v0.1.1 - Initial release

ONNX-YOLO is a Python package for running inference on YOLO-WORLD open-vocabulary object detection models using ONNX runtime.

Installation

You can install YOLO-World-ONNX using pip:

pip install yolo-world-onnx

Usage

Inference

Here's an example of how to perform inference using YOLO-World-ONNX:

import cv2 as cv
from yolo_world_onnx import YOLOWORLD

# Load the YOLO model
model_path = "path/to/your/model.onnx"

# Select a device 0 for GPU and for a CPU is cpu
model = YOLOWORLD(model_path, device="0")

# Set the class names
class_names = ["person", "car", "dog", "cat"]
model.set_classes(class_names)

# Retrieve the names
names = model.names

# Load an image
image = cv.imread("path/to/your/image.jpg")

# Perform object detection
boxes, scores, class_ids = model(image, conf=0.35, imgsz=640, iou=0.7)

# Process the results
for box, score, class_id in zip(boxes, scores, class_ids):
    x, y, w, h = box
    x1, y1 = int(x - w / 2), int(y - h / 2)
    x2, y2 = int(x + w / 2), int(y + h / 2)
    class_name = names[class_id]
    print(f"Detected {class_name} with confidence {score:.2f} at coordinates (x1={x1}, y1={y1}, x2={x2}, y2={y2})")
    ```