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.