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

_images/vehicle-attributes-recognition-barrier-0042-1.png

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]

Use Case and High-Level Description

This model presents a vehicle attributes classification algorithm for a traffic analysis scenario.

Example

_images/vehicle-attributes-recognition-barrier-0042-1.png

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.