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 |
Link to performance table
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name: "input" , shape: [1x3x1024x1024] - An input image in the format [BxCxHxW], where:
- B - batch size
- C - number of channels
- H - image height
- W - image width
The expected color order is BGR.
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The net outputs a blob with shape: [1, 1, N, 7], where N is the number of detected bounding boxes. For each detection, the description 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
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