Update HUB SDK Docs (#13309)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

.github/workflows/ci.yaml

@@ -58,20 +58,31 @@ jobs:
         run: pip install tqdm mkdocs-material "mkdocstrings[python]" mkdocs-jupyter mkdocs-redirects mkdocs-ultralytics-plugin
       - name: Update Docs Reference Section
         run: python docs/build_reference.py
-      - name: Commit and Push Changes
+      - name: Commit and Push Reference Section Changes
         run: |
           git add .
           git reset HEAD -- .github/workflows/  # workflow changes are not permitted with default token
+          git config --global user.name "UltralyticsAssistant"
+          git config --global user.email "web@ultralytics.com"
           if ! git diff --staged --quiet; then
-            git config --global user.name "UltralyticsAssistant"
+            git commit -m "Auto-update Ultralytics Docs Reference Section by https://ultralytics.com/actions"
-            git config --global user.email "web@ultralytics.com"
-            git commit -m "Auto-update Ultralytics Docs by https://ultralytics.com/actions"
             git push
           else
             echo "No changes to commit"
           fi
       - name: Build Docs and Check for Warnings
         run: python docs/build_docs.py
+      - name: Commit and Push Docs changes
+        if: always()
+        run: |
+          git add --update  # only add updated files
+          git reset HEAD -- .github/workflows/  # workflow changes are not permitted with default token
+          if ! git diff --staged --quiet; then
+            git commit -m "Auto-update Ultralytics Docs by https://ultralytics.com/actions"
+            git push
+          else
+            echo "No changes to commit"
+          fi
 
   HUB:
     if: github.repository == 'ultralytics/ultralytics' && (github.event_name == 'schedule' || github.event_name == 'push' || (github.event_name == 'workflow_dispatch' && github.event.inputs.hub == 'true'))

docs/build_docs.py

@@ -37,7 +37,7 @@ DOCS = Path(__file__).parent.resolve()
 SITE = DOCS.parent / "site"
 
 
-def build_docs(clone_repos=True):
+def prepare_docs_markdown(clone_repos=True):
     """Build docs using mkdocs."""
     if SITE.exists():
         print(f"Removing existing {SITE}")
@@ -56,10 +56,9 @@ def build_docs(clone_repos=True):
         shutil.copytree(local_dir / "hub_sdk", DOCS.parent / "hub_sdk")  # for mkdocstrings
         print(f"Cloned/Updated {repo} in {local_dir}")
 
-    # Build the main documentation
+    # Add frontmatter
-    print(f"Building docs from {DOCS}")
+    for file in tqdm((DOCS / "en").rglob("*.md"), desc="Adding frontmatter"):
-    subprocess.run(f"mkdocs build -f {DOCS.parent}/mkdocs.yml --strict", check=True, shell=True)
+        update_markdown_files(file)
-    print(f"Site built at {SITE}")
 
 
 def update_page_title(file_path: Path, new_title: str):
@@ -116,10 +115,10 @@ def update_subdir_edit_links(subdir="", docs_url=""):
             file.write(str(soup))
 
 
-def update_page(md_filepath: Path):
+def update_markdown_files(md_filepath: Path):
     """Creates or updates a Markdown file, ensuring frontmatter is present."""
     if md_filepath.exists():
-        content = md_filepath.read_text()
+        content = md_filepath.read_text().strip()
 
         # Replace apostrophes
         content = content.replace("‘", "'").replace("’", "'")
@@ -129,20 +128,17 @@ def update_page(md_filepath: Path):
             header = "---\ncomments: true\ndescription: TODO ADD DESCRIPTION\nkeywords: TODO ADD KEYWORDS\n---\n\n"
             content = header + content
 
+        # Add EOF newline if missing
+        if not content.endswith("\n"):
+            content += "\n"
+
         # Save page
         md_filepath.write_text(content)
     return
 
 
-def main():
+def update_docs_html():
-    """Builds docs, updates titles and edit links, and prints local server command."""
+    """Updates titles, edit links and head sections of HTML documentation for improved accessibility and relevance."""
-    build_docs()
-
-    # Add frontmatter
-    for file in tqdm((DOCS / "en").rglob("*.md"), desc="Adding frontmatter"):
-        update_page(file)
-
-    # Update titles
     update_page_title(SITE / "404.html", new_title="Ultralytics Docs - Not Found")
 
     # Update edit links
@@ -156,8 +152,21 @@ def main():
     if any(script):
         update_html_head(script)
 
+
+def main():
+    """Builds docs, updates titles and edit links, and prints local server command."""
+    prepare_docs_markdown()
+
+    # Build the main documentation
+    print(f"Building docs from {DOCS}")
+    subprocess.run(f"mkdocs build -f {DOCS.parent}/mkdocs.yml --strict", check=True, shell=True)
+    print(f"Site built at {SITE}")
+
+    # Update docs HTML pages
+    update_docs_html()
+
     # Show command to serve built website
-    print('Serve site at http://localhost:8000 with "python -m http.server --directory site"')
+    print('Docs built correctly ✅\nServe site at http://localhost:8000 with "python -m http.server --directory site"')
 
 
 if __name__ == "__main__":

docs/build_reference.py

@@ -12,10 +12,16 @@ from collections import defaultdict
 from pathlib import Path
 
 # Constants
-FILE = Path(__file__).resolve()
+hub_sdk = False
-PACKAGE_DIR = FILE.parents[1] / "ultralytics"  # i.e. /Users/glennjocher/PycharmProjects/ultralytics/ultralytics
+if hub_sdk:
-REFERENCE_DIR = PACKAGE_DIR.parent / "docs/en/reference"
+    PACKAGE_DIR = Path("/Users/glennjocher/PycharmProjects/hub-sdk/hub_sdk")
-GITHUB_REPO = "ultralytics/ultralytics"
+    REFERENCE_DIR = PACKAGE_DIR.parent / "docs/reference"
+    GITHUB_REPO = "ultralytics/hub-sdk"
+else:
+    FILE = Path(__file__).resolve()
+    PACKAGE_DIR = FILE.parents[1] / "ultralytics"  # i.e. /Users/glennjocher/PycharmProjects/ultralytics/ultralytics
+    REFERENCE_DIR = PACKAGE_DIR.parent / "docs/en/reference"
+    GITHUB_REPO = "ultralytics/ultralytics"
 
 
 def extract_classes_and_functions(filepath: Path) -> tuple:
@@ -54,7 +60,7 @@ def create_markdown(py_filepath: Path, module_path: str, classes: list, function
         f"# Reference for `{module_path}.py`\n\n"
         f"!!! Note\n\n"
         f"    This file is available at [{url}]({url}). If you spot a problem please help fix it by [contributing]"
-        f"(/help/contributing.md) a [Pull Request]({edit}) 🛠️. Thank you 🙏!\n\n"
+        f"(https://docs.ultralytics.com/help/contributing/) a [Pull Request]({edit}) 🛠️. Thank you 🙏!\n\n"
     )
     md_content = ["<br><br>\n"] + [f"## ::: {module_name}.{class_name}\n\n<br><br>\n" for class_name in classes]
     md_content.extend(f"## ::: {module_name}.{func_name}\n\n<br><br>\n" for func_name in functions)
@@ -68,7 +74,7 @@ def create_markdown(py_filepath: Path, module_path: str, classes: list, function
     if not exists:
         # Add new markdown file to the git staging area
         print(f"Created new file '{md_filepath}'")
-        subprocess.run(["git", "add", "-f", str(md_filepath)], check=True)
+        subprocess.run(["git", "add", "-f", str(md_filepath)], check=True, cwd=PACKAGE_DIR)
 
     return md_filepath.relative_to(PACKAGE_DIR.parent)
 

docs/en/datasets/classify/caltech101.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Learn about the Caltech-101 dataset, its structure and uses in machine learning. Includes instructions to train a YOLO model using this dataset.
+description: Explore the widely-used Caltech-101 dataset with 9,000 images across 101 categories. Ideal for object recognition tasks in machine learning and computer vision.
-keywords: Caltech-101, dataset, YOLO training, machine learning, object recognition, ultralytics
+keywords: Caltech-101, dataset, object recognition, machine learning, computer vision, YOLO, deep learning, research, AI
 ---
 
 # Caltech-101 Dataset

docs/en/datasets/classify/caltech256.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Explore the Caltech-256 dataset, a diverse collection of images used for object recognition tasks in machine learning. Learn to train a YOLO model on the dataset.
+description: Explore the Caltech-256 dataset, featuring 30,000 images across 257 categories, ideal for training and testing object recognition algorithms.
-keywords: Ultralytics, YOLO, Caltech-256, dataset, object recognition, machine learning, computer vision, deep learning
+keywords: Caltech-256 dataset, object classification, image dataset, machine learning, computer vision, deep learning, YOLO, training dataset
 ---
 
 # Caltech-256 Dataset

docs/en/datasets/classify/cifar10.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Explore the CIFAR-10 dataset, widely used for training in machine learning and computer vision, and learn how to use it with Ultralytics YOLO.
+description: Explore the CIFAR-10 dataset, featuring 60,000 color images in 10 classes. Learn about its structure, applications, and how to train models using YOLO.
-keywords: CIFAR-10, dataset, machine learning, image classification, computer vision, YOLO, Ultralytics, training, testing, deep learning, Convolutional Neural Networks, Support Vector Machines
+keywords: CIFAR-10, dataset, machine learning, computer vision, image classification, YOLO, deep learning, neural networks
 ---
 
