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: input, shape: 1, 3, 416, 416 in the format B, C, H, W, where:

  • B - batch size
  • C - number of channels
  • H - image height
  • W - image width

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

Legal Information

[*] Same as the original implementation.

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