In this video, Neha Goel and Connell D’Souza will go over the different steps required to prepare a dataset to be used in designing object detection deep learning models.
First, Neha demonstrates how to resize and randomly sample images to create three datasets for training, validation, and testing and discusses the importance of this step.
Next, the Ground truth Labeler app is discussed for data labeling. You can use the ground truth labeler app or Video Labeler app to automate data labeling using either built-in automation algorithms or custom automation algorithms. Once the ground truth has been generated, preparing this data for training neural network is also discussed.
Resources:
- How to Label Data for Deep Learning:
- How to Use Custom Automation Algorithms for Data Labeling:
- Designing Object Detectors in MATLAB: https://bit.ly/38I60Fo
Download the files used in this video: https://bit.ly/2RUTTht
Contact the MathWorks student competitions team: roboticsarena@mathworks.com
Visit the MATLAB and Simulink Robotics Arena for additional videos: https://bit.ly/2Rwf8qM
Request software for your student competition: https://bit.ly/315QGQj
Get a free product trial: https://goo.gl/ZHFb5u
Learn more about MATLAB: https://goo.gl/8QV7ZZ
Learn more about Simulink: https://goo.gl/nqnbLe
See what's new in MATLAB and Simulink: https://goo.gl/pgGtod
© 2019 The MathWorks, Inc. MATLAB and Simulink are registered
trademarks of The MathWorks, Inc.
See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.