Person/Vehicle/Bike detector is based on SSD detection architecture, RMNet backbone, and learnable image downscale block (like person-vehicle-bike-detection-crossroad-0066, but with extra pooling). The model is intended for security surveillance applications and works in a variety of scenes and weather/lighting conditions.
| Metric | Value |
|---|---|
| Mean Average Precision (mAP) | 65.12% |
| AP people | 77.47% |
| AP vehicles | 74.94% |
| AP bikes | 44.14% |
| Max objects to detect | 200 |
| GFlops | 3.964 |
| MParams | 1.178 |
| Source framework | Caffe* |
Average Precision (AP) is defined as an area under the precision/recall curve.
Validation dataset consists of 34,757 images from various scenes and includes:
| Type of object | Number of bounding boxes |
|---|---|
| Vehicle | 229,503 |
| Pedestrian | 240,009 |
| Bike | 62,643 |
Similarly, training dataset has 160,297 images with:
| Type of object | Number of bounding boxes |
|---|---|
| Vehicle | 501,548 |
| Pedestrian | 706,786 |
| Bike | 55,692 |
Image, name: data, shape: 1, 3, 1024, 1024 in the format B, C, H, W, where:
B- batch sizeC- number of channelsH- image heightW- image width
The 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 (1- person, 2 - vehicle, 3 - bike)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 model can be used in the following demos provided by the Open Model Zoo to show its capabilities:
[*] Other names and brands may be claimed as the property of others.