ultralytics/docs/en/integrations/gradio.md

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true	Discover an interactive way to perform object detection with Ultralytics YOLOv8 using Gradio. Upload images and adjust settings for real-time results.	Ultralytics, YOLOv8, Gradio, object detection, interactive, real-time, image processing, AI

Interactive Object Detection: Gradio & Ultralytics YOLOv8 🚀

Introduction to Interactive Object Detection

This Gradio interface provides an easy and interactive way to perform object detection using the Ultralytics YOLOv8 model. Users can upload images and adjust parameters like confidence threshold and intersection-over-union (IoU) threshold to get real-time detection results.

Watch: Gradio Integration with Ultralytics YOLOv8

Why Use Gradio for Object Detection?

• User-Friendly Interface: Gradio offers a straightforward platform for users to upload images and visualize detection results without any coding requirement.
• Real-Time Adjustments: Parameters such as confidence and IoU thresholds can be adjusted on the fly, allowing for immediate feedback and optimization of detection results.
• Broad Accessibility: The Gradio web interface can be accessed by anyone, making it an excellent tool for demonstrations, educational purposes, and quick experiments.

How to Install the Gradio

pip install gradio

How to Use the Interface

1. Upload Image: Click on 'Upload Image' to choose an image file for object detection.
2. Adjust Parameters:
   • Confidence Threshold: Slider to set the minimum confidence level for detecting objects.
   • IoU Threshold: Slider to set the IoU threshold for distinguishing different objects.
3. View Results: The processed image with detected objects and their labels will be displayed.

Example Use Cases

• Sample Image 1: Bus detection with default thresholds.
• Sample Image 2: Detection on a sports image with default thresholds.

Usage Example

This section provides the Python code used to create the Gradio interface with the Ultralytics YOLOv8 model. Supports classification tasks, detection tasks, segmentation tasks, and key point tasks.

import gradio as gr
import PIL.Image as Image

from ultralytics import ASSETS, YOLO

model = YOLO("yolov8n.pt")


def predict_image(img, conf_threshold, iou_threshold):
    """Predicts and plots labeled objects in an image using YOLOv8 model with adjustable confidence and IOU thresholds."""
    results = model.predict(
        source=img,
        conf=conf_threshold,
        iou=iou_threshold,
        show_labels=True,
        show_conf=True,
        imgsz=640,
    )

    for r in results:
        im_array = r.plot()
        im = Image.fromarray(im_array[..., ::-1])

    return im


iface = gr.Interface(
    fn=predict_image,
    inputs=[
        gr.Image(type="pil", label="Upload Image"),
        gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence threshold"),
        gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold"),
    ],
    outputs=gr.Image(type="pil", label="Result"),
    title="Ultralytics Gradio",
    description="Upload images for inference. The Ultralytics YOLOv8n model is used by default.",
    examples=[
        [ASSETS / "bus.jpg", 0.25, 0.45],
        [ASSETS / "zidane.jpg", 0.25, 0.45],
    ],
)

if __name__ == "__main__":
    iface.launch()

Parameters Explanation

Parameter Name	Type	Description
img	Image	The image on which object detection will be performed.
conf_threshold	float	Confidence threshold for detecting objects.
iou_threshold	float	Intersection-over-union threshold for object separation.

Gradio Interface Components

Component	Description
Image Input	To upload the image for detection.
Sliders	To adjust confidence and IoU thresholds.
Image Output	To display the detection results.