`ultralytics 8.0.97` confusion matrix, windows, docs updates (#2511)

Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>
Co-authored-by: Dowon <ks2515@naver.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>

.github/workflows/publish.yml

@@ -23,6 +23,8 @@ jobs:
     steps:
       - name: Checkout code
         uses: actions/checkout@v3
+        with:
+          fetch-depth: "0" # pulls all commits (needed correct last updated dates in Docs)
       - name: Set up Python environment
         uses: actions/setup-python@v4
         with:

docs/README.md

@@ -1,3 +1,7 @@
+---
+description: Learn how to install the Ultralytics package in developer mode and build/serve locally using MkDocs. Deploy your project to your host easily.
+---
+
 # Ultralytics Docs
 
 Ultralytics Docs are deployed to [https://docs.ultralytics.com](https://docs.ultralytics.com).

docs/SECURITY.md

@@ -1,3 +1,7 @@
+---
+description: Learn how Ultralytics prioritize security. Get insights into Snyk and GitHub CodeQL scans, and how to report security issues in YOLOv8.
+---
+
 # Security Policy
 
 At [Ultralytics](https://ultralytics.com), the security of our users' data and systems is of utmost importance. To

docs/datasets/classify/index.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: Learn how torchvision organizes classification image datasets. Use this code to create and train models. CLI and Python code shown.
 ---
 
 # Image Classification Datasets Overview
@@ -77,6 +78,7 @@ cifar-10-/
 In this example, the `train` directory contains subdirectories for each class in the dataset, and each class subdirectory contains all the images for that class. The `test` directory has a similar structure. The `root` directory also contains other files that are part of the CIFAR10 dataset.
 
 ## Usage
+
 !!! example ""
 
     === "Python"
@@ -98,4 +100,5 @@ In this example, the `train` directory contains subdirectories for each class in
         ```
 
 ## Supported Datasets
+
 TODO

docs/datasets/detect/coco.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: Learn about the COCO dataset, designed to encourage research on object detection, segmentation, and captioning with standardized evaluation metrics.
 ---
 
 # COCO Dataset

docs/datasets/detect/index.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: Learn about supported dataset formats for training YOLO detection models, including Ultralytics YOLO and COCO, in this Object Detection Datasets Overview.
 ---
 
 # Object Detection Datasets Overview
@@ -20,6 +21,7 @@ The dataset format used for training YOLO detection models is as follows:
     - Object width and height: The width and height of the object, normalized to be between 0 and 1.
 
 The format for a single row in the detection dataset file is as follows:
+
 ```
 <object-class> <x> <y> <width> <height>
 ```
@@ -55,6 +57,7 @@ The `names` field is a list of the names of the object classes. The order of the
 NOTE: Either `nc` or `names` must be defined. Defining both are not mandatory
 
 Alternatively, you can directly define class names like this:
+
 ```yaml
 names:
   0: person
@@ -72,6 +75,7 @@ names: ['person', 'car']
 ```
 
 ## Usage
+
 !!! example ""
 
     === "Python"
@@ -93,6 +97,7 @@ names: ['person', 'car']
         ```
 
 ## Supported Datasets
+
 TODO
 
 ## Port or Convert label formats

docs/datasets/index.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: Ultralytics provides support for various datasets to facilitate multiple computer vision tasks. Check out our list of main datasets and their summaries.
 ---
 
 # Datasets Overview

docs/datasets/pose/index.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: Learn how to format your dataset for training YOLO models with Ultralytics YOLO format using our concise tutorial and example YAML files.
 ---
 
 # Pose Estimation Datasets Overview
@@ -25,16 +26,16 @@ Here is an example of the label format for pose estimation task:
 Format with Dim = 2
 
 ```
-<class-index> <x> <y> <width> <height> <px1> <py1> <px2> <py2>  <pxn> <pyn>
+<class-index> <x> <y> <width> <height> <px1> <py1> <px2> <py2> ... <pxn> <pyn>
 ```
+
 Format with Dim = 3
 
 ```
 <class-index> <x> <y> <width> <height> <px1> <py1> <p1-visibility> <px2> <py2> <p2-visibility> <pxn> <pyn> <p2-visibility>
 ```
 
-In this format, `<class-index>` is the index of the class for the object,`<x> <y> <width> <height>` are coordinates of boudning box, and `<px1> <py1> <px2> <py2>  <pxn> <pyn>` are the pixel coordinates of the keypoints. The coordinates are separated by spaces. 
+In this format, `<class-index>` is the index of the class for the object,`<x> <y> <width> <height>` are coordinates of boudning box, and `<px1> <py1> <px2> <py2> ... <pxn> <pyn>` are the pixel coordinates of the keypoints. The coordinates are separated by spaces.
-
 
 ** Dataset file format **
 
@@ -62,6 +63,7 @@ The `names` field is a list of the names of the object classes. The order of the
 NOTE: Either `nc` or `names` must be defined. Defining both are not mandatory
 
 Alternatively, you can directly define class names like this:
+
 ```
 names:
   0: person
@@ -86,6 +88,7 @@ flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
 ```
 
