. name: face_rpn_cls_prob_reshape_stride16, shape: 1,4,40,40, format: [B, Ax2, H, W], represents detection scores from Feature Pyramid Network (FPN) level with stride 16 for 2 classes: background and face.

  5. name: face_rpn_bbox_stride16, shape: 1,8,40,40, format: [B, Ax4, H, W], represents detection box deltas from Feature Pyramid Network (FPN) level with stride 16.

  6. name: face_rpn_landmark_pred_stride16, shape: 1,20,40,40, format: [B, Ax10, H, W], represents facial landmarks from Feature Pyramid Network (FPN) level with stride 16.

  7. name: face_rpn_cls_prob_reshape_stride16, shape: 1,4,80,80, format: [B, Ax2, H, W], represents detection scores from Feature Pyramid Network (FPN) level with stride 8 for 2 classes: background and face.

  8. name: face_rpn_bbox_stride16, shape: 1,8,80,80, format: [B, Ax4, H, W], represents detection box deltas from Feature Pyramid Network (FPN) level with stride 8.

  9. name: face_rpn_landmark_pred_stride16, shape: 1,20,80,80, format: [B, Ax10, H, W], represents facial landmarks from Feature Pyramid Network (FPN) level with stride 8.

For each output format:

  • B - batch size
  • A - number of anchors
  • H - feature height
  • W - feature width

Detection box deltas have format [dx, dy, dh, dw], where:

  • (dx, dy) - regression for left-upper corner of bounding box,
  • (dh, dw) - regression by height and width of bounding box.

Facial landmarks have format [x1, y1, x2, y2, x3, y3, x4, y4, x5, y5], where:

  • (x1, y1) - coordinates of left eye
  • (x2, y2) - coordinates of rights eye
  • (x3, y3) - coordinates of nose
  • (x4, y4) - coordinates of left mouth corner
  • (x5, y5) - coordinates of right mouth corner

Converted model

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

Download a Model and Convert it into Inference Engine Format

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:

python3 <omz_dir>/tools/downloader/downloader.py --name <model_name>

An example of using the Model Converter:

python3 <omz_dir>/tools/downloader/converter.py --name <model_name>

Legal Information

The original model is distributed under the following license:

MIT License

Copyright (c) 2018 Jiankang Deng and Jia Guo

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.