<div align="center">
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<a align="center" href="" target="_blank">
<img
width="850"
src="https://media.roboflow.com/open-source/autodistill/autodistill-banner.png?3"
>
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# Autodistill Florence 2 Module
This repository contains the code supporting the CLIP base model for use with [Autodistill](https://github.com/autodistill/autodistill).
[Florence 2](https://huggingface.co/microsoft/Florence-2-large), introduced in the paper [Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks](https://arxiv.org/abs/2311.06242) 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](https://autodistill.github.io/autodistill/).
Read the [Florence 2 Autodistill documentation](https://autodistill.github.io/autodistill/base_models/florence2/).
## Installation
To use Florence 2 with Autodistill, you need to install the following dependency:
```bash
pip3 install autodistill-florence-2
```
## Quickstart
```python
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](https://huggingface.co/microsoft/Florence-2-large) for more information about the Florence 2 model license.
## 🏆 Contributing
We love your input! Please see the core Autodistill [contributing guide](https://github.com/autodistill/autodistill/blob/main/CONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors!
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