 ## Usage
+
 !!! example ""
 
     === "Python"
@@ -107,6 +110,7 @@ flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
         ```
 
 ## Supported Datasets
+
 TODO
 
 ## Port or Convert label formats

docs/datasets/segment/index.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: Learn about the Ultralytics YOLO dataset format for segmentation models. Use YAML to train Detection Models. Convert COCO to YOLO format using Python.
 ---
 
 # Instance Segmentation Datasets Overview
@@ -32,6 +33,7 @@ Here is an example of the YOLO dataset format for a single image with two object
 0 0.6812 0.48541 0.67 0.4875 0.67656 0.487 0.675 0.489 0.66
 1 0.5046 0.0 0.5015 0.004 0.4984 0.00416 0.4937 0.010 0.492 0.0104
 ```
+
 Note: The length of each row does not have to be equal.
 
 ** Dataset file format **
@@ -56,6 +58,7 @@ The `names` field is a list of the names of the object classes. The order of the
 NOTE: Either `nc` or `names` must be defined. Defining both are not mandatory.
 
 Alternatively, you can directly define class names like this:
+
 ```yaml
 names:
   0: person
@@ -73,6 +76,7 @@ names: ['person', 'car']
 ```
 
 ## Usage
+
 !!! example ""
 
     === "Python"

docs/datasets/track/index.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: Discover the datasets compatible with Multi-Object Detector. Train your trackers and make your detections more efficient with Ultralytics' YOLO.
 ---
 
 # Multi-object Tracking Datasets Overview
@@ -26,4 +27,3 @@ Support for training trackers alone is coming soon
         ```bash
         yolo track model=yolov8n.pt source="https://youtu.be/Zgi9g1ksQHc" conf=0.3, iou=0.5 show
         ```
-

docs/help/CLA.md

@@ -1,3 +1,7 @@
+---
+description: Individual Contributor License Agreement. Settle Intellectual Property issues for Contributions made to anything open source released by Ultralytics.
+---
+
 # Ultralytics Individual Contributor License Agreement
 
 Thank you for your interest in contributing to open source software projects (“Projects”) made available by Ultralytics

docs/help/FAQ.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: 'Get quick answers to common Ultralytics YOLO questions: Hardware requirements, fine-tuning, conversion, real-time detection, and accuracy tips.'
 ---
 
 # Ultralytics YOLO Frequently Asked Questions (FAQ)

docs/help/code_of_conduct.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: Read the Ultralytics Contributor Covenant Code of Conduct. Learn ways to create a welcoming community & consequences for inappropriate conduct.
 ---
 
 # Ultralytics Contributor Covenant Code of Conduct

docs/help/contributing.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: Learn how to contribute to Ultralytics Open-Source YOLO Repositories with contributions guidelines, pull requests requirements, and GitHub CI tests.
 ---
 
 # Contributing to Ultralytics Open-Source YOLO Repositories

docs/help/index.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: Get comprehensive resources for Ultralytics YOLO repositories. Find guides, FAQs, MRE creation, CLA & more. Join the supportive community now!
 ---
 
 Welcome to the Ultralytics Help page! We are committed to providing you with comprehensive resources to make your experience with Ultralytics YOLO repositories as smooth and enjoyable as possible. On this page, you'll find essential links to guides and documents that will help you navigate through common tasks and address any questions you might have while using our repositories.

docs/help/minimum_reproducible_example.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: Learn how to create a Minimum Reproducible Example (MRE) for Ultralytics YOLO bug reports to help maintainers and contributors understand your issue better.
 ---
 
 # Creating a Minimum Reproducible Example for Bug Reports in Ultralytics YOLO Repositories

docs/hub/app/android.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: Run YOLO models on your Android device for real-time object detection with Ultralytics Android App. Utilizes TensorFlow Lite and hardware delegates.
 ---
 
 # Ultralytics Android App: Real-time Object Detection with YOLO Models

docs/hub/app/index.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: Experience the power of YOLOv5 and YOLOv8 models with Ultralytics HUB app. Download from Google Play and App Store now.
 ---
 
 # Ultralytics HUB App

docs/hub/app/ios.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: Get started with the Ultralytics iOS app and run YOLO models in real-time for object detection on your iPhone or iPad with the Apple Neural Engine.
 ---
 
 # Ultralytics iOS App: Real-time Object Detection with YOLO Models
@@ -33,7 +34,6 @@ By combining quantized YOLO models with the Apple Neural Engine, the Ultralytics
 | 2021         | [iPhone 13](https://en.wikipedia.org/wiki/IPhone_13) | [A15 Bionic](https://en.wikipedia.org/wiki/Apple_A15) | 5 nm      | 15.8     |
 | 2022         | [iPhone 14](https://en.wikipedia.org/wiki/IPhone_14) | [A16 Bionic](https://en.wikipedia.org/wiki/Apple_A16) | 4 nm      | 17.0     |
 
-
 Please note that this list only includes iPhone models from 2017 onwards, and the ANE TOPs values are approximate.
 
 ## Getting Started with the Ultralytics iOS App

docs/hub/datasets.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: Upload custom datasets to Ultralytics HUB for YOLOv5 and YOLOv8 models. Follow YAML structure, zip and upload. Scan & train new models.
 ---
 
 # HUB Datasets

docs/hub/index.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: 'Ultralytics HUB: Train & deploy YOLO models from one spot! Use drag-and-drop interface with templates & pre-training models. Check quickstart, datasets, and more.'
 ---
 
 # Ultralytics HUB
@@ -20,7 +21,6 @@ comments: true
 launch [Ultralytics HUB](https://bit.ly/ultralytics_hub), a new web tool for training and deploying all your YOLOv5 and YOLOv8 🚀
 models from one spot!
 
-
 ## Introduction
 
 HUB is designed to be user-friendly and intuitive, with a drag-and-drop interface that allows users to

docs/hub/inference_api.md

@@ -6,7 +6,6 @@ comments: true
 
 This page is currently under construction!️ 👷Please check back later for updates. 😃🔜
 
-
 # YOLO Inference API
 
 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.
@@ -45,7 +44,6 @@ print(response.json())
 
 In this example, replace `API_KEY` with your actual API key, `MODEL_ID` with the desired model ID, and `path/to/image.jpg` with the path to the image you want to analyze.
 
-
 ## Example Usage with CLI
 
 You can use the YOLO Inference API with the command-line interface (CLI) by utilizing the `curl` command. Replace `API_KEY` with your actual API key, `MODEL_ID` with the desired model ID, and `image.jpg` with the path to the image you want to analyze:
@@ -334,7 +332,6 @@ YOLO segmentation models, such as `yolov8n-seg.pt`, can return JSON responses fr
         }
         ```
 
-
 ### Pose Model Format
 
 YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from local inference, CLI API inference, and Python API inference. All of these methods produce the same JSON response format.

docs/hub/models.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: Train and Deploy your Model to 13 different formats, including TensorFlow, ONNX, OpenVINO, CoreML, Paddle or directly on Mobile.
 ---
 
 # HUB Models
@@ -11,7 +12,6 @@ Connect to the Ultralytics HUB notebook and use your model API key to begin trai
 <a href="https://colab.research.google.com/github/ultralytics/hub/blob/master/hub.ipynb" target="_blank">
 <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
 
-
 ## Deploy to Real World
 
 Export your model to 13 different formats, including TensorFlow, ONNX, OpenVINO, CoreML, Paddle and many others. Run

docs/index.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: Explore Ultralytics YOLOv8, a cutting-edge real-time object detection and image segmentation model for various applications and hardware platforms.
 ---
 
 <div align="center">

docs/models/index.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: Learn about the supported models and architectures, such as YOLOv3, YOLOv5, and YOLOv8, and how to contribute your own model to Ultralytics.
 ---
 
 # Models

docs/models/sam.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: Learn about the Vision Transformer (ViT) and segment anything with SAM models. Train and use pre-trained models with Python API.
 ---
 
 # Vision Transformers
@@ -9,11 +10,11 @@ Vit models currently support Python environment:
 ```python
 from ultralytics.vit import SAM
 
