# yolo-v2-ava-sparse-70-0001¶

## 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 (70% of network parameters are set to zeros).

## Specification¶

Metric

Value

Mean Average Precision (mAP)

62.9%

GFlops

29.4205

MParams

50.6451

Source framework

TensorFlow*

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

## Inputs¶

Image, name: data, shape: 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 BGR.

## Outputs¶

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, 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), mapping to class names provided by <omz_dir>/data/dataset_classes/voc_20cl.txt file.

• y_loc and x_loc : spatial location of each grid

## Demo usage¶

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:
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