https://github.com/autodistill/autodistill-fastsam

FastSAM module for use with Autodistill.
https://github.com/autodistill/autodistill-fastsam

autodistill computer-vision fastsam

Last synced: about 1 month ago
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FastSAM module for use with Autodistill.

• Host: GitHub
• URL: https://github.com/autodistill/autodistill-fastsam
• Owner: autodistill
• License: apache-2.0
• Created: 2023-07-03T09:11:18.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-12-06T09:38:55.000Z (about 1 year ago)
• Last Synced: 2024-10-12T19:49:08.764Z (2 months ago)
• Topics: autodistill, computer-vision, fastsam
• Language: Python
• Homepage: https://docs.roboflow.com
• Size: 24.4 KB
• Stars: 4
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# 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!