-# from ultralytics.vit import MODEL_TYPe
+# from ultralytics.vit import MODEL_TYPE
 
 model = SAM("sam_b.pt")
 model.info()  # display model information
-model.predict(...)  # train the model
+model.predict(...)  # predict
 ```
 
 # Segment Anything

docs/models/yolov5.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: Detect objects faster and more accurately using Ultralytics YOLOv5u. Find pre-trained models for each task, including Inference, Validation and Training.
 ---
 
 # YOLOv5u

docs/models/yolov8.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: Learn about YOLOv8's pre-trained weights supporting detection, instance segmentation, pose, and classification tasks. Get performance details.
 ---
 
 # YOLOv8

docs/modes/benchmark.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: Benchmark mode compares speed and accuracy of various YOLOv8 export formats like ONNX or OpenVINO. Optimize formats for speed or accuracy.
 ---
 
 <img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">

docs/modes/export.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: 'Export mode: Create a deployment-ready YOLOv8 model by converting it to various formats. Export to ONNX or OpenVINO for up to 3x CPU speedup.'
 ---
 
 <img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">

docs/modes/index.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: Use Ultralytics YOLOv8 Modes (Train, Val, Predict, Export, Track, Benchmark) to train, validate, predict, track, export or benchmark.
 ---
 
 # Ultralytics YOLOv8 Modes

docs/modes/predict.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: Get started with YOLOv8 Predict mode and input sources. Accepts various input sources such as images, videos, and directories.
 ---
 
 <img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
@@ -58,10 +59,11 @@ whether each source can be used in streaming mode with `stream=True` ✅ and an
 | YouTube ✅   | `'https://youtu.be/Zgi9g1ksQHc'`           | `str`          |                  |
 | stream ✅    | `'rtsp://example.com/media.mp4'`           | `str`          | RTSP, RTMP, HTTP |
 
-
 ## Arguments
+
 `model.predict` accepts multiple arguments that control the prediction operation. These arguments can be passed directly to `model.predict`:
 !!! example
+
     ```
     model.predict(source, save=True, imgsz=320, conf=0.5)
     ```
@@ -220,6 +222,7 @@ masks, classification logits, etc.) found in the results object
     res_plotted = res[0].plot()
     cv2.imshow("result", res_plotted)
     ```
+
 | Argument                      | Description                                                                            |
 |-------------------------------|----------------------------------------------------------------------------------------|
 | `conf (bool)`                 | Whether to plot the detection confidence score.                                        |
@@ -234,7 +237,6 @@ masks, classification logits, etc.) found in the results object
 | `masks (bool)`                | Whether to plot the masks.                                                             |
 | `probs (bool)`                | Whether to plot classification probability.                                            |
 
