Annotating data for machine learning is notoriously time-consuming, and striving for precision doesn't make it any easier. The industry—be it retail, automotive, medical imaging, or any other—doesn't change this fundamental need for quick, accurate data annotation.

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Now, let's explore a scenario (which might not be so hypothetical) where your ML model requires datasets annotated with masks. CVAT.ai has tool for it, but there's an even cooler feature that streamlines the process: annotation actions, particularly the shape converter.

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With this feature, you can initially use whatever annotation shape suits you best or is easiest for you—let's say polygons, for instance. This approach saves time, especially if you're more familiar with a specific tool or leveraging automatic annotation. Once you're done, you can easily convert all your annotations from masks to polygons with just a few clicks, ensuring both speed and accuracy in your work. You can also filter out and delete shapes that you do not need anymore.

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To see how this works in action, check out our latest video:

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The video covers the following topics

You can use shapes converter in the retail sector. For example, ensuring that all products on shelves are monitored accurately for restocking requires uniform annotations. CVAT.ai allows  quick conversion of different shapes to standardize data input, making it easier for models to learn and predict.

In the automotive industry, getting the details right when marking street signs, pedestrians, and vehicles is crucial. CVAT.ai's shape converter assists in creating precisely annotated sets for these needs, enhancing the training of autonomous systems to navigate and understand the real world with greater accuracy.

In the medical field, where the accurate analysis of diagnostic images can be a matter of life and death, CVAT.ai shape conversion tool allows flexible, precise annotations. This is vital for developing medical tools that can accurately diagnose conditions from medical imagery, enhancing research outcomes and patient care.

Conclusion

CVAT Annotations Actions simplify and improves the annotation process, making it faster and more efficient to prepare datasets for machine learning across various industries. This not only saves time but also improves the quality of data, leading to more reliable and effective AI models.

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Happy annotating!

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