Use Case and High-Level Description
This model presents a vehicle attributes classification algorithm for a traffic analysis scenario.
Example
Specification
| Metric | Value |
|---|---|
| Car pose | Front facing cars |
| Occlusion coverage | <50% |
| Min object width | 72 pixels |
| Supported colors | White, gray, yellow, red, green, blue, black |
| Supported types | Car, van, truck, bus |
| GFlops | 0.462 |
| MParams | 11.177 |
| Source framework | PyTorch* |
Accuracy
Color accuracy, %
| Color | Accuracy |
|---|---|
| white | 84.20% |
| gray | 77.47% |
| yellow | 61.50% |
| red | 94.65% |
| green | 81.82% |
| blue | 82.49% |
| black | 96.84% |
Color average accuracy: 82.71%
Type accuracy, %
| Type | Accuracy |
|---|---|
| car | 97.44% |
| van | 86.41% |
| truck | 96.95% |
| bus | 68.57% |
Type average accuracy: 87.34%
Performance
Inputs
Name: input , shape: [1x3x72x72] - an input image in following format [1xCxHxW], where:
- C - number of channels
- H - image height
- W - image width
Expected color order: BGR.
Outputs
- Name:
color, shape: [1, 7] - probabilities across seven color classes [white,gray,yellow,red,green,blue,black] - Name:
type, shape: [1, 4] - probabilities across four type classes [car,van,truck,bus]
Legal Information
[*] Other names and brands may be claimed as the property of others.