person-vehicle-bike-detection-2000#
Use Case and High-Level Description#
This is a person, vehicle, bike 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 256x256 resolution.
Example#
Specification#
Metric |
Value |
---|---|
AP @ [ IoU=0.50:0.95 ] |
0.1647 (internal test set) |
GFlops |
0.787 |
MParams |
1.821 |
Source framework |
PyTorch* |
Average Precision (AP) is defined as an area under the precision/recall curve.
Inputs#
Image, name: image
, shape: 1, 3, 256, 256
in the format B, C, H, W
, where:
B
- batch sizeC
- number of channelsH
- image heightW
- image width
Expected color order is BGR
.
Outputs#
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 - vehicle, 1 - person, 2 - 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
Training Pipeline#
The OpenVINO Training Extensions provide a training pipeline, allowing to fine-tune the model on custom dataset.
Demo usage#
The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:
Legal Information#
[*] Other names and brands may be claimed as the property of others.