https://github.com/autodistill/autodistill-efficient-yolo-world
EfficientSAM + YOLO World base model for use with Autodistill.
https://github.com/autodistill/autodistill-efficient-yolo-world
Science Score: 13.0%
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Low similarity (11.1%) to scientific vocabulary
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Repository
EfficientSAM + YOLO World base model for use with Autodistill.
Basic Info
- Host: GitHub
- Owner: autodistill
- Language: Python
- Default Branch: main
- Homepage: https://docs.roboflow.com
- Size: 5.86 KB
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- Stars: 10
- Watchers: 4
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- Open Issues: 1
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Metadata Files
README.md
Autodistill EfficientYOLOWorld Module
This repository contains the code supporting the EfficientYOLOWorld base model for use with Autodistill.
EfficientYOLOWorld is a combination of two models:
- YOLO-World, a zero-shot object detection model, and;
- 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.
Read the EfficientYOLOWorld Autodistill documentation.
Installation
To use EfficientYOLOWorld with autodistill, you need to install the following dependency:
bash
pip3 install autodistill-efficient-yolo-world
Quickstart
```python from autodistillefficientyolo_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")
maskannotator = sv.MaskAnnotator() annotatedframe = mask_annotator.annotate( scene=image.copy(), detections=result, )
sv.plotimage(annotatedframe)
basemodel.label("./contextimages", extension=".jpeg") ```
License
EfficientSAM is licensed under an Apache 2.0 license.
YOLO-World is licensed under a GPL-3.0 license.
🏆 Contributing
We love your input! Please see the core Autodistill contributing guide to get started. Thank you 🙏 to all our contributors!
Owner
- Name: Autodistill
- Login: autodistill
- Kind: organization
- Email: autodistill@roboflow.com
- Website: https://autodistill.com
- Repositories: 1
- Profile: https://github.com/autodistill
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Dependencies
- autodistill-efficientsam *
- autodistill-yolo-world *
- numpy *
- supervision *
- torch *