Docs URL fixes and YAML updates (#1383)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Huijae Lee <46982469+ZeroAct@users.noreply.github.com>

ultralytics/datasets/Argoverse.yaml

@@ -2,7 +2,7 @@
 # Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
 # Example usage: yolo train data=Argoverse.yaml
 # parent
-# ├── yolov5
+# ├── ultralytics
 # └── datasets
 #     └── Argoverse  ← downloads here (31.3 GB)
 

ultralytics/datasets/GlobalWheat2020.yaml

@@ -2,7 +2,7 @@
 # Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan
 # Example usage: yolo train data=GlobalWheat2020.yaml
 # parent
-# ├── yolov5
+# ├── ultralytics
 # └── datasets
 #     └── GlobalWheat2020  ← downloads here (7.0 GB)
 

ultralytics/datasets/ImageNet.yaml

@@ -3,7 +3,7 @@
 # Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
 # Example usage: yolo train task=classify data=imagenet
 # parent
-# ├── yolov5
+# ├── ultralytics
 # └── datasets
 #     └── imagenet  ← downloads here (144 GB)
 

ultralytics/datasets/Objects365.yaml

@@ -2,7 +2,7 @@
 # Objects365 dataset https://www.objects365.org/ by Megvii
 # Example usage: yolo train data=Objects365.yaml
 # parent
-# ├── yolov5
+# ├── ultralytics
 # └── datasets
 #     └── Objects365  ← downloads here (712 GB = 367G data + 345G zips)
 

ultralytics/datasets/SKU-110K.yaml

@@ -2,7 +2,7 @@
 # SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail
 # Example usage: yolo train data=SKU-110K.yaml
 # parent
-# ├── yolov5
+# ├── ultralytics
 # └── datasets
 #     └── SKU-110K  ← downloads here (13.6 GB)
 

ultralytics/datasets/VOC.yaml

@@ -2,7 +2,7 @@
 # PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
 # Example usage: yolo train data=VOC.yaml
 # parent
-# ├── yolov5
+# ├── ultralytics
 # └── datasets
 #     └── VOC  ← downloads here (2.8 GB)
 

ultralytics/datasets/VisDrone.yaml

@@ -2,7 +2,7 @@
 # VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
 # Example usage: yolo train data=VisDrone.yaml
 # parent
-# ├── yolov5
+# ├── ultralytics
 # └── datasets
 #     └── VisDrone  ← downloads here (2.3 GB)
 

ultralytics/datasets/coco.yaml

@@ -2,7 +2,7 @@
 # COCO 2017 dataset http://cocodataset.org by Microsoft
 # Example usage: yolo train data=coco.yaml
 # parent
-# ├── yolov5
+# ├── ultralytics
 # └── datasets
 #     └── coco  ← downloads here (20.1 GB)
 

ultralytics/datasets/coco128-seg.yaml

@@ -2,7 +2,7 @@
 # COCO128-seg dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
 # Example usage: yolo train data=coco128.yaml
 # parent
-# ├── yolov5
+# ├── ultralytics
 # └── datasets
 #     └── coco128-seg  ← downloads here (7 MB)
 

ultralytics/datasets/coco128.yaml

@@ -2,7 +2,7 @@
 # COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
 # Example usage: yolo train data=coco128.yaml
 # parent
-# ├── yolov5
+# ├── ultralytics
 # └── datasets
 #     └── coco128  ← downloads here (7 MB)
 

ultralytics/datasets/coco8-seg.yaml

@@ -2,7 +2,7 @@
 # COCO8-seg dataset (first 8 images from COCO train2017) by Ultralytics
 # Example usage: yolo train data=coco8-seg.yaml
 # parent
-# ├── yolov5
+# ├── ultralytics
 # └── datasets
 #     └── coco8-seg  ← downloads here (1 MB)
 

ultralytics/datasets/coco8.yaml

@@ -2,7 +2,7 @@
 # COCO8 dataset (first 8 images from COCO train2017) by Ultralytics
 # Example usage: yolo train data=coco8.yaml
 # parent
-# ├── yolov5
+# ├── ultralytics
 # └── datasets
 #     └── coco8  ← downloads here (1 MB)
 

ultralytics/datasets/xView.yaml

@@ -3,7 +3,7 @@
 # --------  DOWNLOAD DATA MANUALLY and jar xf val_images.zip to 'datasets/xView' before running train command!  --------
 # Example usage: yolo train data=xView.yaml
 # parent
-# ├── yolov5
+# ├── ultralytics
 # └── datasets
 #     └── xView  ← downloads here (20.7 GB)