This is a YOLO v2 Tiny network finetuned 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 pretrained on COCO* dataset with 80 classes and then finetuned for vehicle detection.
Metric | Value |
---|---|
Type | Detection |
mAP | 88.64% |
coco_precision | 94.97% |
GFLOPs | 5.424 |
MParams | 11.229 |
Source framework | Keras* |
Image, name - image_input
, shape - 1,3,416,416
, format is B,C,H,W
where:
B
- batch sizeC
- channelH
- heightW
- width
Channel order is BGR
.
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 sizeN
- number of detection boxes for cellCx
,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 cellbox_score
- confidence of detection box in [0,1] rangeclass_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
.
The original model is distributed under the following license:
MIT License
Copyright (c) 2019 david8862
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