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- C - number of channels
- H - image height
- W - image width
The expected color order is BGR.
- The net output is a blob named
attributeswith 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.