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PixelProse, a new dataset comprising over 16 million diverse images with dense captions, has been introduced. Sourced from three web-scraped databases—CommonPool, CC12M, and RedCaps—the dataset features synthetically generated captions using Google's Gemini model. The captions are noted for their high detail and reduced toxicity. PixelProse, which includes 16M image-caption pairs, aims to bridge the gap between commercial vision models and open-source solutions by providing high-quality image-caption pairs that can be refactored into instructions, question-answer pairs, and more using a large language model (LLM).
We know vision might not be ready for language yet. But we know a better large dataset is the first step. Hope you like PixelProse. 📝 https://t.co/oFpnGW6W4N
Let's start closing the gap between commercial vision models and open source! The PixelProse dataset contains 16M images labeled with high quality *dense* captions that are specifically designed to be refactored into instructions, question-answer pairs, etc, using a LLM. https://t.co/ceRAkuV5va
Forget about all the captioning datasets you've tried before! PixelProse is a captioning dataset of 16M image-caption pairs, with less toxicity and higher details ✨ https://t.co/xYrMOjsyzU https://t.co/Cr96kETTeh
[CV] From Pixels to Prose: A Large Dataset of Dense Image Captions https://t.co/XP5rcfGehY - This paper introduces PixelProse, a dataset of over 16 million synthetically generated image captions using Google's Gemini model. The captions are much more detailed and… https://t.co/LKhxBty54d
From Pixels to Prose: A Large Dataset of Dense Image Captions abs: https://t.co/auWTERQ1Bm dataset: https://t.co/AVTaNfMbn7 PixelProse comprises over 16M diverse images sourced from three different web-scraped databases (CommonPool, CC12M, RedCaps), with captions generated… https://t.co/YIkXaw0PLL