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

1. name: input , shape: [1x3x72x72] - An input image in following format [1xCxHxW], where:

   - C - number of channels
   - H - image height
   - W - image width
   

   Expected color order: BGR.

Outputs

1. name: "color", shape: [1, 7, 1, 1] - Softmax output across seven color classes [white, gray, yellow, red, green, blue, black]
2. name: "type", shape: [1, 4, 1, 1] - Softmax output across four type classes [car, bus, truck, van]

Legal Information

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