Add `integrations/gradio` Docs page (#7935)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: WangQvQ <1579093407@qq.com>
Co-authored-by: Martin Pl <martin-plank@gmx.de>
Co-authored-by: Mactarvish <Mactarvish@users.noreply.github.com>

docs/en/datasets/obb/dota-v2.md

@@ -119,7 +119,7 @@ To train a model on the DOTA v1 dataset, you can utilize the following code snip
 
         ```bash
         # Train a new YOLOv8n-OBB model on the DOTAv2 dataset
-        yolo detect train data=DOTAv1.yaml model=yolov8n.pt epochs=100 imgsz=640
+        yolo obb train data=DOTAv1.yaml model=yolov8n-obb.pt epochs=100 imgsz=640
         ```
 
 ## Sample Data and Annotations

docs/en/integrations/gradio.md

@@ -0,0 +1,104 @@
+---
+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.
+
+<img width="800" alt="Gradio example screenshot" src="https://github.com/WangQvQ/ultralytics/assets/58406737/5d906f10-fd62-4bcc-8856-ef3233102c1d">
+
+## 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.        |

docs/en/integrations/index.md

@@ -40,6 +40,8 @@ Welcome to the Ultralytics Integrations page! This page provides an overview of
 
 - [Neural Magic](neural-magic.md): Leverage Quantization Aware Training (QAT) and pruning techniques to optimize Ultralytics models for superior performance and leaner size.
 
+- [Gradio](../integrations/gradio.md) 🚀 NEW: Deploy Ultralytics models with Gradio for real-time, interactive object detection demos.
+
 - [OpenVINO](openvino.md): Intel's toolkit for optimizing and deploying computer vision models efficiently across various Intel CPU and GPU platforms.
 
 - [ONNX](onnx.md): An open-source format created by [Microsoft](https://www.microsoft.com) for facilitating the transfer of AI models between various frameworks, enhancing the versatility and deployment flexibility of Ultralytics models.

mkdocs.yml

@@ -345,6 +345,7 @@ nav:
       - DVC: integrations/dvc.md
       - Weights & Biases: integrations/weights-biases.md
       - Neural Magic: integrations/neural-magic.md
+      - Gradio: integrations/gradio.md
       - TensorBoard: integrations/tensorboard.md
       - Amazon SageMaker: integrations/amazon-sagemaker.md
   - HUB:

ultralytics/utils/metrics.py

@@ -701,7 +701,7 @@ class Metric(SimpleClass):
         Returns the mean Average Precision (mAP) at an IoU threshold of 0.5.
 
         Returns:
-            (float): The mAP50 at an IoU threshold of 0.5.
+            (float): The mAP at an IoU threshold of 0.5.
         """
         return self.all_ap[:, 0].mean() if len(self.all_ap) else 0.0
 
@@ -711,7 +711,7 @@ class Metric(SimpleClass):
         Returns the mean Average Precision (mAP) at an IoU threshold of 0.75.
 
         Returns:
-            (float): The mAP50 at an IoU threshold of 0.75.
+            (float): The mAP at an IoU threshold of 0.75.
         """
         return self.all_ap[:, 5].mean() if len(self.all_ap) else 0.0