Use Case and High-Level Description

The retinaface-resnet50-pytorch model is a PyTorch* implementation of medium size RetinaFace 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










Source framework


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 repository. In contrast to the Accuracy Checker strategy where whole set is evaluated, the validation set is divided into 3 predefined subsets(hard, medium and easy) and all subsets are verified separately in the original evaluation strategy. For details about original WIDER results please see repository.


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 BGR. Mean values: [104.0, 117.0, 123.0].

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.


Original model

Model outputs are floating points tensors:

  1. name: face_rpn_cls_prob, shape: 1, 16800, 2, format: B, A*C, 2, represents detection scores for 2 classes: background and face.

  2. name: face_rpn_bbox_pred, shape: 1, 16800, 4, format: B, A*C, 4, represents detection box deltas.

  3. name: face_rpn_landmark_pred, shape: 1, 16800, 10, format: B, A*C, 10, represents facial landmarks.

For each output format:

  • B - batch size

  • A - number of anchors

  • C - sum of products of dimensions for each stride, C = H32 * W32 + H16 * W16 + H8 * W8

  • H - feature height with the corresponding stride

  • W - feature width with the corresponding stride

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

  • (dx, dy) - regression for center 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 outputs 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/ --name <model_name>

An example of using the Model Converter:

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