With hundreds of ready-to-use applications in our Ecosystem, you get a vital summarization of datasets, models, tasks, action performance in a structured and easy to understand way.
More, good visualization helps you find hidden insights. Discover critical mistakes early and avoid negative long-term consequences. And, because many of our applications are interactive, you can immediately fix problems and get one step closer to the perfect results.
Because Supervisely is built like OS for computer vision, we made possible integration of the best machine learning models and tools on a single platform.
You will find a well-known projects from data science community, as well as our own Apps, providing a complete solution for entire AI development pipeline.
Configure every aspect of training from target classes to online augmentations, monitor metrics and terminal logs in real-time.
Understand how your model works on ground truth and new data and find how to correct negative output and increase performance.
Put pre-trained or custom neural network models to use in labeling interfaces to archive extraordinary results.
Generate synthetic datasets that drastically improve model results, especially when there is not enough ground truth.
With so many things you can on Supervisely, it's essential to have a clear and streamlined way to transform existing information on the platform into meaningful reports and charts. Luckily, Supervisely has all the right tools:
Visualizations you have seen can be presented in a form of static reports — a great tool to summarize information and give you a better understanding of what's going on.
But it becomes a much more useful instrument, when you add communication loop into this formula. Now, with the help of interactive widgets, we can build complex tools that significantly boost performance in many tasks across the platform.
Confusion matrix for selected classes provides a descriptive information about false positive outputs: say, we see that there are 11 images on which birds were detected as dogs.
Next, we can click on the cell and see those images:
Now, if we select an image, we can clearly see the error. We can immediately open the image in labeling tool and fix the mistake or create a new issue in issue tracker.
There is more numbers and dimensions you can explore, such as per-classes metrics. Let's select "chair" class and sort Table widget to find images with the largest amount of chairs:
Or see overall picture with per-image metrics:
Constantly growing ecosystem of hundreds of ready-to-use apps that extend and add new functionality.Learn about Ecosystem
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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.
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