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, whiches 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 |
- C - number of channels - H - image height - W - image width. The expected color order is BGR.
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.[*] Other names and brands may be claimed as the property of others.