AI dataset

MedTrinity-25M is a large-scale multi-modal dataset containing medical annotations at multiple granularities. It was developed by multiple authors to advance research in the field of medical image and text processing. The construction of the data set includes steps such as data extraction and multi-granularity text description generation, and supports a variety of medical image analysis tasks, such as visual question answering (VQA), pathology image analysis, etc.

PixelProse is a large-scale dataset created by tomg-group-umd that leverages the advanced visual-language model Gemini 1.0 Pro Vision to generate more than 16 million detailed image descriptions. This dataset is of great significance for developing and improving image-to-text conversion technology, which can be used for image description generation, visual question answering and other tasks.

AutoMathText is an extensive and carefully curated dataset containing approximately 200GB of mathematical text. Each piece of content in the dataset is independently selected and scored by Qwen, the most advanced open source language model, ensuring high standards of relevance and quality. This dataset is particularly suitable for promoting advanced research at the intersection of mathematics and artificial intelligence, as an educational tool for learning and teaching complex mathematical concepts, and as a basis for developing and training AI models that specifically process and understand mathematical content.