https://github.com/autodistill/autodistill-siglip
SigLIP base model for use with Autodistill.
Science Score: 13.0%
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○Scientific vocabulary similarity
Low similarity (11.2%) to scientific vocabulary
Repository
SigLIP base model for use with Autodistill.
Basic Info
- Host: GitHub
- Owner: autodistill
- Language: Python
- Default Branch: main
- Size: 9.77 KB
Statistics
- Stars: 7
- Watchers: 4
- Forks: 1
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Autodistill SigLIP Module
This repository contains the code supporting the SigLIP 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 SigLIP Autodistill documentation.
Installation
To use SigLIP with autodistill, you need to install the following dependency:
bash
pip3 install autodistill-siglip
Quickstart
```python from autodistill_siglip import SigLIP from autodistill.detection import CaptionOntology
define an ontology to map class names to our SigLIP 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
labels = ["person", "a forklift"] base_model = SigLIP( ontology=CaptionOntology({item: item for item in labels}) )
results = base_model.predict("image.jpeg", confidence=0.1)
top1 = results.gettop_k(1)
show top label
print(labels[top_1[0][0]])
label folder of images
basemodel.label("./contextimages", extension=".jpeg") ```
License
The SigLIP model is licensed under an Apache 2.0 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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pypi.org: autodistill-siglip
SigLIP base model for use with Autodistill
- Homepage: https://github.com/autodistill/autodistill-siglip
- Documentation: https://autodistill-siglip.readthedocs.io/
- License: MIT License
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Latest release: 0.1.0
published over 2 years ago
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Maintainers (1)
Dependencies
- autodistill *
- numpy *
- torch *
- transformers *