 # CIFAR-10 Dataset

docs/en/datasets/classify/cifar100.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Discover how to leverage the CIFAR-100 dataset for machine learning and computer vision tasks with YOLO. Gain insights on its structure, use, and utilization for model training.
+description: Explore the CIFAR-100 dataset, consisting of 60,000 32x32 color images across 100 classes. Ideal for machine learning and computer vision tasks.
-keywords: Ultralytics, YOLO, CIFAR-100 dataset, image classification, machine learning, computer vision, YOLO model training
+keywords: CIFAR-100, dataset, machine learning, computer vision, image classification, deep learning, YOLO, training, testing, Alex Krizhevsky
 ---
 
 # CIFAR-100 Dataset

docs/en/datasets/classify/fashion-mnist.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Learn how to use the Fashion-MNIST dataset for image classification with the Ultralytics YOLO model. Covers dataset structure, labels, applications, and usage.
+description: Explore the Fashion-MNIST dataset, a modern replacement for MNIST with 70,000 Zalando article images. Ideal for benchmarking machine learning models.
-keywords: Ultralytics, YOLO, Fashion-MNIST, dataset, image classification, machine learning, deep learning, neural networks, training, testing
+keywords: Fashion-MNIST, image classification, Zalando dataset, machine learning, deep learning, CNN, dataset overview
 ---
 
 # Fashion-MNIST Dataset

docs/en/datasets/classify/imagenet.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Understand how to use ImageNet, an extensive annotated image dataset for object recognition research, with Ultralytics YOLO models. Learn about its structure, usage, and significance in computer vision.
+description: Explore the extensive ImageNet dataset and discover its role in advancing deep learning in computer vision. Access pretrained models and training examples.
-keywords: Ultralytics, YOLO, ImageNet, dataset, object recognition, deep learning, computer vision, machine learning, dataset training, model training, image classification, object detection
+keywords: ImageNet, deep learning, visual recognition, computer vision, pretrained models, YOLO, dataset, object detection, image classification
 ---
 
 # ImageNet Dataset

docs/en/datasets/classify/imagenet10.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Explore the compact ImageNet10 Dataset developed by Ultralytics. Ideal for fast testing of computer vision training pipelines and CV model sanity checks.
+description: Discover ImageNet10 a compact version of ImageNet for rapid model testing and CI checks. Perfect for quick evaluations in computer vision tasks.
-keywords: Ultralytics, YOLO, ImageNet10 Dataset, Image detection, Deep Learning, ImageNet, AI model testing, Computer vision, Machine learning
+keywords: ImageNet10, ImageNet, Ultralytics, CI tests, sanity checks, training pipelines, computer vision, deep learning, dataset
 ---
 
 # ImageNet10 Dataset

docs/en/datasets/classify/imagenette.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Learn about the ImageNette dataset and its usage in deep learning model training. Find code snippets for model training and explore ImageNette datatypes.
+description: Explore the ImageNette dataset, a subset of ImageNet with 10 classes for efficient training and evaluation of image classification models. Ideal for ML and CV projects.
-keywords: ImageNette dataset, Ultralytics, YOLO, Image classification, Machine Learning, Deep learning, Training code snippets, CNN, ImageNette160, ImageNette320
+keywords: ImageNette dataset, ImageNet subset, image classification, machine learning, deep learning, YOLO, Convolutional Neural Networks, ML dataset, education, training
 ---
 
 # ImageNette Dataset

docs/en/datasets/classify/imagewoof.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Explore the ImageWoof dataset, designed for challenging dog breed classification. Train AI models with Ultralytics YOLO using this dataset.
+description: Explore the ImageWoof dataset, a challenging subset of ImageNet focusing on 10 dog breeds, designed to enhance image classification models. Learn more on Ultralytics Docs.
-keywords: ImageWoof, image classification, dog breeds, machine learning, deep learning, Ultralytics, YOLO, dataset
+keywords: ImageWoof dataset, ImageNet subset, dog breeds, image classification, deep learning, machine learning, Ultralytics, training dataset, noisy labels
 ---
 
 # ImageWoof Dataset

docs/en/datasets/classify/index.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Explore image classification datasets supported by Ultralytics, learn the standard dataset format, and set up your own dataset for training models.
+description: Learn how to structure datasets for YOLO classification tasks. Detailed folder structure and usage examples for effective training.
-keywords: Ultralytics, image classification, dataset, machine learning, CIFAR-10, ImageNet, MNIST, torchvision
+keywords: YOLO, image classification, dataset structure, CIFAR-10, Ultralytics, machine learning, training data, model evaluation
 ---
 
 # Image Classification Datasets Overview

docs/en/datasets/classify/mnist.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Detailed guide on the MNIST Dataset, a benchmark in the machine learning community for image classification tasks. Learn about its structure, usage and application.
+description: Explore the MNIST dataset, a cornerstone in machine learning for handwritten digit recognition. Learn about its structure, features, and applications.
-keywords: MNIST dataset, Ultralytics, image classification, machine learning, computer vision, deep learning, AI, dataset guide
+keywords: MNIST, dataset, handwritten digits, image classification, deep learning, machine learning, training set, testing set, NIST
 ---
 
 # MNIST Dataset

docs/en/datasets/detect/african-wildlife.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: African Wildlife objects detection, a leading dataset for object detection in forests, integrates with Ultralytics. Discover ways to use it for training YOLO models.
+description: Explore our African Wildlife Dataset featuring images of buffalo, elephant, rhino, and zebra for training computer vision models. Ideal for research and conservation.
-keywords: Ultralytics, African Wildlife dataset, object detection, YOLO, YOLO model training, object tracking, computer vision, deep learning models, forest research, animals tracking
+keywords: African Wildlife Dataset, South African animals, object detection, computer vision, YOLOv8, wildlife research, conservation, dataset
 ---
 
 # African Wildlife Dataset

docs/en/datasets/detect/argoverse.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Explore Argoverse, a comprehensive dataset for autonomous driving tasks including 3D tracking, motion forecasting and depth estimation used in YOLO.
+description: Explore the comprehensive Argoverse dataset by Argo AI for 3D tracking, motion forecasting, and stereo depth estimation in autonomous driving research.
-keywords: Argoverse dataset, autonomous driving, YOLO, 3D tracking, motion forecasting, LiDAR data, HD maps, ultralytics documentation
+keywords: Argoverse dataset, autonomous driving, 3D tracking, motion forecasting, stereo depth estimation, Argo AI, LiDAR point clouds, high-resolution images, HD maps
 ---
 
 # Argoverse Dataset

docs/en/datasets/detect/brain-tumor.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Brain tumor detection, a leading dataset for medical imaging, integrates with Ultralytics. Discover ways to use it for training YOLO models.
+description: Explore the brain tumor detection dataset with MRI/CT images. Essential for training AI models for early diagnosis and treatment planning.
-keywords: Ultralytics, Brain Tumor dataset, object detection, YOLO, YOLO model training, object tracking, computer vision, deep learning models
+keywords: brain tumor dataset, MRI scans, CT scans, brain tumor detection, medical imaging, AI in healthcare, computer vision, early diagnosis, treatment planning
 ---
 
 # Brain Tumor Dataset

docs/en/datasets/detect/coco.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Learn how COCO, a leading dataset for object detection and segmentation, integrates with Ultralytics. Discover ways to use it for training YOLO models.
+description: Explore the COCO dataset for object detection and segmentation. Learn about its structure, usage, pretrained models, and key features.
-keywords: Ultralytics, COCO dataset, object detection, YOLO, YOLO model training, image segmentation, computer vision, deep learning models
+keywords: COCO dataset, object detection, segmentation, benchmarking, computer vision, pose estimation, YOLO models, COCO annotations
 ---
 
 # COCO Dataset

docs/en/datasets/detect/coco8.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Discover the benefits of using the practical and diverse COCO8 dataset for object detection model testing. Learn to configure and use it via Ultralytics HUB and YOLOv8.
+description: Explore the Ultralytics COCO8 dataset, a versatile and manageable set of 8 images perfect for testing object detection models and training pipelines.
-keywords: Ultralytics, COCO8 dataset, object detection, model testing, dataset configuration, detection approaches, sanity check, training pipelines, YOLOv8
+keywords: COCO8, Ultralytics, dataset, object detection, YOLOv8, training, validation, machine learning, computer vision
 ---
 
 # COCO8 Dataset

docs/en/datasets/detect/globalwheat2020.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Understand how to utilize the vast Global Wheat Head Dataset for building wheat head detection models. Features, structure, applications, usage, sample data, and citation.
+description: Explore the Global Wheat Head Dataset to develop accurate wheat head detection models. Includes training images, annotations, and usage for crop management.
-keywords: Ultralytics, YOLO, Global Wheat Head Dataset, wheat head detection, plant phenotyping, crop management, deep learning, outdoor images, annotations, YAML configuration
+keywords: Global Wheat Head Dataset, wheat head detection, wheat phenotyping, crop management, deep learning, object detection, training datasets
 ---
 
