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.
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 uses 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.
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.
Hasty.ai helped us improve our ML workflow by 40%, which is fantastic. It reduced our overall investment by 90% to get high-quality annotations and an initial model.