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:
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.
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.
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:
Tips for writing instructions
To learn more, download our instructions template.
Everything from uploading data to seeing it labeled in real time was really cool. This is just way simpler to use compared to Amazon Sagemaker and LabelBox. I was also very impressed with how the platform delivered exactly what we needed in terms of label quality.
I was also able to view the labels as they were being generated, which gave me quick feedback about the label quality, rather than waiting for the whole batch. This replaced my standard manual QA process using external tools like Voxel's Fiftyone, as the labels were clear and easy to parse through in real-time.