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 |
name: input
, shape: [1x3x160x80] - An input image in the format [1xCxHxW], where
The expected color order is BGR.
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.[*] Other names and brands may be claimed as the property of others.