person-attributes-recognition-crossroad-0230

Use Case and High-Level Description

This model presents a person attributes classification algorithm analysis scenario. It produces probability of person attributions existing on the sample and a position of two point on sample, which can be used for color prob (like, color picker in graphical editors)

Examples

Specification

Metric	Value
Pedestrian pose	Standing person
Occlusion coverage	<20%
Min object width	80 pixels
Supported attributes	is_male, has_bag, has_backpack, has hat, has longsleeves, has longpants, has longhair, has coat_jacket
GFlops	0.174
MParams	0.735
Source framework	PyTorch*

Accuracy

Attribute	F1
is_male	0.91
has_bag	0.66
has_backpack	0.77
has_hat	0.64
has_longsleeves	0.21
has_longpants	0.83
has_longhair	0.83
has_coat_jacket	NA

Inputs

Image, name: 0, shape: 1, 3, 160, 80 in the format 1, C, H, W, where:

• C - number of channels

• H - image height

• W - image width

The expected color order is BGR.

Outputs

1. The net outputs a blob named 453 with shape: 1, 8, 1, 1 across eight attributes: [is_male, has_bag, has_backpack, has_hat, has_longsleeves, has_longpants, has_longhair, has_coat_jacket]. Value > 0.5 means that an attribute is present.

2. The net outputs a blob named 456 with shape: 1, 2, 1, 1. It is location of point with top color.

3. The net outputs a blob named 459 with shape: 1, 2, 1, 1. It is location of point with bottom color.

Demo usage

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:

• Crossroad Camera C++ Demo

Legal Information

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