Docs updates for HUB, YOLOv4, YOLOv7, NAS (#3174)

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

docs/datasets/detect/argoverse.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Learn about the Argoverse dataset, a rich dataset designed to support research in autonomous driving tasks such as 3D tracking, motion forecasting, and stereo depth estimation.
+keywords: Argoverse Dataset, Sensor Dataset, Autonomous Driving Research, Deep Learning Models, YOLOv8n Model, 3D Tracking, Motion Forecasting, Stereo Depth Estimation, Labeled 3D Object Tracks, High-Quality Sensor Data, Richly Annotated HD Maps
 ---
 
 # Argoverse Dataset

docs/datasets/detect/coco.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Learn about the COCO dataset, designed to encourage research on object detection, segmentation, and captioning with standardized evaluation metrics.
+keywords: COCO dataset, object detection, segmentation, captioning, deep learning models, computer vision, benchmarking, data annotations, COCO Consortium
 ---
 
 # COCO Dataset

docs/datasets/detect/coco8.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Get started with Ultralytics COCO8. Ideal for testing and debugging object detection models or experimenting with new detection approaches.
+keywords: Ultralytics, COCO8, object detection dataset, YAML file format, dataset usage, COCO dataset, acknowledgments
 ---
 
 # COCO8 Dataset

docs/datasets/detect/globalwheat2020.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Learn about the Global Wheat Head Dataset, aimed at supporting the development of accurate wheat head models for applications in wheat phenotyping and crop management.
+keywords: Global Wheat Head Dataset, wheat head detection, wheat phenotyping, crop management, object detection, deep learning models, dataset structure, annotations, sample data, citations and acknowledgments
 ---
 
 # Global Wheat Head Dataset

docs/datasets/detect/index.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Learn about supported dataset formats for training YOLO detection models, including Ultralytics YOLO and COCO, in this Object Detection Datasets Overview.
+keywords: object detection, datasets, formats, Ultralytics YOLO, label format, dataset file format, dataset definition, YOLO dataset, model configuration
 ---
 
 # Object Detection Datasets Overview

docs/datasets/detect/objects365.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Discover the Objects365 dataset, designed for object detection research with a focus on diverse objects, featuring 365 categories, 2 million images, and 30 million bounding boxes.
+keywords: Objects365 dataset, object detection, computer vision, deep learning, Ultralytics Docs
 ---
 
 # Objects365 Dataset
@@ -84,4 +85,4 @@ If you use the Objects365 dataset in your research or development work, please c
 }
 ```
 
-We would like to acknowledge the team of researchers who created and maintain the Objects365 dataset as a valuable resource for the computer vision research community. For more information about the Objects365 dataset and its creators, visit the [Objects365 dataset website](https://www.objects365.org/).
+We would like to acknowledge the team of researchers who created and maintain the Objects365 dataset as a valuable resource for the computer vision research community. For more information about the Objects365 dataset and its creators, visit the [Objects365 dataset website](https://www.objects365.org/).

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

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Explore the SKU-110k dataset, designed for object detection in densely packed retail shelf images, featuring over 110k unique SKU categories and annotations.
+keywords: SKU-110k, object detection, retail shelves, dataset, computer vision
 ---
 
 # SKU-110k Dataset

docs/datasets/detect/visdrone.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Discover the VisDrone dataset, a comprehensive benchmark for drone-based computer vision tasks, including object detection, tracking, and crowd counting.
+keywords: VisDrone Dataset, Ultralytics YOLO Docs, AISKYEYE, Lab of Machine Learning and Data Mining, Computer Vision tasks, drone-based image analysis, object detection, object tracking, crowd counting, YOLOv8n model
 ---
 
 # VisDrone Dataset

docs/datasets/detect/voc.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Learn about the VOC dataset, designed to encourage research on object detection, segmentation, and classification with standardized evaluation metrics.
+keywords: PASCAL VOC dataset, object detection, segmentation, classification, computer vision, deep learning, benchmarking, VOC2007, VOC2012, mean Average Precision, mAP, PASCAL VOC evaluation server, trained models, YAML, YAML file, VOC.yaml, training, YOLOv8n model, model training, image size, annotations, object bounding boxes, segmentation masks, instance segmentation, SSD, Mask R-CNN, yolov8n.pt, mosaicing, PASCAL VOC Consortium
 ---
 
 # VOC Dataset

docs/datasets/detect/xview.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Discover the xView Dataset, a large-scale overhead imagery dataset for object detection tasks, featuring 1M instances, 60 classes, and high-resolution images.
+keywords: xView dataset, overhead imagery, computer vision, deep learning models, satellite imagery analysis, object detection
 ---
 
 # xView Dataset
@@ -89,4 +90,4 @@ If you use the xView dataset in your research or development work, please cite t
 }
 ```
 
-We would like to acknowledge the [Defense Innovation Unit](https://www.diu.mil/) (DIU) and the creators of the xView dataset for their valuable contribution to the computer vision research community. For more information about the xView dataset and its creators, visit the [xView dataset website](http://xviewdataset.org/).
+We would like to acknowledge the [Defense Innovation Unit](https://www.diu.mil/) (DIU) and the creators of the xView dataset for their valuable contribution to the computer vision research community. For more information about the xView dataset and its creators, visit the [xView dataset website](http://xviewdataset.org/).