baaivision/tokenize-anything
A promptable vision foundation model for segmenting, recognizing, and captioning arbitrary regions with flexible visual prompts.
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Tokenize Anything via Prompting (TAP) is a unified model that simultaneously performs open-world segmentation, recognition, and captioning using visual prompts such as points, boxes, and sketches. The model is trained on exhaustive segmentation masks from SA-1B combined with semantic priors from EVA-CLIP, a 5-billion parameter vision-language model. It provides a modular design with decoupled components and predictors for flexible integration.
Frequently asked
- What is baaivision/tokenize-anything?
- A promptable vision foundation model for segmenting, recognizing, and captioning arbitrary regions with flexible visual prompts.
- Is tokenize-anything open source?
- Yes — baaivision/tokenize-anything is open source, released under the Apache-2.0 license.
- What language is tokenize-anything written in?
- baaivision/tokenize-anything is primarily written in Jupyter Notebook.
- How popular is tokenize-anything?
- baaivision/tokenize-anything has 601 stars on GitHub.
- Where can I find tokenize-anything?
- baaivision/tokenize-anything is on GitHub at https://github.com/baaivision/tokenize-anything.