Object Detection Using YOLO v2 Deep-Learning

This demo shows the full deep learning workflow for an example using image data in MATLAB.

In it we use deep learning based object detection using Yolo v2 to identify vehicles of interest in a scene.

We show examples on how to perform the following parts of the Deep Learning workflow:

Part1 - Data Preparation
Part2 - Modeling
Part3 - Deployment

For more details, please refer to the documentation article Getting Started with YOLO v2:
https://www.mathworks.com/help/vision/ug/getting-started-with-yolo-v2.html

This demo is implemented as a MATLAB project and will require you to open the project to run it. The project will manage all paths and shortcuts you need. There is also a significant data copy required the first time you run the project.

Part 1 - Data Preparation
This example shows how to automate ground truth labeling.

To run:
Open MATLAB project YOLOv2ObjectDetection.prj
Open and run Part01_DataPreparation.mlx

Part 2 - Modeling
This example shows how to train a you only look once (YOLO) v2 object detector.

To run:
Open MATLAB project YOLOv2ObjectDetection.prj
Open and run Part02_Modeling.mlx

Part 3 - Deployment
This example shows how to generate CUDA® MEX for a you only look once (YOLO) v2 object detector.

To run:
Open MATLAB project YOLOv2ObjectDetection.prj
Open and run Part03_Deployment.mlx