This is a reimplemented and retrained version of the tiny YOLO v2 object detection network trained with the VOC2012 training dataset. Network weight pruning is applied to sparsify convolution layers (60% of network parameters are set to zeros).
|Mean Average Precision (mAP)||35.32%|
For Average Precision metric description, see The PASCAL Visual Object Classes (VOC) Challenge. Tested on the VOC 2012 validation dataset.
input, shape: [1x3x416x416] - An input image in the format [BxCxHxW], where:
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:
x_loc: spatial location of each grid
[*] Same as the original implementation.
[**] Other names and brands may be claimed as the property of others.