-
 ## Streaming Source `for`-loop
 
 Here's a Python script using OpenCV (cv2) and YOLOv8 to run inference on video frames. This script assumes you have already installed the necessary packages (opencv-python and ultralytics).

docs/modes/val.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: Validate and improve YOLOv8n model accuracy on COCO128 and other datasets using hyperparameter & configuration tuning, in Val mode.
 ---
 
 <img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">

docs/quickstart.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+description: Install and use YOLOv8 via CLI or Python. Run single-line commands or integrate with Python projects for object detection, segmentation, and classification.
 ---
 
 ## Install
@@ -32,13 +33,11 @@ See the `ultralytics` [requirements.txt](https://github.com/ultralytics/ultralyt
         <img width="800" alt="PyTorch Installation Instructions" src="https://user-images.githubusercontent.com/26833433/228650108-ab0ec98a-b328-4f40-a40d-95355e8a84e3.png">
     </a>
 
-
 ## Use with CLI
 
 The YOLO command line interface (CLI) allows for simple single-line commands without the need for a Python environment.
 CLI requires no customization or Python code. You can simply run all tasks from the terminal with the `yolo` command. Check out the [CLI Guide](usage/cli.md) to learn more about using YOLOv8 from the command line.
 
-
 !!! example
 
     === "Syntax"
@@ -93,7 +92,6 @@ CLI requires no customization or Python code. You can simply run all tasks from
         yolo cfg
         ```
 
-
 !!! warning "Warning"
 
     Arguments must be passed as `arg=val` pairs, split by an equals `=` sign and delimited by spaces ` ` between pairs. Do not use `--` argument prefixes or commas `,` between arguments.

docs/reference/hub/auth.md

@@ -1,3 +1,7 @@
+---
+description: Learn how to use Ultralytics hub authentication in your projects with examples and guidelines from the Auth page on Ultralytics Docs.
+---
+
 # Auth
 ---
 :::ultralytics.hub.auth.Auth

docs/reference/hub/session.md

@@ -1,3 +1,7 @@
+---
+description: Accelerate your AI development with the Ultralytics HUB Training Session. High-performance training of object detection models.
+---
+
 # HUBTrainingSession
 ---
 :::ultralytics.hub.session.HUBTrainingSession

docs/reference/hub/utils.md

@@ -1,3 +1,7 @@
+---
+description: Explore Ultralytics events, including 'request_with_credentials' and 'smart_request', to improve your project's performance and efficiency.
+---
+
 # Events
 ---
 :::ultralytics.hub.utils.Events

docs/reference/nn/autobackend.md

@@ -1,3 +1,7 @@
+---
+description: Ensure class names match filenames for easy imports. Use AutoBackend to automatically rename and refactor model files.
+---
+
 # AutoBackend
 ---
 :::ultralytics.nn.autobackend.AutoBackend

docs/reference/nn/autoshape.md

@@ -1,3 +1,7 @@
+---
+description: Detect 80+ object categories with bounding box coordinates and class probabilities using AutoShape in Ultralytics YOLO. Explore Detections now.
+---
+
 # AutoShape
 ---
 :::ultralytics.nn.autoshape.AutoShape

docs/reference/nn/modules.md

@@ -1,3 +1,7 @@
+---
+description: Explore Ultralytics neural network modules for convolution, attention, detection, pose, and classification in PyTorch.
+---
+
 # Conv
 ---
 :::ultralytics.nn.modules.Conv

docs/reference/nn/tasks.md

@@ -1,3 +1,7 @@
+---
+description: Learn how to work with Ultralytics YOLO Detection, Segmentation & Classification Models, load weights and parse models in PyTorch.
+---
+
 # BaseModel
 ---
 :::ultralytics.nn.tasks.BaseModel

docs/reference/tracker/track.md

@@ -1,3 +1,7 @@
+---
+description: Learn how to register custom event-tracking and track predictions with Ultralytics YOLO via on_predict_start and register_tracker methods.
+---
+
 # on_predict_start
 ---
 :::ultralytics.tracker.track.on_predict_start

docs/reference/tracker/trackers/basetrack.md

@@ -1,3 +1,7 @@
+---
+description: 'TrackState: A comprehensive guide to Ultralytics tracker''s BaseTrack for monitoring model performance. Improve your tracking capabilities now!'
+---
+
 # TrackState
 ---
 :::ultralytics.tracker.trackers.basetrack.TrackState

