Update LICENSE to AGPL-3.0 (#11359)

* Update LICENSE to AGPL-3.0

This pull request updates the license of the YOLOv5 project from GNU General Public License v3.0 (GPL-3.0) to GNU Affero General Public License v3.0 (AGPL-3.0).

We at Ultralytics have decided to make this change in order to better protect our intellectual property and ensure that any modifications made to the YOLOv5 source code will be shared back with the community when used over a network.

AGPL-3.0 is very similar to GPL-3.0, but with an additional clause to address the use of software over a network. This change ensures that if someone modifies YOLOv5 and provides it as a service over a network (e.g., through a web application or API), they must also make the source code of their modified version available to users of the service.

This update includes the following changes:
- Replace the `LICENSE` file with the AGPL-3.0 license text
- Update the license reference in the `README.md` file
- Update the license headers in source code files

We believe that this change will promote a more collaborative environment and help drive further innovation within the YOLOv5 community.

Please review the changes and let us know if you have any questions or concerns.


Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

* Update headers to AGPL-3.0

---------

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

data/Argoverse.yaml

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

data/SKU-110K.yaml

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

data/coco128-seg.yaml

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

data/coco128.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 # COCO128 dataset https://www.kaggle.com/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 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 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 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 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 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 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 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 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 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 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 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 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/scripts/download_weights.sh

@@ -1,5 +1,5 @@
 #!/bin/bash
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 # Download latest models from https://github.com/ultralytics/yolov5/releases
 # Example usage: bash data/scripts/download_weights.sh
 # parent

data/scripts/get_coco.sh

@@ -1,5 +1,5 @@
 #!/bin/bash
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-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, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-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/scripts/get_imagenet.sh

@@ -1,5 +1,5 @@
 #!/bin/bash
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
 # Download ILSVRC2012 ImageNet dataset https://image-net.org
 # Example usage: bash data/scripts/get_imagenet.sh
 # parent

data/xView.yaml

@@ -1,4 +1,4 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 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