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
Citation pour cette source
David Willingham (2024). Object Detection Using YOLO v2 Deep-Learning (https://github.com/matlab-deep-learning/Object-Detection-Using-YOLO-v2-Deep-Learning), GitHub. Récupéré le .
Compatibilité avec les versions de MATLAB
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Remerciements
A inspiré : Detection of ships on satellite images using YOLO v2
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Version | Publié le | Notes de version | |
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