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* | 80.0% |
| 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:
- C - number of channels
- H - image height
- W - image width
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 corner - (
x_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.