README.md

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%
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:

• Object Detection C++ Demo
• Object Detection Python* Demo
• Pedestrian Tracker C++ Demo

Legal Information

[*] Same as the original implementation.

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