Standardize license headers in TOML/YAML files (#13491)

Refactor code for speed and clarity

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

data/Argoverse.yaml

@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
 # Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
 # Example usage: python train.py --data Argoverse.yaml
 # parent

data/GlobalWheat2020.yaml

@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
 # Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan
 # Example usage: python train.py --data GlobalWheat2020.yaml
 # parent

data/ImageNet.yaml

@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
 # ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
 # Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
 # Example usage: python classify/train.py --data imagenet

data/ImageNet10.yaml

@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
 # ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
 # Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
 # Example usage: python classify/train.py --data imagenet

data/ImageNet100.yaml

@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
 # ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
 # Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
 # Example usage: python classify/train.py --data imagenet

data/ImageNet1000.yaml

@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
 # ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
 # Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
 # Example usage: python classify/train.py --data imagenet

data/Objects365.yaml

@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
 # Objects365 dataset https://www.objects365.org/ by Megvii
 # Example usage: python train.py --data Objects365.yaml
 # parent

data/SKU-110K.yaml

@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
 # SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail
 # Example usage: python train.py --data SKU-110K.yaml
 # parent

data/VOC.yaml

@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
 # PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
 # Example usage: python train.py --data VOC.yaml
 # parent

data/VisDrone.yaml

@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
 # VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
 # Example usage: python train.py --data VisDrone.yaml
 # parent

data/coco.yaml

@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
 # COCO 2017 dataset http://cocodataset.org by Microsoft
 # Example usage: python train.py --data coco.yaml
 # parent

data/coco128-seg.yaml

@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
 # COCO128-seg dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
 # Example usage: python train.py --data coco128.yaml
 # parent

data/coco128.yaml

@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
 # COCO128 dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
 # Example usage: python train.py --data coco128.yaml
 # parent

data/hyps/hyp.Objects365.yaml

@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
 # Hyperparameters for Objects365 training
 # python train.py --weights yolov5m.pt --data Objects365.yaml --evolve
 # See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials

data/hyps/hyp.VOC.yaml

@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
 # Hyperparameters for VOC training
 # python train.py --batch 128 --weights yolov5m6.pt --data VOC.yaml --epochs 50 --img 512 --hyp hyp.scratch-med.yaml --evolve
 # See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials

data/hyps/hyp.no-augmentation.yaml

@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
 # Hyperparameters when using Albumentations frameworks
 # python train.py --hyp hyp.no-augmentation.yaml
 # See https://github.com/ultralytics/yolov5/pull/3882 for YOLOv5 + Albumentations Usage examples

data/hyps/hyp.scratch-high.yaml

@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
 # Hyperparameters for high-augmentation COCO training from scratch
 # python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
 # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials

data/hyps/hyp.scratch-low.yaml

@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
 # Hyperparameters for low-augmentation COCO training from scratch
 # python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear
 # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials

data/hyps/hyp.scratch-med.yaml

@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
 # Hyperparameters for medium-augmentation COCO training from scratch
 # python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
 # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials

data/xView.yaml

@@ -1,4 +1,5 @@
-# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
 # DIUx xView 2018 Challenge https://challenge.xviewdataset.org by U.S. National Geospatial-Intelligence Agency (NGA)
 # --------  DOWNLOAD DATA MANUALLY and jar xf val_images.zip to 'datasets/xView' before running train command!  --------
 # Example usage: python train.py --data xView.yaml