Custom labeling UI
Tailored annotation solutions:
just as you need them
From universal labeling tools that work great for every job, to customized tools for your very problem.
Trusted by Fortune 500. Used by 80,000+ companies and researchers worldwide
Universal labeling tools
Variety of annotation interfaces
The core component of Supervisely has always been feature-rich labeling suites that solve numerous annotation tasks for diverse types of data, from images to 3D sensor fusion point clouds.
More than that, we introduced AI elements, such as Interactive AI assisted tools and AI-powered applications to streamline labeling even further.
Overview
Clothing
Wardrobe
Single person
Multiple people
Plant
Fruits
Animals
Person
Room
Busy street
Manual correction
Customized labeling tools
Next step: fully customized annotation
with Supervisely Apps
While those methods work perfectly great for many of our customers, there is always a way for improvement, especially when it comes to less common tasks and areas.
Medical images with unusual formats, complex labeling pipelines from dozens of CCTV at once, multiple teams that require non-standard approach to labeling process organization — it's impossible to create one solution that would cover all possible scenarios.
Luckily, in Supervisely it's possible to build custom interfaces for any task without worrying of deployment, integration, format conversion and other boring things. Like Docker and Heroku simplified and standardized those questions, Supervisely Apps are doing the same for computer vision.
Use case: action recognition
The task of labeling a video sequence with tags that describe actions and context is not an uncommon one. Supervisely comes with feature-rich video labeling suite that has video timeline, sequence tags any many more options for professional labeling.
But, one day we got a feedback from our customer:
- User interface is too complicated for new labelers that jump on the project with every day, thus, slowing down the introduction
- A very specific features are needed, such as re-play of video sequences formed by intersection or union of a specific set of tags
- Finally, the review process is much more complicated than usual which requires a dedicated report application
Usually, it's a huge problem for some products that forces users to abandon it and switch to development of a custom in-house solution, tailored for their needs.
Luckily, this is not the case with Supervisely: built as OS for computer vision, we can efficiently create built-in interactive applications by using App Engine. As the result, we developed a dedicated Supervisely App with functionality that would perfectly fit our customer needs.
Use case: retail labeling
In another case the task was tagging again, this time of store shelves. The hard part is that each image has about 50-80 items that need to be labeled with a bounding box and assign an appropriate ID from the customer database of 10,000+ items.
While we could still use general-purpose image labeling tool, there are a number issues:
- The general tag select input is not suitable for hierarchical catalog of thousands of possible items
- There is no reference image so that labeler can confirm that they places the correct tag
- An AI module could have drastically improve labeling performance by providing suggestions of the most fitting items for the selected part of an image
With the feedback above we were managed to reduce labeling time up to 20x times while maintaining the highest accuracy by introducing a set of applications that extend existing annotation UI.
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> downloading pre-requirements...
> pulling docker images...
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> Done! Supervisely is running on port :80
supervisely update
> checking for updates...
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Here’s why our customers trust us
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.
7
years
Supervisely provides first-rate experience since 2017, longer than most of computer vision platforms over there.
80,000+
users
Join community of thousands computer vision enthusiasts and companies of every size that use Supervisely every day.
1,000,000,000+
labels
Our online version has over a 220 million of images and over a billion of labels created by our great community.
Trusted by Fortune 500. Used by 80,000+ companies and researchers worldwide
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