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https://github.com/autodistill/autodistill-gpt-4v

GPT-4V(ision) module for use with Autodistill.

https://github.com/autodistill/autodistill-gpt-4v

Science Score: 13.0%

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    Scientific vocabulary similarity
    Low similarity (14.8%) to scientific vocabulary

Keywords

autodistill computer-vision gpt-4 gpt-4v object-detection
Last synced: 6 months ago · JSON representation

Repository

GPT-4V(ision) module for use with Autodistill.

Basic Info
  • Host: GitHub
  • Owner: autodistill
  • License: mit
  • Language: Python
  • Default Branch: main
  • Homepage: https://docs.roboflow.com
  • Size: 24.4 KB
Statistics
  • Stars: 26
  • Watchers: 5
  • Forks: 7
  • Open Issues: 4
  • Releases: 0
Topics
autodistill computer-vision gpt-4 gpt-4v object-detection
Created over 2 years ago · Last pushed over 1 year ago
Metadata Files
Readme License

README.md

Autodistill GPT-4V Module

This repository contains the code supporting the GPT-4V base model for use with Autodistill.

GPT-4V, developed by OpenAI, is a multi-modal language model. With GPT-4V, you can ask questions about images in natural language. The autodistill-gpt4v module enables you to classify images using GPT-4V.

This model uses the gpt-4-vision-preview API announced by OpenAI on November 6th, 2023.

[!NOTE]
Using this project will incur billing charges for API calls to the OpenAI GPT-4 Vision API. Refer to the OpenAI pricing page for more information and to calculate your expected pricing. This package makes one API call per image you want to label.

Read the full Autodistill documentation.

Read the GPT-4V Autodistill documentation.

Installation

To use GPT-4V with autodistill, you need to install the following dependency:

bash pip3 install autodistill-gpt-4v

Quickstart

```python from autodistillgpt4v import GPT4V

define an ontology to map class names to our GPT-4V prompt

the ontology dictionary has the format {caption: class}

where caption is the prompt sent to the base model, and class is the label that will

be saved for that caption in the generated annotations

then, load the model

basemodel = GPT4V( ontology=CaptionOntology( { "person": "person", "a forklift": "forklift" } ), apikey="OPENAIAPIKEY" ) basemodel.label("./contextimages", extension=".jpeg") ```

License

This project is licensed under an MIT license.

🏆 Contributing

We love your input! Please see the core Autodistill contributing guide to get started. Thank you 🙏 to all our contributors!

Owner

  • Name: Autodistill
  • Login: autodistill
  • Kind: organization
  • Email: autodistill@roboflow.com

Use bigger slower models to train smaller faster ones

GitHub Events

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Last synced: 9 months ago

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  • Total issues: 4
  • Total pull requests: 3
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  • Average time to close pull requests: about 2 months
  • Total issue authors: 3
  • Total pull request authors: 3
  • Average comments per issue: 0.0
  • Average comments per pull request: 1.67
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
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  • Pull requests: 1
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Top Authors
Issue Authors
  • alexfilothodoros (1)
  • gepdev (1)
  • shersoni610 (1)
Pull Request Authors
  • japha (2)
  • daaniyaan (1)
  • elabbarw (1)
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 95 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 6
  • Total maintainers: 1
pypi.org: autodistill-gpt-4v

GPT-4V model for use with Autodistill

  • Versions: 6
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 95 Last month
Rankings
Downloads: 7.3%
Dependent packages count: 10.0%
Stargazers count: 13.3%
Average: 14.3%
Forks count: 19.1%
Dependent repos count: 21.7%
Maintainers (1)
yeldarb
Last synced: 8 months ago

Dependencies

setup.py pypi
  • autodistill *
  • requests *
  • supervision *