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)



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*


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


  1. name: input , shape: [1x3x160x80] - An input image in following format [1xCxHxW], where
    - C - number of channels
        - H - image height
        - W - image width
    The expected color order is BGR.


  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.

Legal Information

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