Use Case and High-Level Description¶
This is a reimplemented and retrained version of the YOLO v2 object detection network trained with the VOC2012 training dataset. Network weight pruning is applied to sparsify convolution layers (35% of network parameters are set to zeros).
Mean Average Precision (mAP)
For Average Precision metric description, see The PASCAL Visual Object Classes (VOC) Challenge. Tested on the VOC 2012 validation dataset.
1, 416, 416, 3 in the format
B, H, W, C, where:
B- batch size
H- image height
W- image width
C- number of channels
Expected color order is
The net outputs a blob with the shape
1, 21125 which can be reshaped to
5, 25, 13, 13, where each number corresponds to [
num_anchors: number of anchor boxes, each spatial location specified by
x_lochas five anchors
cls_reg_obj_params: parameters for classification and regression. The values are made up of the following:
Regression parameters (4)
Objectness score (1)
Class score (20), mapping to class names provided by
x_loc: spatial location of each grid
The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:
[*] Same as the original implementation.
[**] Other names and brands may be claimed as the property of others.