Perform all the necessary actions on your data, from importing and converting to skeletonization of masks and rasterization.
Waste no time on dataset format converting
Make more data from existing content
Create enormous datasets from different sources
Save results in any format you need
Modify your assets into new forms
Waste no time on dataset format converting
Work with data type you require
Waste no time on dataset format converting
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.
Dashboard to configure and monitor training
Understand how your model works on ground truth and new data and find how to correct negative output and increase performance.
Detailed statistics for all classes in images project
Generate synthetic datasets that drastically improve model results, especially when there is not enough ground truth.
Generate synthetic data: flying foregrounds on top of backgrounds
Perform all the necessary actions on your data, from importing and converting to skeletonization of masks and rasterization.
Configure, preview and split images and annotations with sliding window
Training a neural network involves many steps like augmentations or visualizations, but it all starts with the data.
A clear and straightforward way to query your datasets and form new subsets is absolutely crucial for any model training.
More about Data ManagementSupervisely provides essential set of tools for data querying and management, including:
You noticed we have some of the best models and machine learning tools integrated to Supervisely and maybe thinking right now: why is better to use them as Supervisely Apps instead of cloning a GitHub repo and setting things up yourself?
More about AppsDon't waste dozen of hours figuring out how to install, configure and adapt another model from the internet — we did the hard part for you.
Thanks to streamlined way of building interfaces with Supervisely Apps, we can add GUI simplify interaction with integrated code.
Run automatically generated command in terminal to run Supervisely Agent and simply deploy new training tasks with a few clicks.
Since now those models and tools run in web and not on your personal PC, you can easily share and collaborate with your team members.
Project converted to App gets access to the whole Ecosystem: import from different formats, visualization, exploration and much more!
The way of training an AI is not always straightforward and has many boring steps along it. Converting datasets from multiple formats, merging them together, handling different object types, converting shapes, running augmentations and various transformations like sliding windows — we didn't even come to training yet!
Well, one can find all the tools needed across the internet and spend weeks finding how to run them, combine outputs together and hope everything works out.
Or, dive into our Ecosystem of ready-to-use machine learning tools integrated on a single platform with convenient use interface, team collaboration and, most important, seamlessly integrated unified by Supervisely!
Here are just some examples out of hundreds applications available, that already make life thousands of data scientists better every day:
Import images by URLs or create new datasets from already pre-uploaded images in Team Files. Optionally, provide additional information as Tags.
Provided by application:
Upload images using .CSV file
Convert classes to bitmap and rasterize objects without intersections
Provided by application:
Convert classes to bitmap and rasterize objects without intersections
App splits all images and their labels using sliding window approach. Play with sliding window configuration and preview results before start splitting. All results will be saved to a new project. All crops can be mered back with another Sliding window merge app.
Provided by application:Configure, preview and split images and annotations with sliding window
App merges images (and labels) that were split by Sliding window split app. Merge results are saved to a new project, information about input project is added as project metadata.
Provided by application:
Merge images and labels that were split by sliding window before
Convert image dataset to .mp4 downloadable video. Application key points: Images in dataset must be the same size (you can use Resize images); Customize FPS; Customize labels opacity and thickness
Provided by application:
Creates video from images in dataset with selected frame rate and configurable label opacity
The app crops objects of selected classes in images project and creates a new project. User can define some crop settings - for example, padding, resize and what to do with original labels - keep or ignore.
Provided by application:
Creates new project with cropped objects
Data augmentation is a vital step in neural networks training that can incredibly boost model performance.
This a great example of how different Supervisely Apps can benefit from each other and increase value of their sum.
Choose a dataset and augmentation Supervisely App
Visualize and build augmentation pipeline with ImgAug
Save as a new dataset
Dashboard to configure, start and monitor training
A fully customizable AI infrastructure, deployed on cloud or your servers with everything you love about Supervisely, plus advanced security, control, and support.
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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’ve been working together with Supervisely since 2020, and we have helped each other to grow rapidly and significantly.
Supervisely’s team has been incredibly fast and agile in taking on board our requirements and implementing useful, up-to-date computer vision functionalities. In addition, we appreciate the openness and scalability of their ecosystem combined with the Python SDK and API. So far, we have been very satisfied with the platform and the incredibly responsive team.
Working with Supervise.ly has significantly enhanced our capability to develop AI models for lung CT scans. What sets Supervise.ly apart is its exceptional support team who are really responsive and adapt to our unique needs with a range of apps and helper files.
Their team has developed updates driven by our specific user feedback, making Supervise.ly a critical component of our research ecosystem in generating the specific labels we need to provide AI-driven solutions. We are immensely grateful for their pivotal role in our work.
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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