Conceptual Captions (CC3M / CC12M)
Conceptual Captions is built from the alt-text that web authors attach to images, the short descriptions meant for screen readers. Google Research cleaned this raw text and generalised it, a step they call hypernyming: swapping a specific name like a person or brand for a broader category such as "man" or "car", so the captions describe image content rather than name particular entities.
It comes in two sizes, the 3.3 million pair CC3M and the larger 12 million pair CC12M. The dataset is distributed as image URLs paired with captions, so you download the images from their original hosts yourself.
Access is governed by Google Research terms that are permissive for research use. Check those terms before building anything commercial on top of it.
Related sources
DataComp
A benchmark and dataset collection for training multimodal models. It provides a shared pool of image-text candidates and an evaluation framework so teams can test different data-curation strategies against a common yardstick.
LAION-5B
An image-text dataset of over 5.8B examples, built by filtering Common Crawl with a CLIP model that scores how well an image matches a caption. Includes 2.32B English pairs, 2.26B multilingual pairs, and 1.27B not tied to any particular language. Provides URLs, not the images themselves.
OBELICS
A web-scale dataset of 141M multimodal English web documents that interleave text and images, containing 353M images and 115 billion tokens. Unlike image-caption pair datasets, it keeps whole documents with text and images in their original order. Used to train the IDEFICS models.
PD12M (Public Domain 12M)
An image-text dataset built only from materials marked with a Public Domain Mark or released under Creative Commons Zero (CC0). 12.4M image-caption pairs, with a 3.3M subset called PD3M, sized to match the Conceptual Captions datasets while staying copyright-clean for commercial use.