 # Global Wheat Head Dataset

docs/en/datasets/detect/index.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Navigate through supported dataset formats, methods to utilize them and how to add your own datasets. Get insights on porting or converting label formats.
+description: Learn about dataset formats compatible with Ultralytics YOLO for robust object detection. Explore supported datasets and learn how to convert formats.
-keywords: Ultralytics, YOLO, datasets, object detection, dataset formats, label formats, data conversion
+keywords: Ultralytics, YOLO, object detection datasets, dataset formats, COCO, dataset conversion, training datasets
 ---
 
 # Object Detection Datasets Overview

docs/en/datasets/detect/lvis.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Learn how LVIS, a leading dataset for object detection and segmentation, integrates with Ultralytics. Discover ways to use it for training YOLO models.
+description: Discover the LVIS dataset by Facebook AI Research, a benchmark for object detection and instance segmentation with a large, diverse vocabulary. Learn how to utilize it.
-keywords: Ultralytics, LVIS dataset, object detection, YOLO, YOLO model training, image segmentation, computer vision, deep learning models
+keywords: LVIS dataset, object detection, instance segmentation, Facebook AI Research, YOLO, computer vision, model training, LVIS examples
 ---
 
 # LVIS Dataset

docs/en/datasets/detect/objects365.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Discover the Objects365 dataset, a wide-scale, high-quality resource for object detection research. Learn to use it with the Ultralytics YOLO model.
+description: Explore the Objects365 Dataset with 2M images and 30M bounding boxes across 365 categories. Enhance your object detection models with diverse, high-quality data.
-keywords: Objects365, object detection, Ultralytics, dataset, YOLO, bounding boxes, annotations, computer vision, deep learning, training models
+keywords: Objects365 dataset, object detection, machine learning, deep learning, computer vision, annotated images, bounding boxes, YOLOv8, high-resolution images, dataset configuration
 ---
 
 # Objects365 Dataset

docs/en/datasets/detect/open-images-v7.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Dive into Google's Open Images V7, a comprehensive dataset offering a broad scope for computer vision research. Understand its usage with deep learning models.
+description: Explore the comprehensive Open Images V7 dataset by Google. Learn about its annotations, applications, and use YOLOv8 pretrained models for computer vision tasks.
-keywords: Open Images V7, object detection, segmentation masks, visual relationships, localized narratives, computer vision, deep learning, annotations, bounding boxes
+keywords: Open Images V7, Google dataset, computer vision, YOLOv8 models, object detection, image segmentation, visual relationships, AI research, Ultralytics
 ---
 
 # Open Images V7 Dataset

docs/en/datasets/detect/roboflow-100.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Get to know Roboflow 100, a comprehensive object detection benchmark that brings together 100 datasets from different domains.
+description: Explore the Roboflow 100 dataset featuring 100 diverse datasets designed to test object detection models across various domains, from healthcare to video games.
-keywords: Ultralytics, YOLOv8, YOLO models, Roboflow 100, object detection, benchmark, computer vision, datasets, deep learning models
+keywords: Roboflow 100, Ultralytics, object detection, dataset, benchmarking, machine learning, computer vision, diverse datasets, model evaluation
 ---
 
 # Roboflow 100 Dataset

docs/en/datasets/detect/signature.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Signature Detection Dataset, a leading dataset for detecting signatures in documents, integrates with Ultralytics. Discover ways to use it for training YOLO models.
+description: Discover the Signature Detection Dataset for training models to identify and verify human signatures in various documents. Perfect for document verification and fraud prevention.
-keywords: Ultralytics, Signature Detection Dataset, object detection, YOLO, YOLO model training, document analysis, computer vision, deep learning models, signature tracking, document verification
+keywords: Signature Detection Dataset, document verification, fraud detection, computer vision, YOLOv8, Ultralytics, annotated signatures, training dataset
 ---
 
 # Signature Detection Dataset

docs/en/datasets/detect/sku-110k.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Explore the SKU-110k dataset of densely packed retail shelf images for object detection research. Learn how to use it with Ultralytics.
+description: Explore the SKU-110k dataset of densely packed retail shelf images, perfect for training and evaluating deep learning models in object detection tasks.
-keywords: SKU-110k dataset, object detection, retail shelf images, Ultralytics, YOLO, computer vision, deep learning models
+keywords: SKU-110k, dataset, object detection, retail shelf images, deep learning, computer vision, model training
 ---
 
 # SKU-110k Dataset

docs/en/datasets/detect/visdrone.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Explore the VisDrone Dataset, a large-scale benchmark for drone-based image analysis, and learn how to train a YOLO model using it.
+description: Explore the VisDrone Dataset, a large-scale benchmark for drone-based image and video analysis with over 2.6 million annotations for objects like pedestrians and vehicles.
-keywords: VisDrone Dataset, Ultralytics, drone-based image analysis, YOLO model, object detection, object tracking, crowd counting
+keywords: VisDrone, drone dataset, computer vision, object detection, object tracking, crowd counting, machine learning, deep learning
 ---
 
 # VisDrone Dataset

docs/en/datasets/detect/voc.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: A complete guide to the PASCAL VOC dataset used for object detection, segmentation and classification tasks with relevance to YOLO model training.
+description: Discover the PASCAL VOC dataset, essential for object detection, segmentation, and classification. Learn key features, applications, and usage tips.
-keywords: Ultralytics, PASCAL VOC dataset, object detection, segmentation, image classification, YOLO, model training, VOC.yaml, deep learning
+keywords: PASCAL VOC, VOC dataset, object detection, segmentation, classification, YOLO, Faster R-CNN, Mask R-CNN, image annotations, computer vision
 ---
 
 # VOC Dataset

docs/en/datasets/detect/xview.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Explore xView, a large-scale, high resolution satellite imagery dataset for object detection. Dive into dataset structure, usage examples & its potential applications.
+description: Explore the xView dataset, a rich resource of 1M+ object instances in high-resolution satellite imagery. Enhance detection, learning efficiency, and more.
-keywords: Ultralytics, YOLO, computer vision, xView dataset, satellite imagery, object detection, overhead imagery, training, deep learning, dataset YAML
+keywords: xView dataset, overhead imagery, satellite images, object detection, high resolution, bounding boxes, computer vision, TensorFlow, PyTorch, dataset structure
 ---
 
 # xView Dataset

docs/en/datasets/explorer/api.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Explore and analyze CV datasets with Ultralytics Explorer API, offering SQL, vector similarity, and semantic searches for efficient dataset insights.
+description: Explore the Ultralytics Explorer API for dataset exploration with SQL queries, vector similarity search, and semantic search. Learn installation and usage tips.
-keywords: Ultralytics Explorer API, Dataset Exploration, SQL Queries, Vector Similarity Search, Semantic Search, Embeddings Table, Image Similarity, Python API for Datasets, CV Dataset Analysis, LanceDB Integration
+keywords: Ultralytics, Explorer API, dataset exploration, SQL queries, similarity search, semantic search, Python API, LanceDB, embeddings, data analysis
 ---
 
 # Ultralytics Explorer API

docs/en/datasets/explorer/dashboard.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Learn about Ultralytics Explorer GUI for semantic search, SQL queries, and AI-powered natural language search in CV datasets.
+description: Unlock advanced data exploration with Ultralytics Explorer GUI. Utilize semantic search, run SQL queries, and ask AI for natural language data insights.
-keywords: Ultralytics, Explorer GUI, semantic search, vector similarity search, AI queries, SQL queries, computer vision, dataset exploration, image search, OpenAI integration
+keywords: Ultralytics Explorer GUI, semantic search, vector similarity, SQL queries, AI, natural language search, data exploration, machine learning, OpenAI, LLMs
 ---
 
 # Explorer GUI

docs/en/datasets/explorer/index.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Discover the Ultralytics Explorer, a versatile tool and Python API for CV dataset exploration, enabling semantic search, SQL queries, and vector similarity searches.
+description: Discover Ultralytics Explorer for semantic search, SQL queries, vector similarity, and natural language dataset exploration. Enhance your CV datasets effortlessly.
-keywords: Ultralytics Explorer, CV Dataset Tools, Semantic Search, SQL Dataset Queries, Vector Similarity, Python API, GUI Explorer, Dataset Analysis, YOLO Explorer, Data Insights
+keywords: Ultralytics Explorer, CV datasets, semantic search, SQL queries, vector similarity, dataset visualization, python API, machine learning, computer vision
 ---
 
 # Ultralytics Explorer

docs/en/datasets/index.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Explore various computer vision datasets supported by Ultralytics for object detection, segmentation, pose estimation, image classification, and multi-object tracking.
+description: Explore Ultralytics' diverse datasets for vision tasks like detection, segmentation, classification, and more. Enhance your projects with high-quality annotated data.
-keywords: computer vision, datasets, Ultralytics, YOLO, object detection, instance segmentation, pose estimation, image classification, multi-object tracking
+keywords: Ultralytics, datasets, computer vision, object detection, instance segmentation, pose estimation, image classification, multi-object tracking
 ---
 
