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Caption Anything

Caption Anything

โ˜… 3.8 ๐Ÿ”– 1200 saves ๐Ÿ’ฐ Free GitHubImage

Caption Anything is an open-source image captioning tool that combines Meta’s Segment Anything model, visual captioning models, and ChatGPT. It has 1,778 stars on GitHub and was last updated in 2023. Instead of describing a whole image generically, it lets you point at any object and get a caption written for that object, in the style you ask for.

Overview

The workflow is interactive: click on an object in an image and Segment Anything isolates it; a captioning model drafts a description; and ChatGPT refines it under your language controls. You can steer length, sentiment, factuality, and language, chat with the tool about the selected object for a deeper understanding, or caption everything in the scene as a paragraph. The project, from Tencent ARC and partner labs, ships with public demos on Hugging Face Spaces and a Colab tutorial, and it accompanies a 2023 research paper. The system was one of the earliest popular combinations of Segment Anything with a large language model, and it helped popularize the click-to-describe interaction pattern now common in vision tools. Instructions and checkpoints for running the demo locally are included in the repository.

Key Features

  • Click-to-caption any object in an image using Segment Anything segmentation
  • Language controls for caption length, sentiment, factuality, and output language
  • Chat about the selected object for detailed visual question answering
  • Whole-image paragraph captioning mode
  • Interactive web demo and Hugging Face Spaces deployments
  • Mouse-trajectory visual control for pointing at regions (beta)

๐Ÿ’ณ Pricing

Caption Anything is free and open source under the BSD-3-Clause license. Running the full pipeline locally may require a ChatGPT API key for the language refinement step.

Who Is It For

It is for computer vision researchers, dataset builders, and accessibility experimenters who need controllable, object-level image descriptions rather than one generic caption per image. It remains a frequently cited example of composing foundation models into a new capability.

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