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How To Define Quality Data Labeling

Machine learning applications present an enormous opportunity to drive business value, but even the most sophisticated models won’t perform well if they’re not trained on high-quality data. The path to a high-quality, scalable data labeling pipeline starts with a deep understanding of your business requirements, allowing you to develop well-defined annotation criteria against which to measure quality. 

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Training data quality is an evaluation of a data set’s fitness to serve its purpose in a given ML use case. Your requirements will be driven by the use case, and you will need to evaluate the quality of your data annotation over multiple dimensions, including completeness, exactness, and accuracy. But before you can measure quality, you need to establish an unambiguous set of rules that describe what “quality” means in the context of your project. 

There are 3 key dimensions to consider when defining your quality requirements:

  1. Localization accuracy
  2. Occlusion rules
  3. Object classification criteria

Our Data Science Team put together a short guide to help you establish an unambiguous set of rules that describe what “quality” means in the context of your project. Download a complimentary copy of this guide to upgrade your quality.

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