https://github.com/autodistill/autodistill-metaclip
MetaCLIP module for use with Autodistill.
Science Score: 36.0%
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Low similarity (12.1%) to scientific vocabulary
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Repository
MetaCLIP module for use with Autodistill.
Basic Info
- Host: GitHub
- Owner: autodistill
- License: other
- Language: Python
- Default Branch: main
- Homepage: https://docs.autodistill.com
- Size: 22.5 KB
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- Stars: 21
- Watchers: 2
- Forks: 1
- Open Issues: 0
- Releases: 0
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Metadata Files
README.md
Autodistill MetaCLIP Module
This repository contains the code supporting the MetaCLIP base model for use with Autodistill.
MetaCLIP, developed by Meta AI Research, is a computer vision model trained using pairs of images and text. The model was described in the Demystifying CLIP Data paper. You can use MetaCLIP with autodistill for image classification.
Read the full Autodistill documentation.
Read the MetaCLIP Autodistill documentation.
Installation
To use MetaCLIP with autodistill, you need to install the following dependency:
bash
pip3 install autodistill-metaclip
Quickstart
get predictions
```python from autodistill_metaclip import MetaCLIP
define an ontology to map class names to our MetaCLIP 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 = MetaCLIP( ontology=CaptionOntology( { "person": "person", "a forklift": "forklift" } ) )
results = base_model.predict("./image.png") print(results) ```
calculate and compare embeddings
```python from autodistill_metaclip import MetaCLIP
base_model = MetaCLIP(None)
text = basemodel.embedtext("coffee") image = basemodel.embedimage("coffeeshop.jpg")
print(base_model.compare(text, image)) ```
License
This project was licensed under a Creative Commons Attribution-NonCommercial 4.0 International.
🏆 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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- Total versions: 4
- Total maintainers: 1
pypi.org: autodistill-metaclip
MetaCLIP base model for use with Autodistill.
- Homepage: https://github.com/autodistill/autodistill-metaclip
- Documentation: https://autodistill-metaclip.readthedocs.io/
- License: MIT License
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Latest release: 0.1.3
published over 2 years ago