person-attributes-recognition-crossroad-0234

Use Case and High-Level Description

This model presents a person attributes classification algorithm analysis scenario. The model consists of the ResNet-50 backbone and a head. For an input image with a pedestrian the model returns 7 values that are probabilities of the corresponding 7 attributes.

Specification

Metric

Value

Pedestrian pose

Standing person

Occlusion coverage

<20%

Min object width

80 pixels

Supported attributes

is_male, has_bag, has_hat, has_longsleeves, has_longpants, has_longhair, has_coat_jacket

GFlops

2.167

MParams

23.510

Source framework

PyTorch*

Accuracy

Attribute

F1

is_male

0.92

has_bag

0.44

has_hat

0.74

has_longsleeves

0.45

has_longpants

0.89

has_longhair

0.84

has_coat_jacket

NA

Inputs

Image, name: input, 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

The net output is a blob named attributes with shape 1, 7 across seven attributes: [is_male, has_bag, has_hat, has_longsleeves, has_longpants, has_longhair, has_coat_jacket]. Value > 0.5 means that the corresponding attribute is present.

Demo usage

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