yolo-v2-tiny-ava-sparse-30-0001#
Use Case and High-Level Description#
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 (30% of network parameters are set to zeros).
Specification#
Metric |
Value |
---|---|
Mean Average Precision (mAP) |
36.33% |
GFlops |
6.9949 |
MParams |
15.8587 |
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 sizeH
- image heightW
- image widthC
- 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 byy_loc
andx_loc
has five anchorscls_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
andx_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:
Legal Information#
[*] Same as the original implementation.
[**] Other names and brands may be claimed as the property of others.