This is a person detector that is based on MobileNetV2 backbone with two SSD heads from 1/16 and 1/8 scale feature maps and clustered prior boxes for 384x384 resolution.
| Metric | Value |
|---|---|
| AP @ [ IoU=0.50:0.95 ] | 0.2975 (internal test set) |
| GFlops | 1.768 |
| MParams | 1.817 |
| Source framework | PyTorch* |
Average Precision (AP) is defined as an area under the precision/recall curve.
Image, name: image, shape: 1, 3, 384, 384 in the format B, C, H, W, where:
B- batch sizeC- number of channelsH- image heightW- image width
Expected color order is BGR.
The net outputs blob with shape: 1, 1, 200, 7 in the format 1, 1, N, 7, where N is the number of detected
bounding boxes. Each detection has the format [image_id, label, conf, x_min, y_min, x_max, y_max], where:
image_id- ID of the image in the batchlabel- predicted class ID (0 - person)conf- confidence for the predicted class- (
x_min,y_min) - coordinates of the top left bounding box corner - (
x_max,y_max) - coordinates of the bottom right bounding box corner
The OpenVINO Training Extensions provide a training pipeline, allowing to fine-tune the model on custom dataset.
The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:
- Object Detection C++ Demo
- Object Detection Python* Demo
- Pedestrian Tracker C++ Demo
- Social Distance C++ Demo
[*] Other names and brands may be claimed as the property of others.