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---
comments: true
description: Learn to use Gradio and Ultralytics YOLOv8 for interactive object detection. Upload images and adjust detection parameters in real-time.
keywords: Gradio, Ultralytics YOLOv8, object detection, interactive AI, Python
---
# 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](https://github.com/ultralytics/ultralytics/) model. Users can upload images and adjust parameters like confidence threshold and intersection-over-union (IoU) threshold to get real-time detection results.
## 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.
<p align="center">
<img width="800" alt="Gradio example screenshot" src="https://github.com/RizwanMunawar/ultralytics/assets/26833433/52ee3cd2-ac59-4c27-9084-0fd05c6c33be">
</p>
## How to Install the Gradio
```bash
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.
```python
import PIL.Image as Image
import gradio as gr
from ultralytics import ASSETS, YOLO
model = YOLO("yolov8n.pt")
def predict_image(img, conf_threshold, iou_threshold):
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. |
|