vehicle-attributes-recognition-barrier-0042¶
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%
Inputs¶
Image, name: input, shape: 1, 3, 72, 72 in format 1, C, H, W, where:
C- number of channelsH- image heightW- 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]
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.