 # Datasets Overview

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

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Delve into DOTA, an Oriented Bounding Box (OBB) aerial imagery dataset with 1.7 million instances and 11,268 images.
+description: Explore the DOTA dataset for object detection in aerial images, featuring 1.7M Oriented Bounding Boxes across 18 categories. Ideal for aerial image analysis.
-keywords: DOTA v1, DOTA v1.5, DOTA v2, object detection, aerial images, computer vision, deep learning, annotations, oriented bounding boxes, OBB
+keywords: DOTA dataset, object detection, aerial images, oriented bounding boxes, OBB, DOTA v1.0, DOTA v1.5, DOTA v2.0, multiscale detection, Ultralytics
 ---
 
 # DOTA Dataset with OBB

docs/en/datasets/obb/dota8.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Discover the versatile DOTA8 dataset, perfect for testing and debugging oriented detection models. Learn how to get started with YOLOv8-obb model training.
+description: Explore the DOTA8 dataset - a small, versatile oriented object detection dataset ideal for testing and debugging object detection models using Ultralytics YOLOv8.
-keywords: Ultralytics, YOLOv8, oriented detection, DOTA8 dataset, dataset, model training, YAML
+keywords: DOTA8 dataset, Ultralytics, YOLOv8, object detection, debugging, training models, oriented object detection, dataset YAML
 ---
 
 # DOTA8 Dataset

docs/en/datasets/obb/index.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Dive deep into various oriented bounding box (OBB) dataset formats compatible with Ultralytics YOLO models. Grasp the nuances of using and converting datasets to this format.
+description: Discover OBB dataset formats for Ultralytics YOLO models. Learn about their structure, application, and format conversions to enhance your object detection training.
-keywords: Ultralytics, YOLO, oriented bounding boxes, OBB, dataset formats, label formats, DOTA v2, data conversion
+keywords: Oriented Bounding Box, OBB Datasets, YOLO, Ultralytics, Object Detection, Dataset Formats
 ---
 
 # Oriented Bounding Box (OBB) Datasets Overview

docs/en/datasets/pose/coco.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Detailed guide on the special COCO-Pose Dataset in Ultralytics. Learn about its key features, structure, and usage in pose estimation tasks with YOLO.
+description: Explore the COCO-Pose dataset for advanced pose estimation. Learn about datasets, pretrained models, metrics, and applications for training with YOLO.
-keywords: Ultralytics YOLO, COCO-Pose Dataset, Deep Learning, Pose Estimation, Training Models, Dataset YAML, openpose, YOLO
+keywords: COCO-Pose, pose estimation, dataset, keypoints, COCO Keypoints 2017, YOLO, deep learning, computer vision
 ---
 
 # COCO-Pose Dataset

docs/en/datasets/pose/coco8-pose.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Discover the versatile COCO8-Pose dataset, perfect for testing and debugging pose detection models. Learn how to get started with YOLOv8-pose model training.
+description: Explore the compact, versatile COCO8-Pose dataset for testing and debugging object detection models. Ideal for quick experiments with YOLOv8.
-keywords: Ultralytics, YOLOv8, pose detection, COCO8-Pose dataset, dataset, model training, YAML
+keywords: COCO8-Pose, Ultralytics, pose detection dataset, object detection, YOLOv8, machine learning, computer vision, training data
 ---
 
 # COCO8-Pose Dataset

docs/en/datasets/pose/index.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Understand the YOLO pose dataset format and learn to use Ultralytics datasets to train your pose estimation models effectively.
+description: Learn about Ultralytics YOLO format for pose estimation datasets, supported formats, COCO-Pose, COCO8-Pose, Tiger-Pose, and how to add your own dataset.
-keywords: Ultralytics, YOLO, pose estimation, datasets, training, YAML, keypoints, COCO-Pose, COCO8-Pose, data conversion
+keywords: pose estimation, Ultralytics, YOLO format, COCO-Pose, COCO8-Pose, Tiger-Pose, dataset conversion, keypoints
 ---
 
 # Pose Estimation Datasets Overview

docs/en/datasets/pose/tiger-pose.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Discover the versatile Tiger-Pose dataset, perfect for testing and debugging pose detection models. Learn how to get started with YOLOv8-pose model training.
+description: Explore Ultralytics Tiger-Pose dataset with 263 diverse images. Ideal for testing, training, and refining pose estimation algorithms.
-keywords: Ultralytics, YOLOv8, pose detection, COCO8-Pose dataset, dataset, model training, YAML
+keywords: Ultralytics, Tiger-Pose, dataset, pose estimation, YOLOv8, training data, machine learning, neural networks
 ---
 
 # Tiger-Pose Dataset

docs/en/datasets/segment/carparts-seg.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Explore the Carparts Segmentation using Ultralytics YOLOv8 Dataset, a large-scale benchmark for Vehicle Maintenance, and learn how to train a YOLO model using it.
+description: Explore Roboflow's Carparts Segmentation Dataset for automotive AI applications. Enhance your segmentation models with rich, annotated data.
-keywords: CarParts Segmentation Dataset, Ultralytics, Vehicle Analytics, Spare parts Detection, YOLO model, object detection, object tracking
+keywords: Carparts Segmentation Dataset, Roboflow, computer vision, automotive AI, vehicle maintenance, Ultralytics
 ---
 
 # Roboflow Universe Carparts Segmentation Dataset

docs/en/datasets/segment/coco.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Explore the possibilities of the COCO-Seg dataset, designed for object instance segmentation and YOLO model training. Discover key features, dataset structure, applications, and usage.
+description: Explore the COCO-Seg dataset, an extension of COCO, with detailed segmentation annotations. Learn how to train YOLO models with COCO-Seg.
-keywords: Ultralytics, YOLO, COCO-Seg, dataset, instance segmentation, model training, deep learning, computer vision
+keywords: COCO-Seg, dataset, YOLO models, instance segmentation, object detection, COCO dataset, YOLOv8, computer vision, Ultralytics, machine learning
 ---
 
 # COCO-Seg Dataset

docs/en/datasets/segment/coco8-seg.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: 'Discover the COCO8-Seg: a compact but versatile instance segmentation dataset ideal for testing Ultralytics YOLOv8 detection approaches. Complete usage guide included.'
+description: Discover the versatile and manageable COCO8-Seg dataset by Ultralytics, ideal for testing and debugging segmentation models or new detection approaches.
-keywords: COCO8-Seg dataset, Ultralytics, YOLOv8, instance segmentation, dataset configuration, YAML, YOLOv8n-seg model, mosaiced dataset images
+keywords: COCO8-Seg, Ultralytics, segmentation dataset, YOLOv8, COCO 2017, model training, computer vision, dataset configuration
 ---
 
 # COCO8-Seg Dataset

docs/en/datasets/segment/crack-seg.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Explore the Crack Segmentation using Ultralytics YOLOv8 Dataset, a large-scale benchmark for road safety analysis, and learn how to train a YOLO model using it.
+description: Explore the extensive Roboflow Crack Segmentation Dataset, perfect for transportation and public safety studies or self-driving car model development.
-keywords: Crack Segmentation Dataset, Ultralytics, road cracks monitoring, YOLO model, object detection, object tracking, road safety
+keywords: Roboflow, Crack Segmentation Dataset, Ultralytics, transportation safety, public safety, self-driving cars, computer vision, road safety, infrastructure maintenance, dataset
 ---
 
 # Roboflow Universe Crack Segmentation Dataset

docs/en/datasets/segment/index.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Learn how Ultralytics YOLO supports various dataset formats for instance segmentation. This guide includes information on data conversions, auto-annotations, and dataset usage.
+description: Explore the supported dataset formats for Ultralytics YOLO and learn how to prepare and use datasets for training object segmentation models.
-keywords: Ultralytics, YOLO, Instance Segmentation, Dataset, YAML, COCO, Auto-Annotation, Image Segmentation
+keywords: Ultralytics, YOLO, instance segmentation, dataset formats, auto-annotation, COCO, segmentation models, training data
 ---
 
 # Instance Segmentation Datasets Overview

docs/en/datasets/segment/package-seg.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Explore the Package Segmentation using Ultralytics YOLOv8 Dataset, a large-scale benchmark for logistics, and learn how to train a YOLO model using it.
+description: Explore Roboflow's Package Segmentation Dataset. Optimize logistics and enhance vision models with curated images for package identification and sorting.
-keywords: Packet Segmentation Dataset, Ultralytics, Manufacturing, Logistics, YOLO model, object detection, object tracking
+keywords: Roboflow, Package Segmentation Dataset, computer vision, package identification, logistics, warehouse automation, segmentation models, training data
 ---
 
 # Roboflow Universe Package Segmentation Dataset

docs/en/datasets/track/index.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Understand multi-object tracking datasets, upcoming features and how to use them with YOLO in Python and CLI. Dive in now!.
+description: Learn how to use Multi-Object Tracking with YOLO. Explore dataset formats and see upcoming features for training trackers. Start with Python or CLI examples.
-keywords: Ultralytics, YOLO, multi-object tracking, datasets, detection, segmentation, pose models, Python, CLI
+keywords: YOLO, Multi-Object Tracking, Tracking Datasets, Python Tracking Example, CLI Tracking Example, Object Detection, Ultralytics, AI, Machine Learning
 ---
 
