vehicle-attributes-recognition-barrier-0039#
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, bus, truck, van |
GFlops |
0.126 |
MParams |
0.626 |
Source framework |
Caffe* |
Accuracy - Confusion Matrix#
Color accuracy, %#
blue |
gray |
yellow |
green |
black |
white |
red |
|
---|---|---|---|---|---|---|---|
blue |
79.53 |
4.32 |
0.62 |
6.41 |
6.54 |
2.47 |
0.12 |
gray |
2.53 |
78.01 |
0 |
1.36 |
1.18 |
16.74 |
0.18 |
yellow |
0 |
13.9 |
54.01 |
11.21 |
0 |
10.7 |
10.16 |
green |
3.79 |
1.52 |
1.52 |
83.33 |
6.06 |
3.03 |
0.76 |
black |
0.85 |
1.92 |
0 |
0.32 |
96.1 |
0.74 |
0.07 |
white |
1.45 |
10.86 |
0.17 |
2.53 |
0.08 |
84.83 |
0.08 |
red |
0.89 |
0.3 |
2.18 |
2.18 |
0.3 |
1.88 |
92.27 |
Color average accuracy: 81.15 %
Type accuracy, %#
car |
van |
truck |
bus |
|
---|---|---|---|---|
car |
98.26 |
0.56 |
0.98 |
0.2 |
van |
3.72 |
89.16 |
6.15 |
0.97 |
track |
1.71 |
2.46 |
94.27 |
1.56 |
bus |
7.94 |
3.8 |
19.69 |
68.57 |
Type average accuracy: 87.56 %
Inputs#
Image, name: input
, shape: 1, 3, 72, 72
in the format 1, C, H, W
, where:
C
- number of channelsH
- image heightW
- image width
Expected color order: BGR
.
Outputs#
name:
color
, shape:1, 7, 1, 1
- Softmax output across seven color classes [white, gray, yellow, red, green, blue, black]name:
type
, shape:1, 4, 1, 1
- Softmax output across four type classes [car, bus, truck, van]
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.