Improved Usage example docstrings (#9075)

classify/predict.py

 # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
 """
-Run classification inference on file/dir/URL/glob
-
-Usage:
-    $ python classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg
+Run YOLOv5 classification inference on images, videos, directories, and globs.
+
+Usage - sources:
+    $ python classify/predict.py --weights yolov5s.pt --source img.jpg        # image
+                                                               vid.mp4        # video
+                                                               path/          # directory
+                                                               'path/*.jpg'   # glob
+
+Usage - formats:
+    $ python classify/predict.py --weights yolov5s-cls.pt                 # PyTorch
+                                           yolov5s-cls.torchscript        # TorchScript
+                                           yolov5s-cls.onnx               # ONNX Runtime or OpenCV DNN with --dnn
+                                           yolov5s-cls.xml                # OpenVINO
+                                           yolov5s-cls.engine             # TensorRT
+                                           yolov5s-cls.mlmodel            # CoreML (macOS-only)
+                                           yolov5s-cls_saved_model        # TensorFlow SavedModel
+                                           yolov5s-cls.pb                 # TensorFlow GraphDef
+                                           yolov5s-cls.tflite             # TensorFlow Lite
+                                           yolov5s-cls_edgetpu.tflite     # TensorFlow Edge TPU
 """
 
 import argparse

classify/train.py

 # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
 """
 Train a YOLOv5 classifier model on a classification dataset
-Datasets: --data mnist, fashion-mnist, cifar10, cifar100, imagenette, imagewoof, imagenet, or 'path/to/custom/dataset'
-YOLOv5-cls models: --model yolov5n-cls.pt, yolov5s-cls.pt, yolov5m-cls.pt, yolov5l-cls.pt, yolov5x-cls.pt
-Torchvision models: --model resnet50, efficientnet_b0, etc. See https://pytorch.org/vision/stable/models.html
 
-Usage - Single-GPU and Multi-GPU DDP
+Usage - Single-GPU training:
     $ python classify/train.py --model yolov5s-cls.pt --data imagenette160 --epochs 5 --img 128
+
+Usage - Multi-GPU DDP training:
     $ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
+
+Datasets:           --data mnist, fashion-mnist, cifar10, cifar100, imagenette, imagewoof, imagenet, or 'path/to/data'
+YOLOv5-cls models:  --model yolov5n-cls.pt, yolov5s-cls.pt, yolov5m-cls.pt, yolov5l-cls.pt, yolov5x-cls.pt
+Torchvision models: --model resnet50, efficientnet_b0, etc. See https://pytorch.org/vision/stable/models.html
 """
 
 import argparse

classify/val.py

 # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
 """
-Validate a classification model on a dataset
+Validate a trained YOLOv5 classification model on a classification dataset
 
 Usage:
     $ bash data/scripts/get_imagenet.sh --val  # download ImageNet val split (6.3G, 50000 images)
-    $ python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224  # validate
+    $ python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224  # validate ImageNet
+
+Usage - formats:
+    $ python classify/val.py --weights yolov5s-cls.pt                 # PyTorch
+                                       yolov5s-cls.torchscript        # TorchScript
+                                       yolov5s-cls.onnx               # ONNX Runtime or OpenCV DNN with --dnn
+                                       yolov5s-cls.xml                # OpenVINO
+                                       yolov5s-cls.engine             # TensorRT
+                                       yolov5s-cls.mlmodel            # CoreML (macOS-only)
+                                       yolov5s-cls_saved_model        # TensorFlow SavedModel
+                                       yolov5s-cls.pb                 # TensorFlow GraphDef
+                                       yolov5s-cls.tflite             # TensorFlow Lite
+                                       yolov5s-cls_edgetpu.tflite     # TensorFlow Edge TPU
 """
 
 import argparse

detect.py

 # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
 """
-Run inference on images, videos, directories, streams, etc.
+Run YOLOv5 detection inference on images, videos, directories, streams, etc.
 
