Release Notes Oct 2021
Our commitment at Innotescus is to always do what’s best for our customers. As a result, we are always implementing user-oriented solutions on our platform. We owe a great deal to companies that have pushed us to keep innovating and making fixes for them. As we help more and more teams find data-centric solutions to their ML problems, we’ve increased our arsenal with new and improved features.
Our latest releases include…
What’s inside this article:
New Import & Export Formats
Innotescus is leading the pack by offering an always-growing list of import and export formats. Easily import annotations to images, with or without metadata, using the Innotescus .json format. Then easily export into a growing list of open-source and proprietary formats, now including YOLO Darknet and YOLO Keras. In particular, users can now import and export to the KITTI format, as well as automatically configure metadata fields that are compatible with the KITTI format.
Improved Analytics and Navigation
Context is key to simple navigation. That’s why now you can find breadcrumbs at the top of every project, task, and dataset details page, as well as a refreshed dataset details page! We’ve also added new options and metrics for Dive analytics to give you more ways to explore your data. Want a more granular look at team performance? View project member activity with per-instance activity charts with a .csv export option.
Here at Innotescus, we’re driven to make frequent improvements to our platform. Part of that drive comes from the satisfaction of responding to our users’ needs with features that go above and beyond to help them produce excellent work. If you have an idea for something you’d like to see in the platform, please contact firstname.lastname@example.org
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We are a group of scientists, engineers, and entrepreneurs with a vision for better AI. With backgrounds primarily in Machine Learning and Computer Vision, the Innotescus team understands the importance of having full control over and insight into data used to train Machine Learning models.
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