Explore constantly growing ecosystem of hundreds of ready-to-use Supervisely Apps and make your contribution.
Supervisely Apps are interactive web-widgets that add new functionality to any part of the platform.
Built with 
Python backend and 
VueJs frontend, apps are very easy for development and can tailor even the most complex or custom workflows.
Supervisely Apps can be deeply integrated into the core of the platform, hide in a context menu a dataset, be a part of a labeling toolbox, or run as a stand-alone dashboard.
That creates an incredible synergy between core features of Supervisely and variety of features a labeler, manager, data engineer or scientists may need.
Computer vision stands on the shoulders of academia — new open-source projects like state-of-the-art models and machine learning tools can dramatically improve your day-by-day workflow.
Unfortunately, there are some extra steps you need to make before you can start using those tools:
Often you need to install additional software and libraries, provide configs and read long readmes before you can even try it.
Usually those projects are built by researcher for researchers and are not intended to be used by your manager — unless they know how to run it in terminal, of course.
Unless two projects are made by the same people, you cannot use output of a tool in another tool, say, train model and visualize its weights without wasting your time on format conversion.
Open-source projects integrated as Supervisely Apps are ready to go: just pick the tool you need form the ecosystem and run it straight away.
Now everyone: you, your colleagues, your manager, even your grand-ma can configure and run complex tools that previously could require you editing .jsons and python knowledge.
Maybe, the most important benefit of Supervisely Apps: since every tool now takes as an input and outputs results in the same format, you can easily build end-to-end pipelines such as data preparation, argumentation and training shockingly easy.
Here are some of the recent examples:
Object Detection Metrics
All our Supervisely Apps are open-sourced and publicly available on GitHub for everyone to explore, fork and develop new apps.
Need a private app for your private task? No problem, our Enterprise Edition supports closed Git repositories so you can develop and host it in secret.
And by the way, we can develop a private app for your task to tailor Supervisely for even the most unique workflow.
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We use Supervisely since 2019. The key advantage of this tool is that Supervisely provides a complete data treatment pipeline. An important advantage is that a Supervisely instance can be deployed autonomously on a Client infrastructure, and distributed on different servers.
It helps to treat enterprise’s internal and often confidential data in a secured way. Together with a user-friendly interface, a clear documentation and a friendly and reactive support team it helps us to do Data Scientist work better and faster.
BMW Group is using the Supervise.ly solution to create automated verifications for ensuring a very high product quality across the whole production chain in vehicle and vehicle component manufacturing.
BMW Group uses Supervise.ly to annotate manufacturing images from production lines in their world-wide plants for enhancing quality inspections using deep learning. The Supervise.ly tooling also supports the process for continuously updating AI models using semi-automated labeling.
Supervise.ly is integrated into the BMW Group AI Platform in order to empower computer vision based AI use cases.
We originally set out to look for tools that could help us with data annotation, and we discovered that Supervisely excels at that and much more. It has become an integral part of our workflow in annotation, model training, and evaluation.
We've been exceedingly impressed with the customer support, addition of new features, and the flexibility of the publicly available SDK/API. The Supervisely team has also been fast to respond to support questions, and has shown a lot of openness when given feedback on potential improvements.
We have been using Supervisely for a few years now to help label and organize our data for AI training. The interface is user-friendly and the tools are intuitive to use, which has made the annotation process much more efficient for our team. We run Supervisely locally, which allows us to stay in control of our data. We also use Supervisely for annotation reviews, and the review tools have been invaluable in ensuring the quality and accuracy. The Python SDK has also been incredibly helpful in automating and streamlining our workflow. In addition, the support team on Slack has been extremely helpful and responsive. The ability to collaborate with my colleagues on the same project has also been a huge time-saver.
Overall, we have been extremely satisfied with Supervisely and would highly recommend it to anyone in need of a reliable and efficient annotation solution.
Supervisely provides first-rate experience since 2017, longer than most of computer vision platforms over there.
Join community of thousands computer vision enthusiasts and companies of every size that use Supervisely every day.
Our online version has over a 220 million of images and over a billion of labels created by our great community.
Speak with people who are on the same page with you. An actual data scientist will:
Get accurate training data on scale with expert annotators, ML-assisted tools, dedicated project manager and the leading labeling platform.
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