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, Evidently AI seems to be more popular. It has been mentiond 2 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.
It is doable. However the main focus of MLFlow is in experiment tracking. I would suggest for you to look into another monitoring tools such evidentlyai . You can track more things than performance (e.g.data drift). Which may be helpful in a production setting. Source: almost 2 years ago
Evidently is an open-source Python library that analyzes and monitors machine learning models. It generates interactive reports based on Panda DataFrames and CSV files for troubleshooting models and checking data integrity. These reports show model health, data drift, target drift, data integrity, feature analysis, and performance by segment. - Source: dev.to / over 2 years ago
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