 Usage - sources:
-    $ python path/to/detect.py --weights yolov5s.pt --source 0              # webcam
+    $ python detect.py --weights yolov5s.pt --source 0                               # webcam
                                                      img.jpg                         # image
                                                      vid.mp4                         # video
                                                      path/                           # directory
@@ -12,7 +12,7 @@ Usage - sources:
                                                      'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
 
 Usage - formats:
-    $ python path/to/detect.py --weights yolov5s.pt                 # PyTorch
+    $ python detect.py --weights yolov5s.pt                 # PyTorch
                                  yolov5s.torchscript        # TorchScript
                                  yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
                                  yolov5s.xml                # OpenVINO

export.py

@@ -21,10 +21,10 @@ Requirements:
     $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow  # GPU
 
 Usage:
-    $ python path/to/export.py --weights yolov5s.pt --include torchscript onnx openvino engine coreml tflite ...
+    $ python export.py --weights yolov5s.pt --include torchscript onnx openvino engine coreml tflite ...
 
 Inference:
-    $ python path/to/detect.py --weights yolov5s.pt                 # PyTorch
+    $ python detect.py --weights yolov5s.pt                 # PyTorch
                                  yolov5s.torchscript        # TorchScript
                                  yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
                                  yolov5s.xml                # OpenVINO

hubconf.py

 # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
 """
-PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5/
+PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5
 
 Usage:
     import torch
     model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
-    model = torch.hub.load('ultralytics/yolov5:master', 'custom', 'path/to/yolov5s.onnx')  # file from branch
+    model = torch.hub.load('ultralytics/yolov5:master', 'custom', 'path/to/yolov5s.onnx')  # custom model from branch
 """
 
 import torch

models/tf.py

@@ -7,7 +7,7 @@ Usage:
     $ python models/tf.py --weights yolov5s.pt
 
 Export:
-    $ python path/to/export.py --weights yolov5s.pt --include saved_model pb tflite tfjs
+    $ python export.py --weights yolov5s.pt --include saved_model pb tflite tfjs
 """
 
 import argparse

models/yolo.py

@@ -3,7 +3,7 @@
 YOLO-specific modules
 
 Usage:
-    $ python path/to/models/yolo.py --cfg yolov5s.yaml
+    $ python models/yolo.py --cfg yolov5s.yaml
 """
 
 import argparse

train.py

 # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
 """
 Train a YOLOv5 model on a custom dataset.
-
 Models and datasets download automatically from the latest YOLOv5 release.
+
+Usage - Single-GPU training:
+    $ python train.py --data coco128.yaml --weights yolov5s.pt --img 640  # from pretrained (recommended)
+    $ python train.py --data coco128.yaml --weights '' --cfg yolov5s.yaml --img 640  # from scratch
+
+Usage - Multi-GPU DDP training:
+    $ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 train.py --data coco128.yaml --weights yolov5s.pt --img 640 --device 0,1,2,3
+
 Models:     https://github.com/ultralytics/yolov5/tree/master/models
 Datasets:   https://github.com/ultralytics/yolov5/tree/master/data
 Tutorial:   https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data
-
-Usage:
-    $ python path/to/train.py --data coco128.yaml --weights yolov5s.pt --img 640  # from pretrained (RECOMMENDED)
-    $ python path/to/train.py --data coco128.yaml --weights '' --cfg yolov5s.yaml --img 640  # from scratch
 """
 
 import argparse

val.py

 # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
 """
-Validate a trained YOLOv5 model accuracy on a custom dataset
+Validate a trained YOLOv5 detection model on a detection dataset
 
 Usage:
-    $ python path/to/val.py --weights yolov5s.pt --data coco128.yaml --img 640
+    $ python val.py --weights yolov5s.pt --data coco128.yaml --img 640
 
 Usage - formats:
-    $ python path/to/val.py --weights yolov5s.pt                 # PyTorch
+    $ python val.py --weights yolov5s.pt                 # PyTorch
                               yolov5s.torchscript        # TorchScript
                               yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
                               yolov5s.xml                # OpenVINO