Overview

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YOLO-World, introduced in the research paper “YOLO-World: Real-Time Open-Vocabulary Object Detection”, shows a significant advancement in the field of open-vocabulary object detection by demonstrating that lightweight detectors, such as those from the YOLO series, can achieve strong open-vocabulary performance. This is particularly noteworthy for real-world applications where efficiency and speed are crucial, like edge applications. In the following image, YOLO-World demonstrates a 20x speedup from previous models, while also keeping similar accuracy, which makes it heavily applicable to real-time applications.

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YOLO-World has grounding capabilities and can understand the context in a prompt to provide detections. You do not need to train the model on a particular class because the model has been trained using image-text pairs and grounded images. The model has learned how to take an arbitrary prompt – for example, “person wearing a white shirt” – and use that for detection.

YOLO-World exclusively supports object detection.

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YOLO-World is supported on autodistill and inference.

Performance

According to the paper YOLO-World reached between 35.4 AP with 52.0 FPS for the large version and 26.2 AP with 74.1 FPS for the small version. While the V100 is a powerful GPU, achieving such high FPS on any device is impressive.

Use This Model

First, install Inference:


pip install inference

Run the following command to set your API key in your coding environment:

export ROBOFLOW_API_KEY=<your api key>

Then, create a new Python file called app.py and add the following code:


import cv2
import supervision as sv

from inference.models.yolo_world.yolo_world import YOLOWorld

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

model = YOLOWorld(model_id="yolo_world/l")
classes = ["person", "backpack", "dog", "eye", "nose", "ear", "tongue"]
results = model.infer("image.jpeg", text=classes, confidence=0.03)

detections = sv.Detections.from_inference(results[0])

bounding_box_annotator = sv.BoundingBoxAnnotator()
label_annotator = sv.LabelAnnotator()

labels = [classes[class_id] for class_id in detections.class_id]

annotated_image = bounding_box_annotator.annotate(
    scene=image, detections=detections
)
annotated_image = label_annotator.annotate(
    scene=annotated_image, detections=detections, labels=labels
)

sv.plot_image(annotated_image)

Label Data Automatically with YOLO-World

You can automatically label a dataset using YOLO-World with help from Autodistill, an open source package for training computer vision models. You can label a folder of images automatically with only a few lines of code. Below, see our tutorials that demonstrate how to use YOLO-World to train a computer vision model.

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