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.

Specification

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: