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src="https://media.roboflow.com/open-source/autodistill/autodistill-banner.png"
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# Autodistill EfficientYOLOWorld Module
This repository contains the code supporting the EfficientYOLOWorld base model for use with [Autodistill](https://github.com/autodistill/autodistill).
EfficientYOLOWorld is a combination of two models:
1. YOLO-World, a zero-shot object detection model, and;
2. EfficientSAM, an image segmentation model.
This model runs EfficientSAM on each bounding box region generated by YOLO-World. This allows you to retrieve both the bounding box and the segmentation mask for each object of interest in an image.
Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).
Read the [EfficientYOLOWorld Autodistill documentation](https://autodistill.github.io/autodistill/base_models/efficient_yolo_world/).
## Installation
To use EfficientYOLOWorld with autodistill, you need to install the following dependency:
```bash
pip3 install autodistill-efficient-yolo-world
```
## Quickstart
```python
from autodistill_efficient_yolo_world import EfficientYOLOWorld
from autodistill.detection import CaptionOntology
import cv2
import supervision as sv
# define an ontology to map class names to our EfficientYOLOWorld 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 = EfficientYOLOWorld(ontology=CaptionOntology({"book": "book"}))
# predict on an image
result = base_model.predict("bookshelf.jpeg", confidence=0.1)
image = cv2.imread("bookshelf.jpeg")
mask_annotator = sv.MaskAnnotator()
annotated_frame = mask_annotator.annotate(
scene=image.copy(),
detections=result,
)
sv.plot_image(annotated_frame)
base_model.label("./context_images", extension=".jpeg")
```
## License
EfficientSAM is licensed under an [Apache 2.0 license](EFFICIENT_SAM_LICENSE).
YOLO-World is licensed under a [GPL-3.0 license](YOLO_WORLD_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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