 # Multi-object Tracking Datasets Overview

docs/en/guides/analytics.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Comprehensive Guide to Understanding and Creating Line Graphs, Bar Plots, and Pie Charts
+description: Learn to create line graphs, bar plots, and pie charts using Python with guided instructions and code snippets. Maximize your data visualization skills!.
-keywords: Analytics, Data Visualization, Line Graphs, Bar Plots, Pie Charts, Quickstart Guide, Data Analysis, Python, Visualization Tools
+keywords: Ultralytics, YOLOv8, data visualization, line graphs, bar plots, pie charts, Python, analytics, tutorial, guide
 ---
 
 # Analytics using Ultralytics YOLOv8 📊

docs/en/guides/azureml-quickstart.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Step-by-step Quickstart Guide to Running YOLOv8 Object Detection Models on AzureML for Fast Prototyping and Testing
+description: Learn how to run YOLOv8 on AzureML. Quickstart instructions for terminal and notebooks to harness Azure's cloud computing for efficient model training.
-keywords: Ultralytics, YOLOv8, Object Detection, Azure Machine Learning, Quickstart Guide, Prototype, Compute Instance, Terminal, Notebook, IPython Kernel, CLI, Python SDK
+keywords: YOLOv8, AzureML, machine learning, cloud computing, quickstart, terminal, notebooks, model training, Python SDK, AI, Ultralytics
 ---
 
 # YOLOv8 🚀 on AzureML

docs/en/guides/conda-quickstart.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Comprehensive guide to setting up and using Ultralytics YOLO models in a Conda environment. Learn how to install the package, manage dependencies, and get started with object detection projects.
+description: Learn to set up a Conda environment for Ultralytics projects. Follow our comprehensive guide for easy installation and initialization.
-keywords: Ultralytics, YOLO, Conda, environment setup, object detection, package installation, deep learning, machine learning, guide
+keywords: Ultralytics, Conda, setup, installation, environment, guide, machine learning, data science
 ---
 
 # Conda Quickstart Guide for Ultralytics

docs/en/guides/coral-edge-tpu-on-raspberry-pi.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Guide on how to use Ultralytics with a Coral Edge TPU on a Raspberry Pi for increased inference performance.
+description: Learn how to boost your Raspberry Pi's ML performance using Coral Edge TPU with Ultralytics YOLOv8. Follow our detailed setup and installation guide.
-keywords: Ultralytics, YOLOv8, Object Detection, Coral, Edge TPU, Raspberry Pi, embedded, edge compute, sbc, accelerator, mobile
+keywords: Coral Edge TPU, Raspberry Pi, YOLOv8, Ultralytics, TensorFlow Lite, ML inference, machine learning, AI, installation guide, setup tutorial
 ---
 
 # Coral Edge TPU on a Raspberry Pi with Ultralytics YOLOv8 🚀

docs/en/guides/defining-project-goals.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: A detailed guide on defining the problem and setting objectives in a computer vision project, highlighting the importance of clear problem statements and measurable objectives.
+description: Learn how to define clear goals and objectives for your computer vision project with our practical guide. Includes tips on problem statements, measurable objectives, and key decisions.
-keywords: Computer Vision Project, Defining Problems, Setting Objectives, SMART Objectives, Project Scope, How Does Computer Vision Work, Computer Vision Techniques, Computer Vision Basics
+keywords: computer vision, project planning, problem statement, measurable objectives, dataset preparation, model selection, YOLOv8, Ultralytics
 ---
 
 # A Practical Guide for Defining Your Computer Vision Project

docs/en/guides/distance-calculation.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Distance Calculation Using Ultralytics YOLOv8
+description: Learn how to calculate distances between objects using Ultralytics YOLOv8 for accurate spatial positioning and scene understanding.
-keywords: Ultralytics, YOLOv8, Object Detection, Distance Calculation, Object Tracking, Notebook, IPython Kernel, CLI, Python SDK
+keywords: Ultralytics, YOLOv8, distance calculation, computer vision, object tracking, spatial positioning
 ---
 
 # Distance Calculation using Ultralytics YOLOv8 🚀

docs/en/guides/docker-quickstart.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Complete guide to setting up and using Ultralytics YOLO models with Docker. Learn how to install Docker, manage GPU support, and run YOLO models in isolated containers.
+description: Learn to effortlessly set up Ultralytics in Docker, from installation to running with CPU/GPU support. Follow our comprehensive guide for seamless container experience.
-keywords: Ultralytics, YOLO, Docker, GPU, containerization, object detection, package installation, deep learning, machine learning, guide
+keywords: Ultralytics, Docker, Quickstart Guide, CPU support, GPU support, NVIDIA Docker, container setup, Docker environment, Docker Hub, Ultralytics projects
 ---
 
 # Docker Quickstart Guide for Ultralytics

docs/en/guides/heatmaps.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Advanced Data Visualization with Ultralytics YOLOv8 Heatmaps
+description: Transform complex data into insightful heatmaps using Ultralytics YOLOv8. Discover patterns, trends, and anomalies with vibrant visualizations.
-keywords: Ultralytics, YOLOv8, Advanced Data Visualization, Heatmap Technology, Object Detection and Tracking, Jupyter Notebook, Python SDK, Command Line Interface
+keywords: Ultralytics, YOLOv8, heatmaps, data visualization, data analysis, complex data, patterns, trends, anomalies
 ---
 
 # Advanced Data Visualization: Heatmaps using Ultralytics YOLOv8 🚀

docs/en/guides/hyperparameter-tuning.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Dive into hyperparameter tuning in Ultralytics YOLO models. Learn how to optimize performance using the Tuner class and genetic evolution.
+description: Master hyperparameter tuning for Ultralytics YOLO to optimize model performance with our comprehensive guide. Elevate your machine learning models today!.
-keywords: Ultralytics, YOLO, Hyperparameter Tuning, Tuner Class, Genetic Evolution, Optimization
+keywords: Ultralytics YOLO, hyperparameter tuning, machine learning, model optimization, genetic algorithms, learning rate, batch size, epochs
 ---
 
 # Ultralytics YOLO Hyperparameter Tuning Guide

docs/en/guides/index.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: In-depth exploration of Ultralytics' YOLO. Learn about the YOLO object detection model, how to train it on custom data, multi-GPU training, exporting, predicting, deploying, and more.
+description: Master YOLO with Ultralytics tutorials covering training, deployment and optimization. Find solutions, improve metrics, and deploy with ease!.
-keywords: Ultralytics, YOLO, Deep Learning, Object detection, PyTorch, Tutorial, Multi-GPU training, Custom data training, SAHI, Tiled Inference
+keywords: Ultralytics, YOLO, tutorials, guides, object detection, deep learning, PyTorch, training, deployment, optimization, computer vision
 ---
 
 # Comprehensive Tutorials to Ultralytics YOLO

docs/en/guides/instance-segmentation-and-tracking.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Instance Segmentation with Object Tracking using Ultralytics YOLOv8
+description: Master instance segmentation and tracking with Ultralytics YOLOv8. Learn techniques for precise object identification and tracking.
-keywords: Ultralytics, YOLOv8, Instance Segmentation, Object Detection, Object Tracking, Bounding Box, Computer Vision, Notebook, IPython Kernel, CLI, Python SDK
+keywords: instance segmentation, tracking, YOLOv8, Ultralytics, object detection, machine learning, computer vision, python
 ---
 
 # Instance Segmentation and Tracking using Ultralytics YOLOv8 🚀

docs/en/guides/isolating-segmentation-objects.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: A concise guide on isolating segmented objects using Ultralytics.
+description: Learn to extract isolated objects from inference results using Ultralytics Predict Mode. Step-by-step guide for segmentation object isolation.
-keywords: Ultralytics, YOLO, segmentation, Python, object detection, inference, dataset, prediction, instance segmentation, contours, binary mask, object mask, image processing
+keywords: Ultralytics, segmentation, object isolation, Predict Mode, YOLOv8, machine learning, object detection, binary mask, image processing
 ---
 
 # Isolating Segmentation Objects

docs/en/guides/kfold-cross-validation.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: An in-depth guide demonstrating the implementation of K-Fold Cross Validation with the Ultralytics ecosystem for object detection datasets, leveraging Python, YOLO, and sklearn.
+description: Learn to implement K-Fold Cross Validation for object detection datasets using Ultralytics YOLO. Improve your model's reliability and robustness.
-keywords: K-Fold cross validation, Ultralytics, YOLO detection format, Python, sklearn, object detection
+keywords: Ultralytics, YOLO, K-Fold Cross Validation, object detection, sklearn, pandas, PyYaml, machine learning, dataset split
 ---
 
 # K-Fold Cross Validation with Ultralytics

docs/en/guides/model-deployment-options.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: A guide to help determine which deployment option to choose for your YOLOv8 model, including essential considerations.
+description: Learn about YOLOv8's diverse deployment options to maximize your model's performance. Explore PyTorch, TensorRT, OpenVINO, TF Lite, and more!.
-keywords: YOLOv8, Deployment, PyTorch, TorchScript, ONNX, OpenVINO, TensorRT, CoreML, TensorFlow, Export
+keywords: YOLOv8, deployment options, export formats, PyTorch, TensorRT, OpenVINO, TF Lite, machine learning, model deployment
 ---
 
