From the course: Data-Centric Visual AI
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Understanding annotations
From the course: Data-Centric Visual AI
Understanding annotations
- [Instructor] Before we jump ahead, let's take a second to understand the different type of annotations you might encounter in Visual AI. There are many different tasks in the Visual AI space and in the next few modules we'll go in through how to clean and curate these data sets. But before we can hop into that, let's understand the different forms data sets can come in. Visual AI has many popular tasks like classification, detection, segmentation and more. And all these data sets may come in different shapes and sizes, but one thing that comes as true to most of them is that they all have different forms of annotations on them. These annotations, commonly referred to as the ground truth of the data set is what helps you define what the model should be learning. The ground truth is the pinnacle of learning for your model, and if it was to capture 100% accuracy, that would mean that the ground truth and the model's predictions are exactly the same. When loading in your data sets it is…
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