https://github.com/autodistill/autodistill-clip

CLIP module for use with Autodistill.

Science Score: 13.0%

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Low similarity (11.0%) to scientific vocabulary

Keywords

autodistill clip image-classification

Last synced: 5 months ago · JSON representation

Repository

CLIP module for use with Autodistill.

Basic Info

Host: GitHub
Owner: autodistill
License: mit
Language: Python
Default Branch: main
Homepage: https://docs.autodistill.com
Size: 14.6 KB

Statistics

Stars: 8
Watchers: 3
Forks: 1
Open Issues: 1
Releases: 0

Topics

autodistill clip image-classification

Created over 2 years ago · Last pushed about 2 years ago

Metadata Files

Readme License

README.md

Autodistill CLIP Module

This repository contains the code supporting the CLIP base model for use with Autodistill.

CLIP, developed by OpenAI, is a computer vision model trained using pairs of images and text. You can use CLIP with autodistill for image classification.

Read the full Autodistill documentation.

Read the CLIP Autodistill documentation.

Installation

To use CLIP with autodistill, you need to install the following dependency:

bash pip3 install autodistill-clip

Quickstart

```python from autodistill_clip import CLIP from autodistill.detection import CaptionOntology

define an ontology to map class names to our CLIP 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

base_model = CLIP( ontology=CaptionOntology( { "person": "person", "a forklift": "forklift" } ) )

results = basemodel.predict("./contextimages/test.jpg")

print(results)

basemodel.label("./contextimages", extension=".jpeg") ```

License

The code in this repository 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

Website: https://autodistill.com
Repositories: 1
Profile: https://github.com/autodistill

Use bigger slower models to train smaller faster ones

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James Gallagher	j**g@j**g	12

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AhmadPhazeRo (1)
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Packages

Total packages: 1
Total downloads:
- pypi 174 last-month

Total dependent packages: 0
Total dependent repositories: 0
Total versions: 6
Total maintainers: 2

pypi.org: autodistill-clip

CLIP module for use with Autodistill

Homepage: https://github.com/autodistill/autodistill-clip
Documentation: https://autodistill-clip.readthedocs.io/
License: MIT License
Latest release: 0.1.5
published about 2 years ago

Versions: 6
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 174 Last month

Rankings

Dependent packages count: 7.2%

Downloads: 17.3%

Average: 24.3%

Stargazers count: 25.4%

Forks count: 30.3%

Dependent repos count: 41.2%

Maintainers (2)

yeldarb capjamesg

Last synced: 6 months ago

Dependencies

.github/workflows/publish-to-pypi.yml actions

actions/checkout v3 composite
actions/setup-python v4 composite
pypa/gh-action-pypi-publish release/v1 composite

.github/workflows/publish.yml actions

actions/checkout v3 composite
actions/setup-python v2 composite

.github/workflows/test.yml actions

actions/checkout v3 composite
actions/setup-python v2 composite

.github/workflows/welcome.yml actions

actions/first-interaction v1.1.1 composite

setup.py pypi

numpy *
supervision *
torch *