About this webcast
Originally broadcast on March 25, 2020
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Whether you are counting cars on a road or people stranded on rooftops in a natural disaster, there are plenty of use cases for object detection. Often times, pre-trained object detection models do not suit our needs and we need to create our own custom models.
How can we utilize machine learning to train our own custom model without substantive computing power and time? Answer: Watson Machine Learning.
How can we leverage our custom trained model to detect object’s, in real-time, with complete user privacy, all in the browser? Answer: TensorFlow.js.
In this webinar, you will learn how to build an app that lets you use your own custom-trained models to detect objects. You’ll create an IBM Cloud Object Storage instance to store your labeled data, then after your data is ready, you’ll learn how to start a Watson Machine Learning instance to train your own custom model on top-of-the-line GPUs. After your model has completed training, you can plug the model into your application.
At the end of this webinar, you should understand how to:
- Label data that can be used for object detection
- Use your custom data to train a model using Watson
Nicholas Bourdakos is a Developer Advocate at IBM based in New York. He is the creator of Cloud Annotations. Nicholas enjoys teaching people about Computer Vision.