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:

pip3 install autodistill-clip

Quickstart

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.

🏆 Contributing

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