docs/reference/tracker/trackers/bot_sort.md

@@ -1,3 +1,7 @@
+---
+description: '"Optimize tracking with Ultralytics BOTrack. Easily sort and track bots with BOTSORT. Streamline data collection for improved performance."'
+---
+
 # BOTrack
 ---
 :::ultralytics.tracker.trackers.bot_sort.BOTrack

docs/reference/tracker/trackers/byte_tracker.md

@@ -1,3 +1,7 @@
+---
+description: Learn how to track ByteAI model sizes and tips for model optimization with STrack, a byte tracking tool from Ultralytics.
+---
+
 # STrack
 ---
 :::ultralytics.tracker.trackers.byte_tracker.STrack

docs/reference/tracker/utils/gmc.md

@@ -1,3 +1,7 @@
+---
+description: '"Track Google Marketing Campaigns in GMC with Ultralytics Tracker. Learn to set up and use GMC for detailed analytics. Get started now."'
+---
+
 # GMC
 ---
 :::ultralytics.tracker.utils.gmc.GMC

docs/reference/tracker/utils/kalman_filter.md

@@ -1,3 +1,7 @@
+---
+description: Improve object tracking with KalmanFilterXYAH in Ultralytics YOLO - an efficient and accurate algorithm for state estimation.
+---
+
 # KalmanFilterXYAH
 ---
 :::ultralytics.tracker.utils.kalman_filter.KalmanFilterXYAH

docs/reference/tracker/utils/matching.md

@@ -1,3 +1,7 @@
+---
+description: Learn how to match and fuse object detections for accurate target tracking using Ultralytics' YOLO merge_matches, iou_distance, and embedding_distance.
+---
+
 # merge_matches
 ---
 :::ultralytics.tracker.utils.matching.merge_matches

docs/reference/yolo/data/annotator.md

@@ -1,3 +1,7 @@
+---
+description: Learn how to use auto_annotate in Ultralytics YOLO to generate annotations automatically for your dataset. Simplify object detection workflows.
+---
+
 # auto_annotate
 ---
 :::ultralytics.yolo.data.annotator.auto_annotate

docs/reference/yolo/data/augment.md

@@ -1,3 +1,7 @@
+---
+description: Use Ultralytics YOLO Data Augmentation transforms with Base, MixUp, and Albumentations for object detection and classification.
+---
+
 # BaseTransform
 ---
 :::ultralytics.yolo.data.augment.BaseTransform

docs/reference/yolo/data/base.md

@@ -1,3 +1,7 @@
+---
+description: Learn about BaseDataset in Ultralytics YOLO, a flexible dataset class for object detection. Maximize your YOLO performance with custom datasets.
+---
+
 # BaseDataset
 ---
 :::ultralytics.yolo.data.base.BaseDataset

docs/reference/yolo/data/build.md

@@ -1,3 +1,7 @@
+---
+description: Maximize YOLO performance with Ultralytics' InfiniteDataLoader, seed_worker, build_dataloader, and load_inference_source functions.
+---
+
 # InfiniteDataLoader
 ---
 :::ultralytics.yolo.data.build.InfiniteDataLoader

docs/reference/yolo/data/converter.md

@@ -1,3 +1,7 @@
+---
+description: Convert COCO-91 to COCO-80 class, RLE to polygon, and merge multi-segment images with Ultralytics YOLO data converter. Improve your object detection.
+---
+
 # coco91_to_coco80_class
 ---
 :::ultralytics.yolo.data.converter.coco91_to_coco80_class

docs/reference/yolo/data/dataloaders/stream_loaders.md

@@ -1,3 +1,7 @@
+---
+description: 'Ultralytics YOLO Docs: Learn about stream loaders for image and tensor data, as well as autocasting techniques. Check out SourceTypes and more.'
+---
+
 # SourceTypes
 ---
 :::ultralytics.yolo.data.dataloaders.stream_loaders.SourceTypes

docs/reference/yolo/data/dataloaders/v5augmentations.md

@@ -1,3 +1,7 @@
+---
+description: Enhance image data with Albumentations CenterCrop, normalize, augment_hsv, replicate, random_perspective, cutout, & box_candidates.
+---
+
 # Albumentations
 ---
 :::ultralytics.yolo.data.dataloaders.v5augmentations.Albumentations

docs/reference/yolo/data/dataloaders/v5loader.md

@@ -1,3 +1,7 @@
+---
+description: Efficiently load images and labels to models using Ultralytics YOLO's InfiniteDataLoader, LoadScreenshots, and LoadStreams.
+---
+
 # InfiniteDataLoader
 ---
 :::ultralytics.yolo.data.dataloaders.v5loader.InfiniteDataLoader

docs/reference/yolo/data/dataset.md

@@ -1,3 +1,7 @@
+---
+description: Create custom YOLOv5 datasets with Ultralytics YOLODataset and SemanticDataset. Streamline your object detection and segmentation projects.
+---
+
 # YOLODataset
 ---
 :::ultralytics.yolo.data.dataset.YOLODataset

