Use Case and High-Level Description
Pedestrian and vehicle detection network based on MobileNet v1.0 + SSD.
Example
Specification
| Metric | Value |
|---|---|
| AP for pedestrians | 88% |
| AP for vehicles | 90% |
| Target pedestrian size | 60x120 pixels |
| Target vehicle size | 40x30 pixels |
| GFLOPS | 3.974 |
| MParams | 1.650 |
| Source framework | Caffe* |
Average Precision (AP) metric is described in: Mark Everingham et al. The PASCAL Visual Object Classes (VOC) Challenge.
Tested on challenging internal datasets with 1001 pedestrian and 12585 vehicles to detect.
Performance
Inputs
Name: input, shape: [1x3x384x672] - An input image in the format [BxCxHxW], where:
- B - batch size
- C - number of channels
- H - image height
- W - image width Expected color order is BGR.
Outputs
The net outputs blob with shape: [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 IDconf- 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.
Legal Information
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