yolo-v2-ava-sparse-35-0001

## 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).

## Specification

Metric Value
Mean Average Precision (mAP) 63.71%
Flops 48.29Bn*
Source framework TensorFlow*

For Average Precision metric description, see The PASCAL Visual Object Classes (VOC) Challenge. Tested on the VOC 2012 validation dataset.

## Inputs

Name: input, shape: [1x3x416x416] - An input image in the format [BxCxHxW], where:

• B - batch size
• C - number of channels
• H - image height
• W - image width Expected color order is BGR.

## Outputs

1The 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, cls_reg_obj_params, y_loc, x_loc] respectively:

• num_anchors: number of anchor boxes, each spatial location specified by y_loc and x_loc has 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)
• y_loc and x_loc: spatial location of each grid

## Legal Information

[*] Same as the original implementation.

[**] Other names and brands may be claimed as the property of others.