docs/reference/yolo/data/dataset_wrappers.md

@@ -1,3 +1,7 @@
+---
+description: Create a custom dataset of mixed and oriented rectangular objects with Ultralytics YOLO's MixAndRectDataset.
+---
+
 # MixAndRectDataset
 ---
 :::ultralytics.yolo.data.dataset_wrappers.MixAndRectDataset

docs/reference/yolo/data/utils.md

@@ -1,3 +1,7 @@
+---
+description: Efficiently handle data in YOLO with Ultralytics. Utilize HUBDatasetStats and customize dataset with these data utility functions.
+---
+
 # HUBDatasetStats
 ---
 :::ultralytics.yolo.data.utils.HUBDatasetStats

docs/reference/yolo/engine/exporter.md

@@ -1,3 +1,7 @@
+---
+description: Learn how to export your YOLO model in various formats using Ultralytics' exporter package - iOS, GDC, and more.
+---
+
 # Exporter
 ---
 :::ultralytics.yolo.engine.exporter.Exporter

docs/reference/yolo/engine/model.md

@@ -1,3 +1,7 @@
+---
+description: Discover the YOLO model of Ultralytics engine to simplify your object detection tasks with state-of-the-art models.
+---
+
 # YOLO
 ---
 :::ultralytics.yolo.engine.model.YOLO

docs/reference/yolo/engine/predictor.md

@@ -1,3 +1,7 @@
+---
+description: '"The BasePredictor class in Ultralytics YOLO Engine predicts object detection in images and videos. Learn to implement YOLO with ease."'
+---
+
 # BasePredictor
 ---
 :::ultralytics.yolo.engine.predictor.BasePredictor

docs/reference/yolo/engine/results.md

@@ -1,3 +1,7 @@
+---
+description: Learn about BaseTensor & Boxes in Ultralytics YOLO Engine. Check out Ultralytics Docs for quality tutorials and resources on object detection.
+---
+
 # BaseTensor
 ---
 :::ultralytics.yolo.engine.results.BaseTensor

docs/reference/yolo/engine/trainer.md

@@ -1,3 +1,7 @@
+---
+description: Train faster with mixed precision. Learn how to use BaseTrainer with Advanced Mixed Precision to optimize YOLOv3 and YOLOv4 models.
+---
+
 # BaseTrainer
 ---
 :::ultralytics.yolo.engine.trainer.BaseTrainer

docs/reference/yolo/engine/validator.md

@@ -1,3 +1,7 @@
+---
+description: Ensure YOLOv5 models meet constraints and standards with the BaseValidator class. Learn how to use it here.
+---
+
 # BaseValidator
 ---
 :::ultralytics.yolo.engine.validator.BaseValidator

docs/reference/yolo/utils/autobatch.md

@@ -1,3 +1,7 @@
+---
+description: Dynamically adjusts input size to optimize GPU memory usage during training. Learn how to use check_train_batch_size with Ultralytics YOLO.
+---
+
 # check_train_batch_size
 ---
 :::ultralytics.yolo.utils.autobatch.check_train_batch_size

docs/reference/yolo/utils/benchmarks.md

@@ -1,3 +1,7 @@
+---
+description: Improve your YOLO's performance and measure its speed. Benchmark utility for YOLOv5.
+---
+
 # benchmark
 ---
 :::ultralytics.yolo.utils.benchmarks.benchmark

docs/reference/yolo/utils/callbacks/base.md

@@ -1,3 +1,7 @@
+---
+description: Learn about YOLO's callback functions from on_train_start to add_integration_callbacks. See how these callbacks modify and save models.
+---
+
 # on_pretrain_routine_start
 ---
 :::ultralytics.yolo.utils.callbacks.base.on_pretrain_routine_start

docs/reference/yolo/utils/callbacks/clearml.md

@@ -1,3 +1,7 @@
+---
+description: Improve your YOLOv5 model training with callbacks from ClearML. Learn about log debug samples, pre-training routines, validation and more.
+---
+
 # _log_debug_samples
 ---
 :::ultralytics.yolo.utils.callbacks.clearml._log_debug_samples

docs/reference/yolo/utils/callbacks/comet.md

@@ -1,3 +1,7 @@
+---
+description: Learn about YOLO callbacks using the Comet.ml platform, enhancing object detection training and testing with custom logging and visualizations.
+---
+
 # _get_comet_mode
 ---
 :::ultralytics.yolo.utils.callbacks.comet._get_comet_mode

docs/reference/yolo/utils/callbacks/hub.md

@@ -1,3 +1,7 @@
+---
+description: Improve YOLOv5 model training with Ultralytics' on-train callbacks. Boost performance on-pretrain-routine-end, model-save, train/predict start.
+---
+
 # on_pretrain_routine_end
 ---
 :::ultralytics.yolo.utils.callbacks.hub.on_pretrain_routine_end

docs/reference/yolo/utils/callbacks/mlflow.md

@@ -1,3 +1,7 @@
+---
+description: Track model performance and metrics with MLflow in YOLOv5. Use callbacks like on_pretrain_routine_end or on_train_end to log information.
+---
+
 # on_pretrain_routine_end
 ---
 :::ultralytics.yolo.utils.callbacks.mlflow.on_pretrain_routine_end