 # Understanding YOLOv8's Deployment Options

docs/en/guides/nvidia-jetson.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Quick start guide to setting up YOLOv8 on a NVIDIA Jetson device with comprehensive benchmarks.
+description: Learn to deploy Ultralytics YOLOv8 on NVIDIA Jetson devices with our detailed guide. Explore performance benchmarks and maximize AI capabilities.
-keywords: Ultralytics, YOLO, NVIDIA, Jetson, TensorRT, quick start guide, hardware setup, machine learning, AI
+keywords: Ultralytics, YOLOv8, NVIDIA Jetson, JetPack, AI deployment, performance benchmarks, embedded systems, deep learning, TensorRT, computer vision
 ---
 
 # Quick Start Guide: NVIDIA Jetson with Ultralytics YOLOv8

docs/en/guides/object-blurring.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Learn to blur objects using Ultralytics YOLOv8 for privacy in images and videos.
+description: Learn how to use Ultralytics YOLOv8 for real-time object blurring to enhance privacy and focus in your images and videos.
-keywords: Ultralytics, YOLOv8, Object Detection, Object Blurring, Privacy Protection, Image Processing, Video Analysis, AI, Machine Learning
+keywords: YOLOv8, object blurring, real-time processing, privacy protection, image manipulation, video editing, Ultralytics
 ---
 
 # Object Blurring using Ultralytics YOLOv8 🚀

docs/en/guides/object-counting.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Object Counting Using Ultralytics YOLOv8
+description: Learn to accurately identify and count objects in real-time using Ultralytics YOLOv8 for applications like crowd analysis and surveillance.
-keywords: Ultralytics, YOLOv8, Object Detection, Object Counting, Object Tracking, Notebook, IPython Kernel, CLI, Python SDK
+keywords: object counting, YOLOv8, Ultralytics, real-time object detection, AI, deep learning, object tracking, crowd analysis, surveillance, resource optimization
 ---
 
 # Object Counting using Ultralytics YOLOv8 🚀

docs/en/guides/object-cropping.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Learn how to isolate and extract specific objects from images and videos using YOLOv8 object cropping.
+description: Learn how to crop and extract objects using Ultralytics YOLOv8 for focused analysis, reduced data volume, and enhanced precision.
-keywords: Ultralytics, YOLOv8, Object Detection, Object Cropping, Image Analysis, Video Processing, Data Extraction, Python
+keywords: Ultralytics, YOLOv8, object cropping, object detection, image processing, video analysis, AI, machine learning
 ---
 
 # Object Cropping using Ultralytics YOLOv8 🚀

docs/en/guides/optimizing-openvino-latency-vs-throughput-modes.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Learn how to optimize Ultralytics YOLOv8 models with Intel OpenVINO for maximum performance. Discover expert techniques to minimize latency and maximize throughput for real-time object detection applications.
+description: Discover how to enhance Ultralytics YOLO model performance using Intel's OpenVINO toolkit. Boost latency and throughput efficiently.
-keywords: Ultralytics, YOLOv8, OpenVINO, optimization, latency, throughput, inference, object detection, deep learning, machine learning, guide, Intel
+keywords: Ultralytics YOLO, OpenVINO optimization, deep learning, model inference, throughput optimization, latency optimization, AI deployment, Intel's OpenVINO, performance tuning
 ---
 
 # Optimizing OpenVINO Inference for Ultralytics YOLO Models: A Comprehensive Guide

docs/en/guides/parking-management.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Parking Management System Using Ultralytics YOLOv8
+description: Optimize parking spaces and enhance safety with Ultralytics YOLOv8. Explore real-time vehicle detection and smart parking solutions.
-keywords: Ultralytics, YOLOv8, Object Detection, Object Counting, Parking lots, Object Tracking, Notebook, IPython Kernel, CLI, Python SDK
+keywords: parking management, YOLOv8, Ultralytics, vehicle detection, real-time tracking, parking lot optimization, smart parking
 ---
 
 # Parking Management using Ultralytics YOLOv8 🚀

docs/en/guides/preprocessing_annotated_data.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Data preprocessing and augmentation help prepare datasets for model training in computer vision projects. Learn about various techniques for preprocessing annotated data.
+description: Learn essential data preprocessing techniques for annotated computer vision data, including resizing, normalizing, augmenting, and splitting datasets for optimal model training.
-keywords: What is Data Preprocessing, Data Preprocessing Techniques, What is Data Augmentation, Data Augmentation Methods, Benefits of Data Augmentation
+keywords: data preprocessing, computer vision, image resizing, normalization, data augmentation, training dataset, validation dataset, test dataset, YOLOv8
 ---
 
 # Data Preprocessing Techniques for Annotated Computer Vision Data

docs/en/guides/queue-management.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Queue Management Using Ultralytics YOLOv8
+description: Learn how to manage and optimize queues using Ultralytics YOLOv8 to reduce wait times and increase efficiency in various real-world applications.
-keywords: Ultralytics, YOLOv8, Queue Management, Object Counting, Object Tracking, Object Detection, Notebook, IPython Kernel, CLI, Python SDK
+keywords: queue management, YOLOv8, Ultralytics, reduce wait times, efficiency, customer satisfaction, retail, airports, healthcare, banks
 ---
 
 # Queue Management using Ultralytics YOLOv8 🚀

docs/en/guides/raspberry-pi.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Quick start guide to setting up YOLOv8 on a Raspberry Pi with comprehensive benchmarks.
+description: Learn how to deploy Ultralytics YOLOv8 on Raspberry Pi with our comprehensive guide. Get performance benchmarks, setup instructions, and best practices.
-keywords: Ultralytics, YOLO, Raspberry Pi, Pi Camera, rpicam, quick start guide, Raspberry Pi 4 vs Raspberry Pi 5, YOLO on Raspberry Pi, hardware setup, machine learning, AI
+keywords: Ultralytics, YOLOv8, Raspberry Pi, setup, guide, benchmarks, computer vision, object detection, NCNN, Docker, camera modules
 ---
 
 # Quick Start Guide: Raspberry Pi with Ultralytics YOLOv8

docs/en/guides/region-counting.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Object Counting in Different Region using Ultralytics YOLOv8
+description: Learn how to use Ultralytics YOLOv8 for precise object counting in specified regions, enhancing efficiency across various applications.
-keywords: Ultralytics, YOLOv8, Object Detection, Object Counting, Object Tracking, Notebook, IPython Kernel, CLI, Python SDK
+keywords: object counting, regions, YOLOv8, computer vision, Ultralytics, efficiency, accuracy, automation, real-time, applications, surveillance, monitoring
 ---
 
 # Object Counting in Different Regions using Ultralytics YOLOv8 🚀

docs/en/guides/sahi-tiled-inference.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: A comprehensive guide on how to use YOLOv8 with SAHI for standard and sliced inference in object detection tasks.
+description: Learn how to implement YOLOv8 with SAHI for sliced inference. Optimize memory usage and enhance detection accuracy for large-scale applications.
-keywords: YOLOv8, SAHI, Sliced Inference, Object Detection, Ultralytics, Large Scale Image Analysis, High-Resolution Imagery
+keywords: YOLOv8, SAHI, Sliced Inference, Object Detection, Ultralytics, High-resolution Images, Computational Efficiency, Integration Guide
 ---
 
 # Ultralytics Docs: Using YOLOv8 with SAHI for Sliced Inference

docs/en/guides/security-alarm-system.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Security Alarm System Project Using Ultralytics YOLOv8. Learn How to implement a Security Alarm System Using ultralytics YOLOv8
+description: Enhance your security with real-time object detection using Ultralytics YOLOv8. Reduce false positives and integrate seamlessly with existing systems.
-keywords: Object Detection, Security Alarm, Object Tracking, YOLOv8, Computer Vision Projects
+keywords: YOLOv8, Security Alarm System, real-time object detection, Ultralytics, computer vision, integration, false positives
 ---
 
 # Security Alarm System Project Using Ultralytics YOLOv8

docs/en/guides/speed-estimation.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Speed Estimation Using Ultralytics YOLOv8
+description: Learn how to estimate object speed using Ultralytics YOLOv8 for applications in traffic control, autonomous navigation, and surveillance.
-keywords: Ultralytics, YOLOv8, Object Detection, Speed Estimation, Object Tracking, Notebook, IPython Kernel, CLI, Python SDK
+keywords: Ultralytics YOLOv8, speed estimation, object tracking, computer vision, traffic control, autonomous navigation, surveillance, security
 ---
 
 # Speed Estimation using Ultralytics YOLOv8 🚀

docs/en/guides/steps-of-a-cv-project.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: A guide that walks through the key steps involved in a computer vision project, including steps like defining the problem, data collection, model training, and deployment.
+description: Discover essential steps for launching a successful computer vision project, from defining goals to model deployment and maintenance. Boost your AI capabilities now!.
-keywords: Computer Vision Steps, How Does Computer Vision Work, Computer Vision Techniques, Computer Vision Basics, Model Deployment, Data Annotation, Model Evaluation
+keywords: Computer Vision, AI, Object Detection, Image Classification, Instance Segmentation, Data Annotation, Model Training, Model Evaluation, Model Deployment
 ---
 
 # Understanding the Key Steps in a Computer Vision Project

docs/en/guides/triton-inference-server.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: A step-by-step guide on integrating Ultralytics YOLOv8 with Triton Inference Server for scalable and high-performance deep learning inference deployments.
+description: Learn how to integrate Ultralytics YOLOv8 with NVIDIA Triton Inference Server for scalable, high-performance AI model deployment.
-keywords: YOLOv8, Triton Inference Server, ONNX, Deep Learning Deployment, Scalable Inference, Ultralytics, NVIDIA, Object Detection, Cloud Inference
+keywords: Triton Inference Server, YOLOv8, Ultralytics, NVIDIA, deep learning, AI model deployment, ONNX, scalable inference
 ---
 
