How to control the quality of your Instance Segmentation annotations with an AI-powered QC feature offered by Hasty?

Instance Segmentation is a prevalent Computer Vision task, as it might help ease your pain across various industries and tasks. In short, Instance Segmentation techniques help Data Scientists find distinct instances on an image, classify, and segment them, producing class labels and pixel-perfect segmentation masks.

In general, one may say that Instance Segmentation can come in handy for anyone as it is widely used by businesses, governments, and other actors. Instance Segmentation real-life application fields include:

• Agriculture;

• Healthcare;

• Autonomous driving systems;

• Metallurgy;

• And many more.

However, nowadays, data annotation might be a bottleneck for AI startups as the conventional labeling approach is both costly and time-consuming. Hasty’s data-centric ML platform addresses the pain and automates 90% of the work needed to build and optimize your dataset for the most​ advanced use cases ​ with our self-learning assistants using AI to train AI.

Still, in the modern world, data quality is just as (if not more) important as data quantity. The conventional Quality Control process for annotations is expensive, slow, and time-consuming. So, we in Hasty addressed the problem and developed an AI-powered Quality Control feature - AI Consensus Scoring. With its help, you can streamline your Quality Control process and spare some time, money, and nerves.

Below is a video tutorial showing how to use, analyze, and interpret the AI-powered data Quality Control runs offered by Hasty. Want to make your annotation loop easier? Watch the video and see how we can help.

Further Resources

• How to streamline your Instance Segmentation labeling experience with Hasty.ai;

• How to streamline your Object Detection labeling experience with Hasty.ai.