docs/reference/yolo/utils/callbacks/neptune.md

@@ -1,3 +1,7 @@
+---
+description: Improve YOLOv5 training with Neptune, a powerful logging tool. Track metrics like images, plots, and epochs for better model performance.
+---
+
 # _log_scalars
 ---
 :::ultralytics.yolo.utils.callbacks.neptune._log_scalars

docs/reference/yolo/utils/callbacks/raytune.md

@@ -1,3 +1,7 @@
+---
+description: '"Improve YOLO model performance with on_fit_epoch_end callback. Learn to integrate with Ray Tune for hyperparameter tuning. Ultralytics YOLO docs."'
+---
+
 # on_fit_epoch_end
 ---
 :::ultralytics.yolo.utils.callbacks.raytune.on_fit_epoch_end

docs/reference/yolo/utils/callbacks/tensorboard.md

@@ -1,3 +1,7 @@
+---
+description: Learn how to monitor the training process with Tensorboard using Ultralytics YOLO's "_log_scalars" and "on_batch_end" methods.
+---
+
 # _log_scalars
 ---
 :::ultralytics.yolo.utils.callbacks.tensorboard._log_scalars

docs/reference/yolo/utils/callbacks/wb.md

@@ -1,3 +1,7 @@
+---
+description: Learn how to use Ultralytics YOLO's built-in callbacks `on_pretrain_routine_start` and `on_train_epoch_end` for improved training performance.
+---
+
 # on_pretrain_routine_start
 ---
 :::ultralytics.yolo.utils.callbacks.wb.on_pretrain_routine_start

docs/reference/yolo/utils/checks.md

@@ -1,3 +1,7 @@
+---
+description: 'Check functions for YOLO utils: image size, version, font, requirements, filename suffix, YAML file, YOLO, and Git version.'
+---
+
 # is_ascii
 ---
 :::ultralytics.yolo.utils.checks.is_ascii

docs/reference/yolo/utils/dist.md

@@ -1,3 +1,7 @@
+---
+description: Learn how to find free network port and generate DDP (Distributed Data Parallel) command in Ultralytics YOLO with easy examples.
+---
+
 # find_free_network_port
 ---
 :::ultralytics.yolo.utils.dist.find_free_network_port

docs/reference/yolo/utils/downloads.md

@@ -1,3 +1,7 @@
+---
+description: Download and unzip YOLO pretrained models. Ultralytics YOLO docs utils.downloads.unzip_file, checks disk space, downloads and attempts assets.
+---
+
 # is_url
 ---
 :::ultralytics.yolo.utils.downloads.is_url

docs/reference/yolo/utils/errors.md

@@ -1,3 +1,7 @@
+---
+description: Learn about HUBModelError in Ultralytics YOLO Docs. Resolve the error and get the most out of your YOLO model.
+---
+
 # HUBModelError
 ---
 :::ultralytics.yolo.utils.errors.HUBModelError

docs/reference/yolo/utils/files.md

@@ -1,3 +1,7 @@
+---
+description: 'Learn about Ultralytics YOLO files and directory utilities: WorkingDirectory, file_age, file_size, and make_dirs.'
+---
+
 # WorkingDirectory
 ---
 :::ultralytics.yolo.utils.files.WorkingDirectory

docs/reference/yolo/utils/instance.md

@@ -1,3 +1,7 @@
+---
+description: Learn about Bounding Boxes (Bboxes) and _ntuple in Ultralytics YOLO for object detection. Improve accuracy and speed with these powerful tools.
+---
+
 # Bboxes
 ---
 :::ultralytics.yolo.utils.instance.Bboxes

docs/reference/yolo/utils/loss.md

@@ -1,3 +1,7 @@
+---
+description: Learn about Varifocal Loss and Keypoint Loss in Ultralytics YOLO for advanced bounding box and pose estimation. Visit our docs for more.
+---
+
 # VarifocalLoss
 ---
 :::ultralytics.yolo.utils.loss.VarifocalLoss

docs/reference/yolo/utils/metrics.md

@@ -1,3 +1,7 @@
+---
+description: Explore Ultralytics YOLO's FocalLoss, DetMetrics, PoseMetrics, ClassifyMetrics, and more with Ultralytics Metrics documentation.
+---
+
 # FocalLoss
 ---
 :::ultralytics.yolo.utils.metrics.FocalLoss

docs/reference/yolo/utils/ops.md

@@ -1,3 +1,7 @@
+---
+description: Learn about various utility functions in Ultralytics YOLO, including x, y, width, height conversions, non-max suppression, and more.
+---
+
 # Profile
 ---
 :::ultralytics.yolo.utils.ops.Profile

docs/reference/yolo/utils/plotting.md

@@ -1,3 +1,7 @@
+---
+description: 'Discover the power of YOLO''s plotting functions: Colors, Labels and Images. Code examples to output targets and visualize features. Check it now.'
+---
+
 # Colors
 ---
 :::ultralytics.yolo.utils.plotting.Colors

