Autodistill EvaCLIP Module

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

EvaCLIP, is a computer vision model trained using pairs of images and text. It can be used for classification of images.

Read the full Autodistill documentation.

Read the EvaCLIP Autodistill documentation.

Installation

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

pip3 install autodistill-evaclip

Quickstart

from autodistill_evaclip import EvaCLIP
from autodistill.detection import CaptionOntology

# define an ontology to map class names to our EvaCLIP 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 = EvaCLIP(
    ontology=CaptionOntology(
        {
            "person": "person",
            "a forklift": "forklift"
        }
    )
)

results = base_model.predict("./context_images/test.jpg")

print(results)

base_model.label("./context_images", 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!