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

name: input , shape: [1x3x160x80] - An input image in the format [1xCxHxW], 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.

Legal Information

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