This is a person detector for the ASL Recognition scenario. It is based on ShuffleNetV2-like backbone that includes depth-wise convolutions to reduce the amount of computation for the 3x3 convolution block and FCOS head.
Metric | Value |
---|---|
Persons AP on MS-COCO* | 77.68% |
Minimal person height | 100 pixel |
GFlops | 0.986 |
MParams | 1.338 |
Source framework | PyTorch* |
Average Precision (AP) is defined as an area under the precision/recall curve.
Name: input
, shape: [1x3x320x320] - An input image in the format [1xCxHxW], where:
Expected color order is BGR.
The net outputs blob with shape: [N, 5], where N is the number of detected bounding boxes. For each detection, the description has the format: [x_min
, y_min
, x_max
, y_max
, conf
]
x_min
, y_min
) - coordinates of the top left bounding box cornerx_max
, y_max
) - coordinates of the bottom right bounding box corner.conf
- confidence for the predicted class[*] Other names and brands may be claimed as the property of others.