<div align="center">
<p>
<a align="center" href="" target="_blank">
<img
width="850"
src="https://media.roboflow.com/open-source/autodistill/autodistill-banner.png"
>
</a>
</p>
</div>
# Autodistill SigLIP Module
This repository contains the code supporting the SigLIP base model for use with [Autodistill](https://github.com/autodistill/autodistill).
[CLIP](https://github.com/openai/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](https://autodistill.github.io/autodistill/).
Read the [SigLIP Autodistill documentation](https://autodistill.github.io/autodistill/base_models/siglip/).
## Installation
To use SigLIP with autodistill, you need to install the following dependency:
```bash
pip3 install autodistill-clip
```
## 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)
top_1 = results.get_top_k(1)
# show top label
print(labels[top_1[0][0]])
# label folder of images
base_model.label("./context_images", extension=".jpeg")
```
## License
The SigLIP model is licensed under an [Apache 2.0 license](https://huggingface.co/google/siglip-base-patch16-224).
## 🏆 Contributing
We love your input! Please see the core Autodistill [contributing guide](https://github.com/autodistill/autodistill/blob/main/CONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors!
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