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.


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*


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


  1. 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.


  1. 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.