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, 3, 416, 416, format is B, C, H, W, where:

  • B - batch size

  • C - channel

  • H - height

  • W - width

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.

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, 3, 416, 416, format is B, C, H, W, where:

  • B - batch size

  • C - channel

  • H - height

  • W - width

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.

Legal Information

The original model is distributed under the following license :

MIT License

Copyright (c) 2019 david8862

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

[*] Other names and brands may be claimed as the property of others.