Standardize headers and docstrings (#4417)

.github/workflows/ci-testing.yml

+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
 name: CI CPU testing
 
 on: # https://help.github.com/en/actions/reference/events-that-trigger-workflows
   push:
-    branches: [ master, develop ]
+    branches: [master, develop]
   pull_request:
     # The branches below must be a subset of the branches above
-    branches: [ master, develop ]
+    branches: [master, develop]
 
 jobs:
   cpu-tests:
@@ -14,9 +16,9 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        os: [ ubuntu-latest, macos-latest, windows-latest ]
-        python-version: [ 3.8 ]
-        model: [ 'yolov5s' ]  # models to test
+        os: [ubuntu-latest, macos-latest, windows-latest]
+        python-version: [3.8]
+        model: ['yolov5s']  # models to test
 
     # Timeout: https://stackoverflow.com/a/59076067/4521646
     timeout-minutes: 50

.github/workflows/codeql-analysis.yml

@@ -15,7 +15,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        language: [ 'python' ]
+        language: ['python']
         # CodeQL supports [ 'cpp', 'csharp', 'go', 'java', 'javascript', 'python' ]
         # Learn more:
         # https://docs.github.com/en/free-pro-team@latest/github/finding-security-vulnerabilities-and-errors-in-your-code/configuring-code-scanning#changing-the-languages-that-are-analyzed

.github/workflows/greetings.yml

+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
 name: Greetings
 
-on: [ pull_request_target, issues ]
+on: [pull_request_target, issues]
 
 jobs:
   greeting:

.github/workflows/rebase.yml

@@ -3,7 +3,7 @@ name: Automatic Rebase
 
 on:
   issue_comment:
-    types: [ created ]
+    types: [created]
 
 jobs:
   rebase:

.github/workflows/stale.yml

+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
 name: Close stale issues
 on:
   schedule:

Dockerfile

+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
 # Start FROM Nvidia PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
 FROM nvcr.io/nvidia/pytorch:21.05-py3
 

data/Argoverse.yaml

-# 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

-# 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

-# 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

-# 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

-# 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

-# 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

-# 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

-# 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

+# 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

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

data/hyps/hyp.scratch-p6.yaml

+# 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

+# 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

 #!/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

 #!/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

 #!/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

-# 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

detect.py

-"""Run inference with a YOLOv5 model on images, videos, directories, streams
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Run inference on images, videos, directories, streams, etc.
 
 Usage:
     $ python path/to/detect.py --source path/to/img.jpg --weights yolov5s.pt --img 640

export.py

-"""Export a YOLOv5 *.pt model to TorchScript, ONNX, CoreML formats
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Export a PyTorch model to TorchScript, ONNX, CoreML formats
 
 Usage:
     $ python path/to/export.py --weights yolov5s.pt --img 640 --batch 1

hubconf.py

-"""YOLOv5 PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5/
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5/
 
 Usage:
     import torch

models/common.py

-# YOLOv5 common modules
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Common modules
+"""
 
 import logging
+import math
 import warnings
 from copy import copy
 from pathlib import Path
 
-import math
 import numpy as np
 import pandas as pd
 import requests

models/experimental.py

-# YOLOv5 experimental modules
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Experimental modules
+"""
 
 import numpy as np
 import torch
 import torch.nn as nn
 
-from models.common import Conv, DWConv
+from models.common import Conv
 from utils.downloads import attempt_download
 
 

models/hub/anchors.yaml

-# Default YOLOv5 anchors for COCO data
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Default anchors for COCO data
 
 
 # P5 -------------------------------------------------------------------------------------------------------------------

models/hub/yolov3-spp.yaml

+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
 # Parameters
 nc: 80  # number of classes
 depth_multiple: 1.0  # model depth multiple

models/hub/yolov3-tiny.yaml

+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
 # Parameters
 nc: 80  # number of classes
 depth_multiple: 1.0  # model depth multiple

models/hub/yolov3.yaml

+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
 # Parameters
 nc: 80  # number of classes
 depth_multiple: 1.0  # model depth multiple

models/hub/yolov5-bifpn.yaml

+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
 # Parameters
 nc: 80  # number of classes
 depth_multiple: 1.0  # model depth multiple

models/hub/yolov5-fpn.yaml

+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
 # Parameters
 nc: 80  # number of classes
 depth_multiple: 1.0  # model depth multiple

models/hub/yolov5-p2.yaml

+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
 # Parameters
 nc: 80  # number of classes
 depth_multiple: 1.0  # model depth multiple

models/hub/yolov5-p6.yaml

+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
 # Parameters
 nc: 80  # number of classes
 depth_multiple: 1.0  # model depth multiple

models/hub/yolov5-p7.yaml

+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
 # Parameters
 nc: 80  # number of classes
 depth_multiple: 1.0  # model depth multiple

models/hub/yolov5-panet.yaml

+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
 # Parameters
 nc: 80  # number of classes
 depth_multiple: 1.0  # model depth multiple

models/hub/yolov5l6.yaml

+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
 # Parameters
 nc: 80  # number of classes
 depth_multiple: 1.0  # model depth multiple

models/hub/yolov5m6.yaml

+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
 # Parameters
 nc: 80  # number of classes
 depth_multiple: 0.67  # model depth multiple

models/hub/yolov5s-ghost.yaml

+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
 # Parameters
 nc: 80  # number of classes
 depth_multiple: 0.33  # model depth multiple

models/hub/yolov5s-transformer.yaml

+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
 # Parameters
 nc: 80  # number of classes
 depth_multiple: 0.33  # model depth multiple

models/hub/yolov5s6.yaml

+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
 # Parameters
 nc: 80  # number of classes
 depth_multiple: 0.33  # model depth multiple

models/hub/yolov5x6.yaml

+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
 # Parameters
 nc: 80  # number of classes
 depth_multiple: 1.33  # model depth multiple

models/yolo.py

-"""YOLOv5-specific modules
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+YOLO-specific modules
 
