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Overview

YOLO-World is a series of models that extend the YOLO (You Only Look Once) framework to open-world settings, allowing for dynamic object detection across a variety of environments. These models are designed to handle a wide range of object types and are particularly useful for applications requiring robust and real-time object detection.

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YOLOWorld(model: str)

The YOLOWorld class encapsulates the functionality of the YOLO-World models, providing methods for loading the model, detecting objects based on textual descriptions, and managing resources.

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model
default:"yolov8l-worldv2"

Specifies which version of the model to load the s models are less accurate and faster will the l models are more accurate but slower.

Valid options include "yolov8s-worldv2", "yolov8m-worldv2", "yolov8l-worldv2", "yolov8s-world", "yolov8m-world", and "yolov8l-world".

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Methods

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YOLOWorld.detect(image, classes)
  • detect(image: Union[np.ndarray, Image.Image], classes: List[str])-> Detections:

This method detects objects in the provided image based on the classes described in text. Returns a BoundingBox Detections object.

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When to use?

YOLO-World models are the fastest among the available models and the only ones capable of performing real-time detection, making them ideal for scenarios where speed is critical, such as in video surveillance or autonomous driving. They provide a good balance between speed and accuracy but may struggle with labels that are not similar to the standard COCO labels.

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Example Usage

wget -O kites.jpg "https://github.com/overeasy-sh/overeasy/blob/main/examples/kites.jpg?raw=true"

from overeasy import *
from PIL import Image
workflow = Workflow([
BoundingBoxSelectAgent(classes=["butterfly kite"], model=YOLOWorld()),
])

image = Image.open("./kites.jpg")
results, graph = workflow.execute(image)
workflow.visualize(graph)

Note how the model did not filter out the butterfly kite, but instead just selected all the kites

​
Overview

YOLO-World is a series of models that extend the YOLO (You Only Look Once) framework to open-world settings, allowing for dynamic object detection across a variety of environments. These models are designed to handle a wide range of object types and are particularly useful for applications requiring robust and real-time object detection.

​
YOLOWorld(model: str)

The YOLOWorld class encapsulates the functionality of the YOLO-World models, providing methods for loading the model, detecting objects based on textual descriptions, and managing resources.

​
model
default:"yolov8l-worldv2"

Specifies which version of the model to load the s models are less accurate and faster will the l models are more accurate but slower.

Valid options include "yolov8s-worldv2", "yolov8m-worldv2", "yolov8l-worldv2", "yolov8s-world", "yolov8m-world", and "yolov8l-world".

​
Methods

​
YOLOWorld.detect(image, classes)
  • detect(image: Union[np.ndarray, Image.Image], classes: List[str])-> Detections:

This method detects objects in the provided image based on the classes described in text. Returns a BoundingBox Detections object.

​
When to use?

YOLO-World models are the fastest among the available models and the only ones capable of performing real-time detection, making them ideal for scenarios where speed is critical, such as in video surveillance or autonomous driving. They provide a good balance between speed and accuracy but may struggle with labels that are not similar to the standard COCO labels.

​
Example Usage

wget -O kites.jpg "https://github.com/overeasy-sh/overeasy/blob/main/examples/kites.jpg?raw=true"

from overeasy import *
from PIL import Image
workflow = Workflow([
BoundingBoxSelectAgent(classes=["butterfly kite"], model=YOLOWorld()),
])

image = Image.open("./kites.jpg")
results, graph = workflow.execute(image)
workflow.visualize(graph)

Note how the model did not filter out the butterfly kite, but instead just selected all the kites