autodistill-florence-2 0.1.0

Use Florence 2 to auto-label data for use in training fine-tuned object detection models.

Autodistill Florence 2 Module

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

Florence 2, introduced in the paper Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks is a multimodal vision model.

You can use Florence 2 to generate object detection annotations for use in training smaller object detection models with Autodistill.

Read the full Autodistill documentation.

Read the Florence 2 Autodistill documentation.

Installation

To use Florence 2 with Autodistill, you need to install the following dependency:

pip3 install autodistill-florence-2

Quickstart

from autodistill_florence_2 import Florence2

# define an ontology to map class names to our Florence 2 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 = Florence2(
    ontology=CaptionOntology(
        {
            "person": "person",
            "a forklift": "forklift"
        }
    )
)
base_model.label("./context_images", extension=".jpeg")

License

This project is licensed under an MIT license. See the Florence 2 license for more information about the Florence 2 model license.

🏆 Contributing

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