Every use-case is different. So why do we still use pre-trained models to make labelling more efficient? Our models learn from your bounding box annotations, allowing us to get you usable results fast, while provding you with unparalled 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 strenghts 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 create more and more data, the suggestions will get better and better. Soon, you’ll find yourself annotating complex images in seconds.
It doesn’t matter that you can create bounding boxes 10x faster if you can’t be sure of the quality. We know that. That’s why we have reinvented the way QA is done in annotation.
With our manual review tool you can see, and filter, every annotation made in your project. No more late nights looking at images trying to figure out what’s wrong.
“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.”