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.

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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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Hello @supervisely! Is there a way to create a project via API?
Supervisely 17:35
Sure thing, check out this docs!

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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.

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