Update dataset headers (#4162)

data/Argoverse_HD.yaml

+# Copyright Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
 # Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/
-# Train command: python train.py --data Argoverse_HD.yaml
-# Default dataset location is next to YOLOv5:
-#   /parent
-#     /datasets/Argoverse
-#     /yolov5
+# YOLOv5 🚀 example usage: python train.py --data Argoverse_HD.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── Argoverse  ← downloads here
 
 
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]

data/GlobalWheat2020.yaml

+# Copyright Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
 # Global Wheat 2020 dataset http://www.global-wheat.com/
-# Train command: python train.py --data GlobalWheat2020.yaml
-# Default dataset location is next to YOLOv5:
-#   /parent
-#     /datasets/GlobalWheat2020
-#     /yolov5
+# YOLOv5 🚀 example usage: python train.py --data GlobalWheat2020.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── GlobalWheat2020  ← downloads here
 
 
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]

data/Objects365.yaml

+# Copyright Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
 # Objects365 dataset https://www.objects365.org/
-# Train command: python train.py --data Objects365.yaml
-# Default dataset location is next to YOLOv5:
-#   /parent
-#     /datasets/Objects365
-#     /yolov5
+# YOLOv5 🚀 example usage: python train.py --data Objects365.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── Objects365  ← downloads here
 
 
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]

data/SKU-110K.yaml

+# Copyright Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
 # SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19
-# Train command: python train.py --data SKU-110K.yaml
-# Default dataset location is next to YOLOv5:
-#   /parent
-#     /datasets/SKU-110K
-#     /yolov5
+# YOLOv5 🚀 example usage: python train.py --data SKU-110K.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── SKU-110K  ← downloads here
 
 
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]

data/VOC.yaml

-# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
-# Train command: python train.py --data VOC.yaml
-# Default dataset location is next to YOLOv5:
-#   /parent
-#     /datasets/VOC
-#     /yolov5
+# Copyright Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
+# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC
+# YOLOv5 🚀 example usage: python train.py --data VOC.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── VOC  ← downloads here
 
 
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]

data/VisDrone.yaml

+# Copyright Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
 # VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset
-# Train command: python train.py --data VisDrone.yaml
-# Default dataset location is next to YOLOv5:
-#   /parent
-#     /datasets/VisDrone
-#     /yolov5
+# YOLOv5 🚀 example usage: python train.py --data VisDrone.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── VisDrone  ← downloads here
 
 
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]

data/coco.yaml

+# Copyright Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
 # COCO 2017 dataset http://cocodataset.org
-# Train command: python train.py --data coco.yaml
-# Default dataset location is next to YOLOv5:
-#   /parent
-#     /datasets/coco
-#     /yolov5
+# YOLOv5 🚀 example usage: python train.py --data coco.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── coco  ← downloads here
 
 
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]

data/coco128.yaml

-# COCO 2017 dataset http://cocodataset.org - first 128 training images
-# Train command: python train.py --data coco128.yaml
-# Default dataset location is next to YOLOv5:
-#   /parent
-#     /datasets/coco128
-#     /yolov5
+# Copyright Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
+# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
+# YOLOv5 🚀 example usage: python train.py --data coco128.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── coco128  ← downloads here
 
 
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]

data/xView.yaml

+# Copyright Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
 # xView 2018 dataset https://challenge.xviewdataset.org
-# ----> NOTE: DOWNLOAD DATA MANUALLY from URL above and unzip to /datasets/xView before running train command below
-# Train command: python train.py --data xView.yaml
-# Default dataset location is next to YOLOv5:
-#   /parent
-#     /datasets/xView
-#     /yolov5
+# --------  DOWNLOAD DATA MANUALLY from URL above and unzip to 'datasets/xView' before running train command!  --------
+# YOLOv5 🚀 example usage: python train.py --data xView.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── xView  ← downloads here
 
 
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]