Learn how Supervisely can dramatically improve model training results or even make impossible — possible!
A traditional approach to model training has many steps, but it always includes the most challenging part: data annotation. There are many potential issues:
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.
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!
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.
Label subset of you data or obtain it from external sources
Usually, it's easy to find or generate typical backgrounds
Just under 70 images is enough to represent every case we want to cover
Let's apply synthetic generation application from the Ecosystem with an appropriate configuration
Done! Verify generated data and start model training
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.
Understand how your model works on ground truth and new data and find how to correct negative output and increase performance.
Put pre-trained or custom neural network models to use in labeling interfaces to archive extraordinary results.
Generate synthetic datasets that drastically improve model results, especially when there is not enough ground truth.
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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.”
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.
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.Order workforce