 # Triton Inference Server with Ultralytics YOLOv8

docs/en/guides/view-results-in-terminal.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Learn how to view image results inside a compatible VSCode terminal.
+description: Learn how to visualize YOLO inference results directly in a VSCode terminal using sixel on Linux and MacOS.
-keywords: YOLOv8, VSCode, Terminal, Remote Development, Ultralytics, SSH, Object Detection, Inference, Results, Remote Tunnel, Images, Helpful, Productivity Hack
+keywords: YOLO, inference results, VSCode terminal, sixel, display images, Linux, MacOS
 ---
 
 # Viewing Inference Results in a Terminal

docs/en/guides/vision-eye.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: VisionEye View Object Mapping using Ultralytics YOLOv8
+description: Discover VisionEye's object mapping and tracking powered by Ultralytics YOLOv8. Simulate human eye precision, track objects, and calculate distances effortlessly.
-keywords: Ultralytics, YOLOv8, Object Detection, Object Tracking, IDetection, VisionEye, Computer Vision, Notebook, IPython Kernel, CLI, Python SDK
+keywords: VisionEye, YOLOv8, Ultralytics, object mapping, object tracking, distance calculation, computer vision, AI, machine learning, Python, tutorial
 ---
 
 # VisionEye View Object Mapping using Ultralytics YOLOv8 🚀

docs/en/guides/workouts-monitoring.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Workouts Monitoring Using Ultralytics YOLOv8
+description: Optimize your fitness routine with real-time workouts monitoring using Ultralytics YOLOv8. Track and improve your exercise form and performance.
-keywords: Ultralytics, YOLOv8, Object Detection, Pose Estimation, PushUps, PullUps, Ab workouts, Notebook, IPython Kernel, CLI, Python SDK
+keywords: workouts monitoring, Ultralytics YOLOv8, pose estimation, fitness tracking, exercise assessment, real-time feedback, exercise form, performance metrics
 ---
 
 # Workouts Monitoring using Ultralytics YOLOv8 🚀

docs/en/guides/yolo-common-issues.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: A comprehensive guide to troubleshooting common issues encountered while working with YOLOv8 in the Ultralytics ecosystem.
+description: Comprehensive guide to troubleshoot common YOLOv8 issues, from installation errors to model training challenges. Enhance your Ultralytics projects with our expert tips.
-keywords: Troubleshooting, Ultralytics, YOLOv8, Installation Errors, Training Data, Model Performance, Hyperparameter Tuning, Deployment
+keywords: YOLO, YOLOv8, troubleshooting, installation errors, model training, GPU issues, Ultralytics, AI, computer vision, deep learning, Python, CUDA, PyTorch, debugging
 ---
 
 # Troubleshooting Common YOLO Issues

docs/en/guides/yolo-performance-metrics.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: A comprehensive guide on various performance metrics related to YOLOv8, their significance, and how to interpret them.
+description: Explore essential YOLOv8 performance metrics like mAP, IoU, F1 Score, Precision, and Recall. Learn how to calculate and interpret them for model evaluation.
-keywords: YOLOv8, Performance metrics, Object detection, Intersection over Union (IoU), Average Precision (AP), Mean Average Precision (mAP), Precision, Recall, Validation mode, Ultralytics
+keywords: YOLOv8 performance metrics, mAP, IoU, F1 Score, Precision, Recall, object detection, Ultralytics
 ---
 
 # Performance Metrics Deep Dive

docs/en/guides/yolo-thread-safe-inference.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: This guide provides best practices for performing thread-safe inference with YOLO models, ensuring reliable and concurrent predictions in multi-threaded applications.
+description: Learn how to ensure thread-safe YOLO model inference in Python. Avoid race conditions and run your multi-threaded tasks reliably with best practices.
-keywords: thread-safe, YOLO inference, multi-threading, concurrent predictions, YOLO models, Ultralytics, Python threading, safe YOLO usage, AI concurrency
+keywords: YOLO models, thread-safe, Python threading, model inference, concurrency, race conditions, multi-threaded, parallelism, Python GIL
 ---
 
 # Thread-Safe Inference with YOLO Models

docs/en/help/CI.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Learn how Ultralytics leverages Continuous Integration (CI) for maintaining high-quality code. Explore our CI tests and the status of these tests for our repositories.
+description: Learn about Ultralytics CI actions, Docker deployment, broken link checks, CodeQL analysis, and PyPI publishing to ensure high-quality code.
-keywords: continuous integration, software development, CI tests, Ultralytics repositories, high-quality code, Docker Deployment, Broken Links, CodeQL, PyPI Publishing
+keywords: Ultralytics, Continuous Integration, CI, Docker deployment, CodeQL, PyPI publishing, code quality, automated testing
 ---
 
 # Continuous Integration (CI)

docs/en/help/CLA.md

@@ -1,6 +1,6 @@
 ---
-description: Understand terms governing contributions to Ultralytics projects including source code, bug fixes, documentation and more. Read our Contributor License Agreement.
+description: Review the terms for contributing to Ultralytics projects. Learn about copyright, patent licenses, and moral rights for your contributions.
-keywords: Ultralytics, Contributor License Agreement, Open Source Software, Contributions, Copyright License, Patent License, Moral Rights
+keywords: Ultralytics, Contributor License Agreement, open source, contributions, copyright license, patent license, moral rights
 ---
 
 # Ultralytics Individual Contributor License Agreement

docs/en/help/FAQ.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Find solutions to your common Ultralytics YOLO related queries. Learn about hardware requirements, fine-tuning YOLO models, conversion to ONNX/TensorFlow, and more.
+description: Explore common questions and solutions related to Ultralytics YOLO, from hardware requirements to model fine-tuning and real-time detection.
-keywords: Ultralytics, YOLO, FAQ, hardware requirements, ONNX, TensorFlow, real-time detection, YOLO accuracy
+keywords: Ultralytics, YOLO, FAQ, object detection, hardware requirements, fine-tuning, ONNX, TensorFlow, real-time detection, model accuracy
 ---
 
 # Ultralytics YOLO Frequently Asked Questions (FAQ)

docs/en/help/code_of_conduct.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Explore Ultralytics community's Code of Conduct, ensuring a supportive, inclusive environment for contributors & members at all levels. Find our guidelines on acceptable behavior & enforcement.
+description: Join our welcoming community! Learn about Ultralytics's Code of Conduct to ensure a harassment-free experience for all participants.
-keywords: Ultralytics, code of conduct, community, contribution, behavior guidelines, enforcement, open source contributions
+keywords: Ultralytics, Contributor Covenant, Code of Conduct, community guidelines, harassment-free, inclusive community, diversity, enforcement policy
 ---
 
 # Ultralytics Contributor Covenant Code of Conduct

docs/en/help/contributing.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Learn how to contribute to Ultralytics YOLO projects – guidelines for pull requests, reporting bugs, code conduct and CLA signing.
+description: Learn how to contribute to Ultralytics YOLO open-source repositories. Follow guidelines for pull requests, code of conduct, and bug reporting.
-keywords: Ultralytics, YOLO, open-source, contribute, pull request, bug report, coding guidelines, CLA, code of conduct, GitHub
+keywords: Ultralytics, YOLO, open-source, contribution, pull request, code of conduct, bug reporting, GitHub, CLA, Google-style docstrings
 ---
 
 # Contributing to Ultralytics Open-Source YOLO Repositories

docs/en/help/environmental-health-safety.md

@@ -1,7 +1,7 @@
 ---
 comments: false
-description: Discover Ultralytics' EHS policy principles and implementation measures. Committed to safety, environment, and continuous improvement for a sustainable future.
+description: Explore Ultralytics' commitment to Environmental, Health, and Safety (EHS) policies. Learn about our measures to ensure safety, compliance, and sustainability.
-keywords: Ultralytics policy, EHS, environment, health and safety, compliance, prevention, continuous improvement, risk management, emergency preparedness, resource allocation, communication
+keywords: Ultralytics, EHS policy, safety, sustainability, environmental impact, health and safety, risk management, compliance, continuous improvement
 ---
 
 # Ultralytics Environmental, Health and Safety (EHS) Policy

docs/en/help/index.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Find comprehensive guides and documents on Ultralytics YOLO tasks. Includes FAQs, contributing guides, CI guide, CLA, MRE guide, code of conduct & more.
+description: Explore the Ultralytics Help Center with guides, FAQs, CI processes, and policies to support your YOLO model experience and contributions.
-keywords: Ultralytics, YOLO, guides, documents, FAQ, contributing, CI guide, CLA, MRE guide, code of conduct, EHS policy, security policy, privacy policy
+keywords: Ultralytics, YOLO, help center, documentation, guides, FAQ, contributing, CI, MRE, CLA, code of conduct, security policy, privacy policy
 ---
 
 Welcome to the Ultralytics Help page! We are dedicated to providing you with detailed resources to enhance your experience with the Ultralytics YOLO models and repositories. This page serves as your portal to guides and documentation designed to assist you with various tasks and answer questions you may encounter while engaging with our repositories.

