OAK-D deploy tool This annotation platform is so much trustworthy for development of computer vision on the edge tasks. Actually I'm working on deploying a detection model on the LUXONIS OAK-D, and has been a magnificent tool.

One of the best integrated annotation workflows I've used I am a scientific researcher in the biomedical imaging community, and I can say first-hand that the SuperAnnotate platform addresses a significant pain point for computer vision researchers. Obtaining expert annotations is often one of the first steps for a medical imaging project. Previously, I had used different open-source or commercial tools, but I often had to cobble together more than one piece of software to get a complete annotation pipeline. This was the first platform I've liked that includes an integrated, streamlined workflow for easy-to-use annotation (with AI augmentation), quality control, as well as neural network training. The team is responsive to feedback to improve the platform, and there are new features that I have yet to try but I am very pleased with the service as is. Note: I have also left a similar review on G2.

I think they are the fastest to date. In terms of speed, SuperAnnotate beats out most of the competitors in the space. I love their automatic segmentation tool.

Convenient, easy to capture edges, cool workflow. SuperAnnotate is very adaptable. All the tools are very reliable and helps a project run smoothly. I love the frequent updates the comfortable shift to a new UI or feature immersion. Their smart segmentation work is awesome and reduces work for semantic-tier labeling more than 50% by my account.

Great team, great product, great support We needed an experienced team with great software to help us develop a new pose detection algorithm. We needed to find the right model for our project which required experimentation and iteration with the annotation team. The Superannotate team has the right team at the right price. Their software, customer support and annotators did a great job of executing on our model.