https://github.com/autodistill/autodistill-yolo-world

YOLO World base module for use with Autodistill.
https://github.com/autodistill/autodistill-yolo-world

autodistill object-detection yolo-world zero-shot-object-detection

Last synced: about 2 months ago
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YOLO World base module for use with Autodistill.

• Host: GitHub
• URL: https://github.com/autodistill/autodistill-yolo-world
• Owner: autodistill
• Created: 2024-02-12T16:40:15.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-02-16T11:19:54.000Z (11 months ago)
• Last Synced: 2024-04-23T23:36:11.111Z (8 months ago)
• Topics: autodistill, object-detection, yolo-world, zero-shot-object-detection
• Language: Python
• Homepage: https://docs.autodistill.com
• Size: 11.7 KB
• Stars: 3
• Watchers: 4
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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# Autodistill YOLO-World Module

This repository contains the code supporting the YOLO-World base model for use with [Autodistill](https://github.com/autodistill/autodistill).

[YOLO-World](https://github.com/AILab-CVC/YOLO-World), developed by Tencent's AI Lab, is a zero-shot object detection model that can run in close-to-real-time on powerful GPUs. You can provide arbitrary text prompts to detect objects in images, and the model will return bounding boxes and class labels for the objects it finds.

You can use YOLO-World in Autodistill to detect objects.

Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).

Read the [YOLO-World Autodistill documentation](https://autodistill.github.io/autodistill/base_models/yolo_world/).

## Installation

To use YOLO-World with autodistill, you need to install the following dependency:

```bash

pip3 install autodistill-yolo-world

```

## Quickstart

```python

from autodistill_yolo_world import YOLOWorldModel

from autodistill.detection import CaptionOntology

from autodistill.utils.plot import plot

# define an ontology to map class names to our YOLO-World 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 = YOLOWorldModel(

    ontology=CaptionOntology(

        {

            "person": "person",

            "a forklift": "forklift"

        }

    )

)

# predict on an image

result = base_model.predict("image.jpeg", confidence=0.1)

plot(

    image=cv2.imread("./image.jpeg"),

    classes=base_model.ontology.classes(),

    detections=result

)

# label a folder of images

base_model.label("./context_images", extension=".jpeg")

```

## License

The YOLO-World model is released under a [GPT-3.0 license](https://github.com/AILab-CVC/YOLO-World).

## 🏆 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!