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 channels

• H - image height

• W - image width

Expected color order: BGR.

Outputs

1. Name: color, shape: 1, 7 - probabilities across seven color classes [white, gray, yellow, red, green, blue, black]

2. 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:

• Security Barrier Camera C++ Demo

Legal Information

[*] Other names and brands may be claimed as the property of others.