# yolo-v2-tiny-vehicle-detection-0001¶

## Use Case and High-Level Description¶

This is a YOLO v2 Tiny network fine-tuned for vehicle detection for the “Barrier” use case.

Tiny Yolo V2 is a real-time object detection model implemented with Keras* from this repository and converted to TensorFlow* framework.

This model was pre-trained on Common Objects in Context (COCO) dataset with 80 classes and then fine-tuned for vehicle detection.

Metric

Value

Type

Detection

mAP

88.64%

coco_precision

94.97%

GFLOPs

5.424

MParams

11.229

Source framework

Keras*

## Input¶

Image, name - image_input, shape - 1, 416, 416, 3, format is B, H, W, C, where:

• B - batch size

• H - height

• W - width

• C - channel

Channel order is BGR.

## Output¶

The array of detection summary info, name - predict_conv/BiasAdd/YoloRegion, shape - 1, 71825, which could be reshaped to 1, 425, 13, 13 with format B, N\*85, Cx, Cy, where:

• B - batch size

• N - number of detection boxes for cell

• Cx, Cy - cell index

Detection box has format [x, y, h, w, box_score, class_no_1, …, class_no_80], where:

• (x, y) - coordinates of box center relative to the cell

• h, w - raw height and width of box, apply exponential function and multiply with corresponding anchors to get height and width values relative to the cell

• box_score - confidence of detection box in [0, 1] range

• class_no_1,…, class_no_80 - probability distribution over the classes in the [0, 1] range, multiply by confidence value to get confidence of each class

The anchor values are 0.57273,0.677385, 1.87446,2.06253, 3.33843,5.47434, 7.88282,3.52778, 9.77052,9.16828.

## Demo usage¶

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