Autodistill RemoteCLIP Module

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

RemoteCLIP is a vision-language CLIP model trained on remote sensing data. According to the RemoteCLIP README:

RemoteCLIP outperforms previous SoTA by 9.14% mean recall on the RSICD dataset and by 8.92% on RSICD dataset. For zero-shot classification, our RemoteCLIP outperforms the CLIP baseline by up to 6.39% average accuracy on 12 downstream datasets.

Read the full Autodistill documentation.

Read the RemoteCLIP Autodistill documentation.

Installation

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

pip3 install autodistill-remote-clip

Quickstart

from autodistill_remote_clip import RemoteCLIP
from autodistill.detection import CaptionOntology

# define an ontology to map class names to our RemoteCLIP 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 = RemoteCLIP(
    ontology=CaptionOntology(
        {
            "airport runway": "runway",
            "countryside": "countryside",
        }
    )
)

predictions = base_model.predict("runway.jpg")

print(predictions)

License

This project is covered 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!