Standardize headers and docstrings (#4417)

* Implement new headers

* Reformat 1

* Reformat 2

* Reformat 3 - math

* Reformat 4 - yaml

data/Argoverse.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
 # Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/
 # Example usage: python train.py --data Argoverse.yaml
 # parent

data/GlobalWheat2020.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
 # Global Wheat 2020 dataset http://www.global-wheat.com/
 # Example usage: python train.py --data GlobalWheat2020.yaml
 # parent

data/Objects365.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
 # Objects365 dataset https://www.objects365.org/
 # Example usage: python train.py --data Objects365.yaml
 # parent

data/SKU-110K.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
 # SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19
 # Example usage: python train.py --data SKU-110K.yaml
 # parent

data/VOC.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
 # PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC
 # Example usage: python train.py --data VOC.yaml
 # parent

data/VisDrone.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
 # VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset
 # Example usage: python train.py --data VisDrone.yaml
 # parent

data/coco.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
 # COCO 2017 dataset http://cocodataset.org
 # Example usage: python train.py --data coco.yaml
 # parent

data/coco128.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
 # COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
 # Example usage: python train.py --data coco128.yaml
 # parent

data/hyps/hyp.finetune.yaml

@@ -1,8 +1,8 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
 # Hyperparameters for VOC finetuning
 # python train.py --batch 64 --weights yolov5m.pt --data VOC.yaml --img 512 --epochs 50
 # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
 
-
 # Hyperparameter Evolution Results
 # Generations: 306
 #                   P         R     mAP.5 mAP.5:.95       box       obj       cls

data/hyps/hyp.finetune_objects365.yaml

@@ -1,3 +1,5 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
 lr0: 0.00258
 lrf: 0.17
 momentum: 0.779

data/hyps/hyp.scratch-p6.yaml

@@ -1,8 +1,8 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
 # Hyperparameters for 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
 
-
 lr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)
 lrf: 0.2  # final OneCycleLR learning rate (lr0 * lrf)
 momentum: 0.937  # SGD momentum/Adam beta1

data/hyps/hyp.scratch.yaml

@@ -1,8 +1,8 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
 # Hyperparameters for COCO training from scratch
 # python train.py --batch 40 --cfg yolov5m.yaml --weights '' --data coco.yaml --img 640 --epochs 300
 # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
 
-
 lr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)
 lrf: 0.2  # final OneCycleLR learning rate (lr0 * lrf)
 momentum: 0.937  # SGD momentum/Adam beta1

data/scripts/download_weights.sh

@@ -1,5 +1,5 @@
 #!/bin/bash
-# YOLOv5 🚀 by Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
 # Download latest models from https://github.com/ultralytics/yolov5/releases
 # Example usage: bash path/to/download_weights.sh
 # parent

data/scripts/get_coco.sh

@@ -1,5 +1,5 @@
 #!/bin/bash
-# YOLOv5 🚀 by Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
 # Download COCO 2017 dataset http://cocodataset.org
 # Example usage: bash data/scripts/get_coco.sh
 # parent

data/scripts/get_coco128.sh

@@ -1,5 +1,5 @@
 #!/bin/bash
-# YOLOv5 🚀 by Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
 # Download COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
 # Example usage: bash data/scripts/get_coco128.sh
 # parent

data/xView.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics https://ultralytics.com, licensed under GNU GPL v3.0
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
 # xView 2018 dataset https://challenge.xviewdataset.org
 # --------  DOWNLOAD DATA MANUALLY from URL above and unzip to 'datasets/xView' before running train command!  --------
 # Example usage: python train.py --data xView.yaml