docs/reference/yolo/utils/tal.md

@@ -1,3 +1,7 @@
+---
+description: Improve your YOLO models with Ultralytics' TaskAlignedAssigner, select_highest_overlaps, and dist2bbox utilities. Streamline your workflow today.
+---
+
 # TaskAlignedAssigner
 ---
 :::ultralytics.yolo.utils.tal.TaskAlignedAssigner

docs/reference/yolo/utils/torch_utils.md

@@ -1,3 +1,7 @@
+---
+description: Optimize your PyTorch models with Ultralytics YOLO's torch_utils functions such as ModelEMA, select_device, and is_parallel.
+---
+
 # ModelEMA
 ---
 :::ultralytics.yolo.utils.torch_utils.ModelEMA

docs/reference/yolo/v8/classify/predict.md

@@ -1,3 +1,7 @@
+---
+description: Learn how to use ClassificationPredictor in Ultralytics YOLOv8 for object classification tasks in a simple and efficient way.
+---
+
 # ClassificationPredictor
 ---
 :::ultralytics.yolo.v8.classify.predict.ClassificationPredictor

docs/reference/yolo/v8/classify/train.md

@@ -1,3 +1,7 @@
+---
+description: Train a custom image classification model using Ultralytics YOLOv8 with ClassificationTrainer. Boost accuracy and efficiency today.
+---
+
 # ClassificationTrainer
 ---
 :::ultralytics.yolo.v8.classify.train.ClassificationTrainer

docs/reference/yolo/v8/classify/val.md

@@ -1,3 +1,7 @@
+---
+description: Ensure model classification accuracy with Ultralytics YOLO's ClassificationValidator. Validate and improve your model with ease.
+---
+
 # ClassificationValidator
 ---
 :::ultralytics.yolo.v8.classify.val.ClassificationValidator

docs/reference/yolo/v8/detect/predict.md

@@ -1,3 +1,7 @@
+---
+description: Detect and predict objects in images and videos using the Ultralytics YOLO v8 model with DetectionPredictor.
+---
+
 # DetectionPredictor
 ---
 :::ultralytics.yolo.v8.detect.predict.DetectionPredictor

docs/reference/yolo/v8/detect/train.md

@@ -1,3 +1,7 @@
+---
+description: Train and optimize custom object detection models with Ultralytics DetectionTrainer and train functions. Get started with YOLO v8 today.
+---
+
 # DetectionTrainer
 ---
 :::ultralytics.yolo.v8.detect.train.DetectionTrainer

docs/reference/yolo/v8/detect/val.md

@@ -1,3 +1,7 @@
+---
+description: Validate YOLOv5 detections using this PyTorch module. Ensure model accuracy with NMS IOU threshold tuning and label mapping.
+---
+
 # DetectionValidator
 ---
 :::ultralytics.yolo.v8.detect.val.DetectionValidator

docs/reference/yolo/v8/pose/predict.md

@@ -1,3 +1,7 @@
+---
+description: Predict human pose coordinates and confidence scores using YOLOv5. Use on real-time video streams or static images.
+---
+
 # PosePredictor
 ---
 :::ultralytics.yolo.v8.pose.predict.PosePredictor

docs/reference/yolo/v8/pose/train.md

@@ -1,3 +1,7 @@
+---
+description: Boost posture detection using PoseTrainer and train models using train() API. Learn PoseLoss for ultra-fast and accurate pose detection with Ultralytics YOLO.
+---
+
 # PoseTrainer
 ---
 :::ultralytics.yolo.v8.pose.train.PoseTrainer

docs/reference/yolo/v8/pose/val.md

@@ -1,3 +1,7 @@
+---
+description: Ensure proper human poses in images with YOLOv8 Pose Validation, part of the Ultralytics YOLO v8 suite.
+---
+
 # PoseValidator
 ---
 :::ultralytics.yolo.v8.pose.val.PoseValidator

docs/reference/yolo/v8/segment/predict.md

@@ -1,3 +1,7 @@
+---
+description: '"Use SegmentationPredictor in YOLOv8 for efficient object detection and segmentation. Explore Ultralytics YOLO Docs for more information."'
+---
+
 # SegmentationPredictor
 ---
 :::ultralytics.yolo.v8.segment.predict.SegmentationPredictor

docs/reference/yolo/v8/segment/train.md

@@ -1,3 +1,7 @@
+---
+description: Learn about SegmentationTrainer and Train in Ultralytics YOLO v8 for efficient object detection models. Improve your training with Ultralytics Docs.
+---
+
 # SegmentationTrainer
 ---
 :::ultralytics.yolo.v8.segment.train.SegmentationTrainer

docs/reference/yolo/v8/segment/val.md

@@ -1,3 +1,7 @@
+---
+description: Ensure segmentation quality on large datasets with SegmentationValidator. Review and visualize results with ease. Learn more at Ultralytics Docs.
+---
+
 # SegmentationValidator
 ---
 :::ultralytics.yolo.v8.segment.val.SegmentationValidator