Model Playground has now been released to open beta. Check out our announcement here.
Learn more

Bounding box annotation just got 10x faster

Use AI to do the hard work for you. With our human-in-the-loop approach, hours of annotation work become minutes by using our cloud-based annotation tool

Powered by Artificial Intelligence

Every use case is different. So why do we still use pre-trained models to make labeling more efficient? Our models learn from your bounding box annotations, allowing us to get you usable results fast while providing you with unparalleled accuracy. And the results get better with time. The more you label, the better our models.

The end result? You get more accurate object detection data in a fraction of the time.

Human-in-the-loop approach

We don’t believe that annotation can be 100% automated. There will always be edge-cases that need human attention. We believe in humans and machines working together, playing to the strengths of both. That’s why Hasty is the only annotation tool on the market with a human-in-the-loop approach.

Our models create bounding box suggestions. You accept the ones you like. You add what’s missing yourself. As you generate more and more data, the recommendations will get better and better. Soon, you’ll find yourself annotating complex images in seconds.

Automated labelling that labels 1000’s of images in minutes

With enough data, the need for a human diminishes. You label thousands of images. Our segmentation assistants are getting better and better. And you find yourself accepting all suggestions on image after image. Now it’s time to use our automated labeling feature, enabling you to batch-process all, or a portion, of your remaining data in one click.

Getting started is easy

Sign up and try for free or book a demo with one of our experts

Jenny Abrahamson

Software Engineer
at Audere

“Before discovering Hasty, labeling images was labor intensive, time-consuming, less accurate, and progression through the groundwork to build our AI detection model was much more frustrating. Hasty’s approach of training the model while labeling with faster annotate-test cycles has saved Audere countless hours. The speed and ease of use have allowed us to accelerate our mission to improve global health in the world’s most underserved communities.”