 Usage:
     $ python path/to/models/yolo.py --cfg yolov5s.yaml

models/yolov5l.yaml

+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
 # Parameters
 nc: 80  # number of classes
 depth_multiple: 1.0  # model depth multiple

models/yolov5m.yaml

+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
 # Parameters
 nc: 80  # number of classes
 depth_multiple: 0.67  # model depth multiple

models/yolov5s.yaml

+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
 # Parameters
 nc: 80  # number of classes
 depth_multiple: 0.33  # model depth multiple

models/yolov5x.yaml

+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
 # Parameters
 nc: 80  # number of classes
 depth_multiple: 1.33  # model depth multiple

train.py

-"""Train a YOLOv5 model on a custom dataset
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Train a YOLOv5 model on a custom dataset
 
 Usage:
     $ python path/to/train.py --data coco128.yaml --weights yolov5s.pt --img 640
@@ -6,6 +8,7 @@ Usage:
 
 import argparse
 import logging
+import math
 import os
 import random
 import sys
@@ -13,7 +16,6 @@ import time
 from copy import deepcopy
 from pathlib import Path
 
-import math
 import numpy as np
 import torch
 import torch.distributed as dist

utils/activations.py

-# Activation functions
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Activation functions
+"""
 
 import torch
 import torch.nn as nn

utils/augmentations.py

-# YOLOv5 image augmentation functions
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Image augmentation functions
+"""
 
 import logging
+import math
 import random
 
 import cv2
-import math
 import numpy as np
 
 from utils.general import colorstr, segment2box, resample_segments, check_version

utils/autoanchor.py

-# Auto-anchor utils
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Auto-anchor utils
+"""
 
 import random
 

utils/callbacks.py

-#!/usr/bin/env python
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Callback utils
+"""
+
 
 class Callbacks:
     """"

utils/datasets.py

-# YOLOv5 dataset utils and dataloaders
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Dataloaders and dataset utils
+"""
 
 import glob
 import hashlib

utils/downloads.py

-# Download utils
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Download utils
+"""
 
 import os
 import platform

utils/flask_rest_api/README.md

 # Flask REST API
-[REST](https://en.wikipedia.org/wiki/Representational_state_transfer) [API](https://en.wikipedia.org/wiki/API)s are commonly used to expose Machine Learning (ML)  models to other services. This folder contains an example REST API created using Flask to expose the YOLOv5s model from [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/).
+
+[REST](https://en.wikipedia.org/wiki/Representational_state_transfer) [API](https://en.wikipedia.org/wiki/API)s are
+commonly used to expose Machine Learning (ML)  models to other services. This folder contains an example REST API
+created using Flask to expose the YOLOv5s model from [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/).
 
 ## Requirements
 
 [Flask](https://palletsprojects.com/p/flask/) is required. Install with:
+
 ```shell
 $ pip install Flask
 ```
@@ -65,4 +69,5 @@ The model inference results are returned as a JSON response:
 ]
 ```
 
-An example python script to perform inference using [requests](https://docs.python-requests.org/en/master/) is given in `example_request.py`
+An example python script to perform inference using [requests](https://docs.python-requests.org/en/master/) is given
+in `example_request.py`

utils/general.py

-# YOLOv5 general utils
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+General utils
+"""
 
 import contextlib
 import glob
 import logging
+import math
 import os
 import platform
 import random
@@ -16,7 +20,6 @@ from pathlib import Path
 from subprocess import check_output
 
 import cv2
-import math
 import numpy as np
 import pandas as pd
 import pkg_resources as pkg

utils/loggers/__init__.py

-# YOLOv5 experiment logging utils
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Logging utils
+"""
+
 import warnings
 from threading import Thread
 

utils/loggers/wandb/wandb_utils.py

@@ -507,4 +507,4 @@ def all_logging_disabled(highest_level=logging.CRITICAL):
     try:
         yield
     finally:
-        logging.disable(previous_level)
\ No newline at end of file
+        logging.disable(previous_level)

utils/loss.py

-# Loss functions
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Loss functions
+"""
 
 import torch
 import torch.nn as nn

utils/metrics.py

-# Model validation metrics
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Model validation metrics
+"""
 
+import math
 import warnings
 from pathlib import Path
 
-import math
 import matplotlib.pyplot as plt
 import numpy as np
 import torch

utils/plots.py

-# Plotting utils
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Plotting utils
+"""
 
 import math
 from copy import copy

utils/torch_utils.py

-# YOLOv5 PyTorch utils
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+PyTorch utils
+"""
 
 import datetime
 import logging
+import math
 import os
 import platform
 import subprocess
@@ -10,7 +14,6 @@ from contextlib import contextmanager
 from copy import deepcopy
 from pathlib import Path
 
-import math
 import torch
 import torch.backends.cudnn as cudnn
 import torch.distributed as dist

val.py

-"""Validate a trained YOLOv5 model accuracy on a custom dataset
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Validate a trained YOLOv5 model accuracy on a custom dataset
 
 Usage:
     $ python path/to/val.py --data coco128.yaml --weights yolov5s.pt --img 640