From 96fb5ee78ecff3b0dfa739b4140f5d70bbdfd61b Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Thu, 1 Jun 2023 00:42:55 +0200 Subject: [PATCH] Update license and tasks docs (#2941) --- docs/hub/inference_api.md | 5 ++++- docs/index.md | 11 ++++++++++- docs/tasks/classify.md | 3 ++- docs/tasks/detect.md | 3 ++- docs/tasks/index.md | 3 ++- docs/tasks/segment.md | 3 ++- examples/tutorial.ipynb | 2 +- mkdocs.yml | 1 + ultralytics/yolo/data/dataloaders/stream_loaders.py | 2 +- 9 files changed, 25 insertions(+), 8 deletions(-) diff --git a/docs/hub/inference_api.md b/docs/hub/inference_api.md index 4bc8f99e16..2244beb1dc 100644 --- a/docs/hub/inference_api.md +++ b/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: @@ -451,4 +454,4 @@ YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from loca } ] } - ``` \ No newline at end of file + ``` diff --git a/docs/index.md b/docs/index.md index 73212a770c..1d835df587 100644 --- a/docs/index.md +++ b/docs/index.md @@ -38,4 +38,13 @@ Explore the YOLOv8 Docs, a comprehensive resource designed to help you understan - [YOLOv5](https://github.com/ultralytics/yolov5) further improved the model's performance and added new features such as hyperparameter optimization, integrated experiment tracking and automatic export to popular export formats. - [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. \ No newline at end of file +- [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. diff --git a/docs/tasks/classify.md b/docs/tasks/classify.md index fbdd96b361..ed14b376ad 100644 --- a/docs/tasks/classify.md +++ b/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. - +
+ 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 diff --git a/docs/tasks/detect.md b/docs/tasks/detect.md index 44a18c7d9c..10149bbe71 100644 --- a/docs/tasks/detect.md +++ b/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. - +
+ 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. diff --git a/docs/tasks/index.md b/docs/tasks/index.md index 0931a3f5e4..253443117d 100644 --- a/docs/tasks/index.md +++ b/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. - +
+ ## [Detection](detect.md) diff --git a/docs/tasks/segment.md b/docs/tasks/segment.md index b12f83e0f8..9bcc90be3f 100644 --- a/docs/tasks/segment.md +++ b/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. - +
+ 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 diff --git a/examples/tutorial.ipynb b/examples/tutorial.ipynb index 82fbc4a2c5..9842bf7c22 100644 --- a/examples/tutorial.ipynb +++ b/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", - "\n" + "
\n" ], "metadata": { "id": "Phm9ccmOKye5" diff --git a/mkdocs.yml b/mkdocs.yml index 2763b74b87..57d8809391 100644 --- a/mkdocs.yml +++ b/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 diff --git a/ultralytics/yolo/data/dataloaders/stream_loaders.py b/ultralytics/yolo/data/dataloaders/stream_loaders.py index c7b4ca5851..400cee69f4 100644 --- a/ultralytics/yolo/data/dataloaders/stream_loaders.py +++ b/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'))