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]

Legal Information

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