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.

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.

This example shows how to automate ground truth labeling.

To run:

1. Open MATLAB project YOLOv2ObjectDetection.prj
2. Open and run Part01_DataPreparation.mlx

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

To run:

1. Open MATLAB project YOLOv2ObjectDetection.prj
2. Open and run Part02_Modeling.mlx

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

To run:

1. Open MATLAB project YOLOv2ObjectDetection.prj
2. Open and run Part03_Deployment.mlx

Requires

• MATLAB R2020b
• Deep Learning Toolbox
• Image Processing Toolbox
• Computer Vision Toolbox
• Parallel Computing Toolbox
• MATLAB Coder
• GPU Coder

Download a free MATLAB trial for Deep Learning

Copyright 2020 The MathWorks, Inc.