docs/en/help/minimum_reproducible_example.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Learn how to create minimum reproducible examples (MRE) for efficient bug reporting in Ultralytics YOLO repositories with this step-by-step guide.
+description: Learn how to create effective Minimum Reproducible Examples (MRE) for bug reports in Ultralytics YOLO repositories. Follow our guide for efficient issue resolution.
-keywords: Ultralytics, YOLO, minimum reproducible example, MRE, bug reports, guide, dependencies, code, troubleshooting
+keywords: Ultralytics, YOLO, Minimum Reproducible Example, MRE, bug report, issue resolution, machine learning, deep learning
 ---
 
 # Creating a Minimum Reproducible Example for Bug Reports in Ultralytics YOLO Repositories

docs/en/help/privacy.md

@@ -1,6 +1,6 @@
 ---
-description: Learn about how Ultralytics collects and uses data to improve user experience, ensure software stability, and address privacy concerns, with options to opt-out.
+description: Discover how Ultralytics collects and uses anonymized data to enhance the YOLO Python package while prioritizing user privacy and control.
-keywords: Ultralytics, Data Collection, User Privacy, Google Analytics, Sentry, Crash Reporting, Anonymized Data, Privacy Settings, Opt-Out
+keywords: Ultralytics, data collection, YOLO, Python package, Google Analytics, Sentry, privacy, anonymized data, user control, crash reporting
 ---
 
 # Data Collection for Ultralytics Python Package

docs/en/help/security.md

@@ -1,6 +1,6 @@
 ---
-description: Explore Ultralytics' comprehensive security strategies safeguarding user data and systems. Learn about our diverse security tools, including Snyk, GitHub CodeQL, and Dependabot Alerts.
+description: Learn about the security measures and tools used by Ultralytics to protect user data and systems. Discover how we address vulnerabilities with Snyk, CodeQL, Dependabot, and more.
-keywords: Ultralytics, Comprehensive Security, user data protection, Snyk, GitHub CodeQL, Dependabot, vulnerability management, coding security practices
+keywords: Ultralytics security policy, Snyk scanning, CodeQL scanning, Dependabot alerts, secret scanning, vulnerability reporting, GitHub security, open-source security
 ---
 
 # Ultralytics Security Policy

docs/en/hub/app/android.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Discover real-time object detection on your Android with the Ultralytics App using optimized YOLO models.
+description: Experience real-time object detection on Android with Ultralytics. Leverage YOLO models for efficient and fast object identification. Download now!.
-keywords: Ultralytics, YOLO, Android app, real-time object detection, TensorFlow Lite, model optimization, AI mobile application
+keywords: Ultralytics, Android app, real-time object detection, YOLO models, TensorFlow Lite, FP16 quantization, INT8 quantization, hardware delegates, mobile AI, download app
 ---
 
 # Ultralytics Android App: Real-time Object Detection with YOLO Models
@@ -97,4 +97,4 @@ To get started with the Ultralytics Android App, follow these steps:
 
 6. Explore the app's settings to adjust the detection threshold, enable or disable specific object classes, and more.
 
-With the Ultralytics Android App, you now have the power of real-time object detection using YOLO models right at your fingertips. Enjoy exploring the app's features and optimizing its settings to suit your specific use cases.
+With the Ultralytics Android App, you now have the power of real-time object detection using YOLO models right at your fingertips. Enjoy exploring the app's features and optimizing its settings to suit your specific use cases.

docs/en/hub/app/index.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Unlock real-time YOLO model detection directly on your mobile! Discover apps for iOS and Android with YOLOv5 and YOLOv8.
+description: Discover the Ultralytics HUB App for running YOLOv5 and YOLOv8 models on iOS and Android devices with hardware acceleration.
-keywords: Ultralytics HUB App, YOLOv5, YOLOv8, real-time detection, mobile AI, iOS, Android, Apple Neural Engine, Android GPU, Ultralytics
+keywords: Ultralytics HUB, YOLO models, mobile app, iOS, Android, hardware acceleration, YOLOv5, YOLOv8, neural engine, GPU, NNAPI
 ---
 
 # Ultralytics HUB App
@@ -45,4 +45,4 @@ Welcome to the Ultralytics HUB App! We are excited to introduce this powerful mo
 - [**iOS**](ios.md): Learn about YOLO CoreML models accelerated on Apple's Neural Engine for iPhones and iPads.
 - [**Android**](android.md): Explore TFLite acceleration on Android mobile devices.
 
-Get started today by downloading the Ultralytics HUB App on your mobile device and unlock the potential of YOLOv5 and YOLOv8 models on-the-go. Don't forget to check out our comprehensive [HUB Docs](../index.md) for more information on training, deploying, and using your custom models with the Ultralytics HUB platform.
+Get started today by downloading the Ultralytics HUB App on your mobile device and unlock the potential of YOLOv5 and YOLOv8 models on-the-go. Don't forget to check out our comprehensive [HUB Docs](../index.md) for more information on training, deploying, and using your custom models with the Ultralytics HUB platform.

docs/en/hub/app/ios.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Discover real-time object detection on iOS devices using powerful YOLO models, optimized via Apple Neural Engine.
+description: Discover the Ultralytics iOS App for running YOLO models on your iPhone or iPad. Achieve fast, real-time object detection with Apple Neural Engine.
-keywords: Ultralytics, iOS app, YOLO models, real-time detection, object detection, iPhone, iPad, Apple Neural Engine, Core ML, quantization, FP16, INT8
+keywords: Ultralytics, iOS App, YOLO models, real-time object detection, Apple Neural Engine, Core ML, FP16 quantization, INT8 quantization, machine learning
 ---
 
 # Ultralytics iOS App: Real-time Object Detection with YOLO Models
@@ -87,4 +87,4 @@ To get started with the Ultralytics iOS App, follow these steps:
 
 6. Explore the app's settings to adjust the detection threshold, enable or disable specific object classes, and more.
 
-With the Ultralytics iOS App, you can now leverage the power of YOLO models for real-time object detection on your iPhone or iPad, powered by the Apple Neural Engine and optimized with FP16 or INT8 quantization.
+With the Ultralytics iOS App, you can now leverage the power of YOLO models for real-time object detection on your iPhone or iPad, powered by the Apple Neural Engine and optimized with FP16 or INT8 quantization.

docs/en/hub/cloud-training.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Transform model training with Ultralytics HUB Cloud. Efficient, simple cloud-based training for Pro users.
+description: Discover Ultralytics HUB Cloud Training for easy model training. Upgrade to Pro and start training with a single click. Streamline your workflow now!.
-keywords: Ultralytics HUB, cloud training, model training, Pro users, cloud-based ML, machine learning, AI model training
+keywords: Ultralytics HUB, cloud training, model training, Pro Plan, easy AI setup
 ---
 
 # Ultralytics HUB Cloud Training

docs/en/hub/datasets.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Discover how to seamlessly upload, manage, and leverage datasets for model training with Ultralytics HUB.
+description: Effortlessly manage, upload, and share your custom datasets on Ultralytics HUB for seamless model training integration. Simplify your workflow today!.
-keywords: Ultralytics HUB datasets, upload datasets, manage datasets, dataset management, model training, YOLOv5 datasets, YOLOv8 datasets
+keywords: Ultralytics HUB, datasets, custom datasets, dataset management, model training, upload datasets, share datasets, dataset workflow
 ---
 
 # Ultralytics HUB Datasets
@@ -164,4 +164,4 @@ Navigate to the Dataset page of the dataset you want to delete, open the dataset
 
     If you change your mind, you can restore the dataset from the [Trash](https://hub.ultralytics.com/trash) page.
 
-    ![Ultralytics HUB screenshot of the Trash page with an arrow pointing to Trash button in the sidebar and one to the Restore option of one of the datasets](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_delete_dataset_3.jpg)
+    ![Ultralytics HUB screenshot of the Trash page with an arrow pointing to Trash button in the sidebar and one to the Restore option of one of the datasets](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_delete_dataset_3.jpg)

docs/en/hub/index.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Launch into AI with Ultralytics HUB! Train, monitor, and deploy YOLO models easily. Start your journey in AI model training today.
+description: Discover Ultralytics HUB, the all-in-one web tool for training and deploying YOLOv5 and YOLOv8 models. Get started quickly with pre-trained models and user-friendly features.
-keywords: Ultralytics HUB, YOLOv5, YOLOv8, model training, AI platform, object detection, deploy models, train YOLO models
+keywords: Ultralytics HUB, YOLO models, train YOLO, YOLOv5, YOLOv8, object detection, model deployment, machine learning, deep learning, AI tools, dataset upload, model training
 ---
 
 # Ultralytics HUB

docs/en/hub/inference-api.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Effortlessly run YOLO model inferences with Ultralytics HUB Inference API. No local setup required!
+description: Learn how to run inference using the Ultralytics HUB Inference API. Includes examples in Python and cURL for quick integration.
-keywords: Ultralytics HUB, Inference API, YOLO, model inference, REST API, machine learning, AI model deployment
+keywords: Ultralytics, HUB, Inference API, Python, cURL, REST API, YOLO, image processing, machine learning, AI integration
 ---
 
 # Ultralytics HUB Inference API