An image captioning app looks at a picture and writes a short sentence describing what is in it.
In plain words
It uses an image-to-text model, which takes an image in and produces text out. Models such as BLIP were trained on image and caption pairs from the web, using a trick where the model writes its own captions and a filter throws out bad ones. Captions help people who are blind or have low vision understand images.
A quick example
Hugging Face's task page shows a photo of animals in a field captioned as giraffes and zebras feeding together in a field. With the Transformers library, a few lines load a BLIP captioning model and return that kind of sentence for any image.
Where you'll meet it
- Alt text for accessibility.
- Reading text in images, a related image-to-text job (OCR).
Related
Sources
- DocsImage-to-Text, Hugging Face Tasks · read 29 Sept 2026
- PaperBLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation, Li et al. (arXiv) · read 29 Sept 2026
Checked by WhatIsAI against the sources above · 29 Sept 2026. Spotted an error? Report it.