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https://github.com/autodistill/autodistill-remote-clip

RemoteCLIP module for use with Autodistill.

https://github.com/autodistill/autodistill-remote-clip

Science Score: 13.0%

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Repository

RemoteCLIP module for use with Autodistill.

Basic Info
  • Host: GitHub
  • Owner: autodistill
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Size: 16.6 KB
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  • Stars: 3
  • Watchers: 3
  • Forks: 0
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Created over 2 years ago · Last pushed about 2 years ago
Metadata Files
Readme License

README.md

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:

bash pip3 install autodistill-remote-clip

Quickstart

```python from autodistillremoteclip 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!

Owner

  • Name: Autodistill
  • Login: autodistill
  • Kind: organization
  • Email: autodistill@roboflow.com

Use bigger slower models to train smaller faster ones

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Dependencies

setup.py pypi
  • huggingface_hub *
  • open-clip-torch *
  • torch *