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Increase your AI solution quality with no-code data augmentation

Multiply your data by 10x in minutes.
Preserve labeling and privacy, and enjoy easy scalability.
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<   Try! It’s free

KopiKat Improves The Neural Network Quality

Kopikat Experiment
We ran an experiment on 5% of MSCOCO dataset and YOLOX-Nano.
We got +1.06 mAP, or 14.6% of the metric, out of the box - and made no changes to the network architecture.
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Our cases

Examples
Crafting Digital Human Images with Comprehensive Annotations
Utilising the COCO dataset, we created an array of label-preserved high-resolution realistic human images within several minutes. Ideal for training neural networks for face detection.
Cases
Crafting Digital Human Images with Comprehensive Annotations
With Kopikat, we created a dataset of high-resolution realistic human images within several minutes. This is a great solution for people detection solutions in different domains: retail, manufacturing, or autonomous driving.
Original
Generated
Generated
Generated
Generated
Cases
Creating high-quality images for the automotive industry
Fine-tuning in KopiKat allows you to create photorealistic environments, such as snow or road surface textures. At the same time you can save the road markings, road signs, poles, and traffic lights location.
Examples
Automotive Images with Preserved Data Annotations
Kopikat allows to augment images with different weather and lighting conditions. This is a game-changer for many industries, including autonomous driving. It's impossible to collect all the road scenes in every weather and lighting - and Kopikat gives an elegant solution to it.
Original
Preserved Annotation
Generated
Night
Snow

Why Kopikat is so useful?

Save time
and
resources
Our service significantly reduces the time and effort required for data collection and labeling.
Improved
model
accuracy
With larger volumes of data, you can create more accurate models and cover a wider range of corner cases.
Flexibility
and
scalability
You can choose the amount of data needed for your specific task and easily scale it as necessary.
No
confidentiality issues
By using synthetic data, problems with personal data confidentiality are eliminated.