| Name | autodistill-efficientsam JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | |
| Summary | EfficientSAM model for use with Autodistill |
| upload_time | 2024-02-16 18:39:45 |
| maintainer | |
| docs_url | None |
| author | Roboflow |
| requires_python | >=3.7 |
| license | |
| keywords |
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No requirements were recorded.
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<div align="center">
<p>
<a align="center" href="" target="_blank">
<img
width="850"
src="https://media.roboflow.com/open-source/autodistill/autodistill-banner.png"
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# Autodistill EfficientSAM Module
This repository contains the code supporting the EfficientSAM base model for use with [Autodistill](https://github.com/autodistill/autodistill).
[EfficientSAM](https://github.com/yformer/EfficientSAM) is an image segmentation model that was introduced in the paper "[EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything](https://yformer.github.io/efficient-sam/)". You can use EfficientSAM with autodistill for image segmentation.
Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).
## Installation
To use EfficientSAM with Autodistill, you need to install the following dependency:
```bash
pip3 install autodistill-efficientsam
```
## Quickstart
This model returns segmentation masks for all objects in an image.
If you want segmentation masks only for specific objects matching a text prompt, we recommend combining EfficientSAM with a zero-shot detection model like GroundingDINO.
Read our ComposedDetectionModel documentation for more information about how to combine models like EfficientSAM and GroundingDINO.
```python
from autodistill_efficientsam import EfficientSAM
# define an ontology to map class names to our EfficientSAM 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 = EfficientSAM(None)
masks = base_model.predict("./image.png")
```
## 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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