<div align="center">
<p>
<a align="center" href="" target="_blank">
<img
width="850"
src="https://media.roboflow.com/open-source/autodistill/autodistill-banner.png"
>
</a>
</p>
</div>
# Autodistill FastSAM Module
This repository contains the code supporting the FastSAM base model for use with [Autodistill](https://github.com/autodistill/autodistill).
[FastSAM](https://github.com/CASIA-IVA-Lab/FastSAM) is a segmentation model trained on 2% of the SA-1B dataset used to train the [Segment Anything Model](https://github.com/facebookresearch/segment-anything).
Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).
Read the [FastSAM Autodistill documentation](https://autodistill.github.io/autodistill/base_models/fastsam/).
## Installation
To use FastSAM with autodistill, you need to install the following dependency:
```bash
pip3 install autodistill-fastsam
```
## Quickstart
> [!NOTE]
> When you first run this model, the installation process will start. Inference may take a few seconds (in testing, up to 30 seconds) while the model is downloaded and installed. Once the model is installed, inference will be much faster.
```python
from autodistill_fastsam import FastSAM
# define an ontology to map class names to our FastSAM 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 = FastSAM(
ontology=CaptionOntology(
{
"person": "person",
"a forklift": "forklift"
}
)
)
base_model.label("./context_images", extension=".jpeg")
```
## License
This project is licensed under an [Apache 2.0 license](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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