From the course: Data-Centric Visual AI
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Outlier detection and data distribution
From the course: Data-Centric Visual AI
Outlier detection and data distribution
- [Instructor] In this notebook, we're going to learn how we can find outliers in our visual AI datasets. Welcome to the second notebook for the course as we're going to learn how to find outliers in our visual AI datasets. We'll be using the open-source Python library, FiftyOne, to help us debug our dataset. And we're going to be using the Berkeley DeepDrive Dataset, which is a large self-driving car dataset to help find the outliers in this dataset. I know this one personally, and I know there's a lot of good examples in here for us to learn about how we can find these outliers and how outliers can come in many shapes and sizes. Right off the bat, in order to run this demo, you're going to have to use FiftyOne and umap learn with a pip install. If you're running this demo from the Colab Notebook, this should work all fine. You'll be able to run perfectly, but I highly encourage you that if you want to download the notebook and run it locally, that is just as good as fine. There's no…
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