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.