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) | 64.48% |
| AP people | 76.22% |
| AP vehicles | 74.66% |
| AP bikes | 42.56% |
| 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 |
name: "input" , shape: [1x3x1024x1024] - An input image in the format [BxCxHxW], where:
The expected color order is BGR.
image_id, label, conf, x_min, y_min, x_max, y_max], where:image_id - ID of the image in the batchlabel - predicted class IDconf - confidence for the predicted classx_min, y_min) - coordinates of the top left bounding box cornerx_max, y_max) - coordinates of the bottom right bounding box corner[*] Other names and brands may be claimed as the property of others.