How to Achieve Data Annotation Accuracy through Clear Guidelines

Artificial intelligence (AI) and machine learning (ML) applications are prompting the need for massive amounts of data to train AI and ML solutions. A large part of a model’s accuracy is determined by the data’s:

• Quality
• Accuracy
• Relevancy

While the first step to achieving accuracy with your model is high-quality data, the question is, how do you ensure data labeling accuracy from the very start?

Whether you opt for working with an outsourced team, or you currently work with an in house data labeling team, communication between you and your data annotators is a vital part of getting your data labeled accurately.

Data labeling guidelines are often ignored, brushed aside, or overlooked. So it’s not always thought that something as fundamental as a high-quality guideline could pave the way to high-quality data. A well-documented guideline with clear and concise instructions makes all the difference.

Why?

It all boils down to your accuracy rate. While you prep your data labeling team for the proper methods to label your data. Firstly, you would need to prepare a data labeling guide that’s as clear and concise as it possibly can be. In ensuring the quality and accuracy of training data, the rate of error is bound to decline if your labellers know exactly what to do, even in your absence.

Additionally, it improves data labeling workflows, giving you a more seamless process for your overall process, reducing back and forth between you and your labeling team. The arduous data labeling process can often be laced with edge cases and subjectivity, especially if you’re working with camera captured images for your computer vision model.

Prepare a data labeling guide

Our users have reported improved annotation quality when using the templates below to create instructions. However, you’re free to adapt the template contents to suit the needs of your project.

How to use the sections of the template effectively:

• Objectives provides a concise overview of the task to provide context.
• General Rules covers general guidelines for how you want your labels to be drawn.
• Label Overview allows our labellers to review your labels quickly.
• Labels specifies how you expect each label to be labelled in detail.
• Edge Cases clarifies ambiguous scenarios which may crop up in your tasks.
• Common Mistakes helps our labellers understand common errors and avoid them.

Tips for writing instructions

1. Add as many images and examples as possible.
2. Include examples of what doesn't need be annotated.
3. Clarify the differences between labels that are similar.
4. Be patient. It often takes several iterations to create good instructions, as every ML project is different.
5. Adjust your instructions based on where labellers make mistakes.

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To learn more, download our instructions template.