Synthetic data generation

Why label a dataset if you could
synthesize it?

Learn how Supervisely can dramatically improve model training results or even make impossible — possible!

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What's a synthetic data generation?

Get bottomless training data with little to no labeling

A traditional approach to model training has many steps, but it always includes the most challenging part: data annotation. There are many potential issues:

Standard workflow:

Weeks to months
  • 😥Extremely time and money consuming
  • 😥Error-prone, people can make mistakes
  • 😥New scenarios and conditions means more labeling

Fortunately, there is a better way. Instead of labeling dataset, we can programmatically compose it from different parts. For example, we are building an OCR system for shop receipts. Rather than labeling photos of read-world receipts, we can generate infinite amount of synthetic receipts that look almost identical. Since we generate all the labels, we don't need the labeling and can proceed straight to the model building.

Almost identical? This may seem like a problem at first, but many researches show that it's impossible to perfectly simulate real world — instead, it's better to teach model to adapt and learn from randomization.

Alternative workflow:

Minutes to hours
  • 😃Much less human involvement, immediate output
  • 😃In case of issues, just re-generate the whole dataset
  • 😃Easy to adapt to any changes

Synthetic data generation approach doesn't applicable for every task. But, usually, when it works, it works very well. What about you case? Get in touch with us and we will figure it out!

How does it work?

Here is an example of generating a synthetic dataset for instance segmentation of seeds. Exactly the same pipeline works for many other tasks, such as microbes or pathology detection.

Basic primitives

Label subset of you data or obtain it from external sources

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Backgrounds

Usually, it's easy to find or generate typical backgrounds

Input dataset

Just under 70 images is enough to represent every case we want to cover

Run Supervisely App

Let's apply synthetic generation application from the Ecosystem with an appropriate configuration

Powered by the following apps:

Get labeled dataset

Done! Verify generated data and start model training

Watch Flying Objects in action

For every step in AI pipeline

From Zero to Hero and beyond

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.

Train state-of-the-art models in browser

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Configure every aspect of training from target classes to online augmentations, monitor metrics and terminal logs in real-time.

Noticeable example app:

Visualize, analyze and improve performance

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Understand how your model works on ground truth and new data and find how to correct negative output and increase performance.

Noticeable example app:

Apply models in various scenarios and interfaces

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Put pre-trained or custom neural network models to use in labeling interfaces to archive extraordinary results.

Noticeable example app:

Boost training with synthetic data

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Generate synthetic datasets that drastically improve model results, especially when there is not enough ground truth.

Noticeable example app:

Query and transform, run augmentations

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Perform all the necessary actions on your data, from importing and converting to skeletonization of masks and rasterization.

Noticeable example app:

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supervisely install
> downloading pre-requirements...
> pulling docker images...
> installing software...
> Done! Supervisely is running on port :80

supervisely update
> checking for updates...
> Your version is up to date!
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#support
Team Manager 17:33
Hello @supervisely! Is there a way to create a project via API?
Supervisely 17:35
Sure thing, check out this docs!

Here’s why our customers trust us

BMW customer testimonial

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.

Engie customer testimonial
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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.”

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Dmitriy Slutskiy
PhD Research Engineer
Resson customer testimonial
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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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Travis Prosser
Engineering Specialist

Trusted by Fortune 500. Used by 65,000+ companies and researchers worldwide

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  • Go through the concepts
  • Learn your case
  • Offer a tailored solution

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