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.
Image, name -
image_input, shape -
1, 3, 416, 416, format is
B, C, H, W, where:
B- batch size
Channel order is
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
Cy- cell index
Detection box has format [
y) - coordinates of box center relative to the cell
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_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.
The original model is distributed under the following license:
[*] Other names and brands may be claimed as the property of others.