KopiKat generates a new, visually realistic duplicate of the original image, maintaining all critical data annotations. It alters the environment of the original images, for instance, adjusting factors like weather, seasons, and lighting conditions to add variety to datasets. This is crucial for fields such as object detection, neural network training, and transfer learning.
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KopiKat's answer:
Our goal with Kopikat is to strengthen practical applications, especially in scenarios where collecting an extensive dataset proves to be difficult. Kopikat is ideally designed for datasets containing up to 5,000 images, a common feature of numerous real-world AI initiatives. It equips engineers with the ability to enhance mean average precision (mAP), broaden and vary datasets—a critical edge in fields like object detection, neural network training, and transfer learning.
KopiKat's answer:
KopiKat's operation is remarkably simple and efficient for its users. All a user has to do is upload one image from their dataset. KopiKat then produces numerous images showcasing different scenarios, like alterations in illumination or weather, all the while preserving the annotations consistently. This attribute considerably expands the diversity of the dataset without requiring extra images, and creates a comprehensive, superior-quality model that introduces diversity beyond what traditional data augmentation techniques can offer. This method has demonstrated an improvement of over 5% in mean average precision (mAP), without any alterations to the AI model.
Based on our record, Gretel AI Beta² seems to be more popular. It has been mentiond 5 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
If you are working with synthetic data and would like to learn more, check out the blogpost that demonstrates how to automatically detect issues in synthetic customer reviews data generated from the http://Gretel.ai LLM synthetic data generator. Source: 10 months ago
I was chatting with the founder of GretelAI recently and learned a lot about the area of synthetic data. Source: about 1 year ago
The requirement is to build out synthetic data with very similar size and shape to our production data and ideally have a framework to do this, maybe at some level use our data and "de-productionize" it. gretel.ai looks like it may be a fit, but from what I see I need to upload production data to their environment and that's a no-go. Source: over 1 year ago
> https://gretel.ai/ Where were you the last decade of my professional life? I couldn’t find anyone to take my money for exactly this. - Source: Hacker News / over 1 year ago
[2] https://gretel.ai/blog/how-to-safely-work-with-another-companys-data. - Source: Hacker News / over 1 year ago
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