Update license and tasks docs (#2941)

docs/hub/inference_api.md

@@ -7,6 +7,9 @@ description: Explore Ultralytics YOLOv8 Inference API for efficient object detec
 
 The YOLO Inference API allows you to access the YOLOv8 object detection capabilities via a RESTful API. This enables you to run object detection on images without the need to install and set up the YOLOv8 environment locally.
 
+![Inference API Screenshot](https://github.com/ultralytics/ultralytics/assets/26833433/c0109ec0-7bb0-46e1-b0d2-bae687960a01)
+Screenshot of the Inference API section in the trained model Preview tab.
+
 ## API URL
 
 The API URL is the address used to access the YOLO Inference API. In this case, the base URL is:

docs/index.md

@@ -39,3 +39,12 @@ Explore the YOLOv8 Docs, a comprehensive resource designed to help you understan
 - [YOLOv6](https://github.com/meituan/YOLOv6) was open-sourced by [Meituan](https://about.meituan.com/) in 2022 and is in use in many of the company's autonomous delivery robots.
 - [YOLOv7](https://github.com/WongKinYiu/yolov7) added additional tasks such as pose estimation on the COCO keypoints dataset.
 - [YOLOv8](https://github.com/ultralytics/ultralytics) is the latest version of YOLO by Ultralytics. As a cutting-edge, state-of-the-art (SOTA) model, YOLOv8 builds on the success of previous versions, introducing new features and improvements for enhanced performance, flexibility, and efficiency. YOLOv8 supports a full range of vision AI tasks, including [detection](tasks/detect.md), [segmentation](tasks/segment.md), [pose estimation](tasks/pose.md), [tracking](modes/track.md), and [classification](tasks/classify.md). This versatility allows users to leverage YOLOv8's capabilities across diverse applications and domains.
+
+## YOLO Licenses: How is Ultralytics YOLO licensed?
+
+Ultralytics YOLO repositories like YOLOv3, YOLOv5, or YOLOv8 are available under two different licenses:
+
+- **AGPL-3.0 License**: See [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for details.
+- **Enterprise License**: Provides greater flexibility for commercial product development without the open-source requirements of AGPL-3.0. Typical use cases are embedding Ultralytics software and AI models in commercial products and applications. Request an Enterprise License at [Ultralytics Licensing](https://ultralytics.com/license).
+
+Please note our licensing approach ensures that any enhancements made to our open-source projects are shared back to the community. We firmly believe in the principles of open source, and we are committed to ensuring that our work can be used and improved upon in a manner that benefits everyone.

docs/tasks/classify.md

@@ -6,7 +6,8 @@ description: Check YOLO class label with only one class for the whole image, usi
 Image classification is the simplest of the three tasks and involves classifying an entire image into one of a set of
 predefined classes.
 
-<img width="1024" src="https://user-images.githubusercontent.com/26833433/212094133-6bb8c21c-3d47-41df-a512-81c5931054ae.png">
+<br>
+<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/tasks/im/banner-tasks.png">
 
 The output of an image classifier is a single class label and a confidence score. Image
 classification is useful when you need to know only what class an image belongs to and don't need to know where objects

docs/tasks/detect.md

@@ -5,7 +5,8 @@ description: Learn how to use YOLOv8, an object detection model pre-trained with
 
 Object detection is a task that involves identifying the location and class of objects in an image or video stream.
 
-<img width="1024" src="https://user-images.githubusercontent.com/26833433/212094133-6bb8c21c-3d47-41df-a512-81c5931054ae.png">
+<br>
+<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/tasks/im/banner-tasks.png">
 
 The output of an object detector is a set of bounding boxes that enclose the objects in the image, along with class labels and confidence scores for each box. Object detection is a good choice when you need to identify objects of interest in a scene, but don't need to know exactly where the object is or its exact shape.
 

docs/tasks/index.md

@@ -9,7 +9,8 @@ YOLOv8 is an AI framework that supports multiple computer vision **tasks**. The
 perform [detection](detect.md), [segmentation](segment.md), [classification](classify.md),
 and [pose](pose.md) estimation. Each of these tasks has a different objective and use case.
 
-<img width="1024" src="https://user-images.githubusercontent.com/26833433/212094133-6bb8c21c-3d47-41df-a512-81c5931054ae.png">
+<br>
+<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/tasks/im/banner-tasks.png">
 
 ## [Detection](detect.md)
 

docs/tasks/segment.md

@@ -6,7 +6,8 @@ description: Learn what Instance segmentation is. Get pretrained YOLOv8 segment
 Instance segmentation goes a step further than object detection and involves identifying individual objects in an image
 and segmenting them from the rest of the image.
 
-<img width="1024" src="https://user-images.githubusercontent.com/26833433/212094133-6bb8c21c-3d47-41df-a512-81c5931054ae.png">
+<br>
+<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/tasks/im/banner-tasks.png">
 
 The output of an instance segmentation model is a set of masks or
 contours that outline each object in the image, along with class labels and confidence scores for each object. Instance

examples/tutorial.ipynb

@@ -548,7 +548,7 @@
         "\n",
         "YOLOv8 can train, val, predict and export models for the most common tasks in vision AI: [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/), [Classify](https://docs.ultralytics.com/tasks/classify/) and [Pose](https://docs.ultralytics.com/tasks/pose/). See [YOLOv8 Tasks Docs](https://docs.ultralytics.com/tasks/) for more information.\n",
         "\n",
-        "<img width=\"1024\" src=\"https://user-images.githubusercontent.com/26833433/212094133-6bb8c21c-3d47-41df-a512-81c5931054ae.png\">\n"
+        "<br><img width=\"1024\" src=\"https://raw.githubusercontent.com/ultralytics/assets/tasks/im/banner-tasks.png\">\n"
       ],
       "metadata": {
         "id": "Phm9ccmOKye5"

mkdocs.yml

@@ -312,6 +312,7 @@ nav:
               - loss: reference/yolo/utils/loss.md
               - metrics: reference/yolo/utils/metrics.md
               - ops: reference/yolo/utils/ops.md
+              - patches: reference/yolo/utils/patches.md
               - plotting: reference/yolo/utils/plotting.md
               - tal: reference/yolo/utils/tal.md
               - torch_utils: reference/yolo/utils/torch_utils.md

ultralytics/yolo/data/dataloaders/stream_loaders.py

@@ -348,7 +348,7 @@ def get_best_youtube_url(url, use_pafy=True):
         use_pafy (bool): Use the pafy package, default=True, otherwise use yt_dlp package.
 
     Returns:
-        str: The URL of the best quality MP4 video stream, or None if no suitable stream is found.
+        (str): The URL of the best quality MP4 video stream, or None if no suitable stream is found.
     """
     if use_pafy:
         check_requirements(('pafy', 'youtube_dl==2020.12.2'))