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

efficientsam yolo-world zero-shot-object-detection zero-shot-segmentation

Last synced: about 2 months ago
JSON representation

EfficientSAM + YOLO World base model for use with Autodistill.

• Host: GitHub
• URL: https://github.com/autodistill/autodistill-efficient-yolo-world
• Owner: autodistill
• Created: 2024-02-16T20:46:15.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-02-21T09:29:27.000Z (10 months ago)
• Last Synced: 2024-02-21T10:37:42.028Z (10 months ago)
• Topics: efficientsam, yolo-world, zero-shot-object-detection, zero-shot-segmentation
• Language: Python
• Homepage: https://docs.roboflow.com
• Size: 5.86 KB
• Stars: 2
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        


  


    

      

    

  



# 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!