Manual segmentation of a complex shape can be ridiculously time-consuming. Our tool uses AI to produce high quality results with lower cost.
It does not matter if your object was never seen by our neural network — it’s class-agnostic
If needed, correct output by pointing where is background and where is an object
Your annotation is saved as a bitmap along with guiding input that can refined later at any time
Smart tool is class-agnostic — it was not trained to capture specific objects, but rather any forms that stand out.
What’s great about the neural network in the core of the smart tool is that it can be re-trained to better fit your very unique case and produce unprecedented results with just a little bit of extra data.
Neural network in the heart of the smart tool never saw a human — it fails to produce great segmentation at first.
Label a few examples of objects smart tool needs to predict better and run automated re-train application.
Now smart tool has re-trained and uses updated neural network that knows how humans look — and it produces precise segmentation.
The core technology behind the Smart Tools is stunning — but it becomes an absolute deal-breaker when we deploy the Smart Tool as part of our Ecosystem. Now, other Supervisely Apps can communicate with it and can build more complex solution, such as this Batch Smart Tools application:Learn about AI Ecosystem
Another substantial thing about neural networks is that it's easy to adapt it to different modalities. That means, that the Smart Tools not only work on images, but on sequential frames, such as videos or multi-slice medial imaging and even 3D point clouds with more than two dimensions!
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No need to trade speed for quality — smart tool can produce prediction almost immediately in response to your input!
While crucially increasing labeling speed on images, smart tool is priceless in segmentation of videos, DICOMs and 3D point clouds.
In many cases smart tool can outperform human labeler not only in terms of speed, but also produce quality beyond human capabilities.
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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 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.”
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.
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