Improved Usage example docstrings (#9075)
* Updated Usage examples * Update detect.py Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update predict.py Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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Run classification inference on file/dir/URL/glob
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Run YOLOv5 classification inference on images, videos, directories, and globs.
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Usage:
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$ python classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg
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Usage - sources:
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$ python classify/predict.py --weights yolov5s.pt --source img.jpg # image
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vid.mp4 # video
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path/ # directory
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'path/*.jpg' # glob
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Usage - formats:
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$ python classify/predict.py --weights yolov5s-cls.pt # PyTorch
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yolov5s-cls.torchscript # TorchScript
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yolov5s-cls.onnx # ONNX Runtime or OpenCV DNN with --dnn
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yolov5s-cls.xml # OpenVINO
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yolov5s-cls.engine # TensorRT
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yolov5s-cls.mlmodel # CoreML (macOS-only)
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yolov5s-cls_saved_model # TensorFlow SavedModel
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yolov5s-cls.pb # TensorFlow GraphDef
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yolov5s-cls.tflite # TensorFlow Lite
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yolov5s-cls_edgetpu.tflite # TensorFlow Edge TPU
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"""
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import argparse
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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Train a YOLOv5 classifier model on a classification dataset
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Datasets: --data mnist, fashion-mnist, cifar10, cifar100, imagenette, imagewoof, imagenet, or 'path/to/custom/dataset'
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YOLOv5-cls models: --model yolov5n-cls.pt, yolov5s-cls.pt, yolov5m-cls.pt, yolov5l-cls.pt, yolov5x-cls.pt
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Torchvision models: --model resnet50, efficientnet_b0, etc. See https://pytorch.org/vision/stable/models.html
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Usage - Single-GPU and Multi-GPU DDP
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Usage - Single-GPU training:
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$ python classify/train.py --model yolov5s-cls.pt --data imagenette160 --epochs 5 --img 128
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Usage - Multi-GPU DDP training:
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$ 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
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Datasets: --data mnist, fashion-mnist, cifar10, cifar100, imagenette, imagewoof, imagenet, or 'path/to/data'
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YOLOv5-cls models: --model yolov5n-cls.pt, yolov5s-cls.pt, yolov5m-cls.pt, yolov5l-cls.pt, yolov5x-cls.pt
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Torchvision models: --model resnet50, efficientnet_b0, etc. See https://pytorch.org/vision/stable/models.html
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"""
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import argparse
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@@ -1,10 +1,22 @@
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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Validate a classification model on a dataset
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Validate a trained YOLOv5 classification model on a classification dataset
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Usage:
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$ bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images)
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$ python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate
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$ python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate ImageNet
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Usage - formats:
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$ python classify/val.py --weights yolov5s-cls.pt # PyTorch
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yolov5s-cls.torchscript # TorchScript
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yolov5s-cls.onnx # ONNX Runtime or OpenCV DNN with --dnn
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yolov5s-cls.xml # OpenVINO
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yolov5s-cls.engine # TensorRT
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yolov5s-cls.mlmodel # CoreML (macOS-only)
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yolov5s-cls_saved_model # TensorFlow SavedModel
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yolov5s-cls.pb # TensorFlow GraphDef
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yolov5s-cls.tflite # TensorFlow Lite
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yolov5s-cls_edgetpu.tflite # TensorFlow Edge TPU
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"""
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import argparse
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@@ -1,27 +1,27 @@
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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Run inference on images, videos, directories, streams, etc.
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Run YOLOv5 detection inference on images, videos, directories, streams, etc.
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Usage - sources:
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$ python path/to/detect.py --weights yolov5s.pt --source 0 # webcam
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img.jpg # image
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vid.mp4 # video
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path/ # directory
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'path/*.jpg' # glob
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'https://youtu.be/Zgi9g1ksQHc' # YouTube
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'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
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$ python detect.py --weights yolov5s.pt --source 0 # webcam
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img.jpg # image
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vid.mp4 # video
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path/ # directory
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'path/*.jpg' # glob
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'https://youtu.be/Zgi9g1ksQHc' # YouTube
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'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
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Usage - formats:
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$ python path/to/detect.py --weights yolov5s.pt # PyTorch
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yolov5s.torchscript # TorchScript
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yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
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yolov5s.xml # OpenVINO
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yolov5s.engine # TensorRT
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yolov5s.mlmodel # CoreML (macOS-only)
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yolov5s_saved_model # TensorFlow SavedModel
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yolov5s.pb # TensorFlow GraphDef
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yolov5s.tflite # TensorFlow Lite
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yolov5s_edgetpu.tflite # TensorFlow Edge TPU
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$ python detect.py --weights yolov5s.pt # PyTorch
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yolov5s.torchscript # TorchScript
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yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
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yolov5s.xml # OpenVINO
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yolov5s.engine # TensorRT
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yolov5s.mlmodel # CoreML (macOS-only)
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yolov5s_saved_model # TensorFlow SavedModel
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yolov5s.pb # TensorFlow GraphDef
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yolov5s.tflite # TensorFlow Lite
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yolov5s_edgetpu.tflite # TensorFlow Edge TPU
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"""
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import argparse
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@@ -21,19 +21,19 @@ Requirements:
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$ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU
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Usage:
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$ python path/to/export.py --weights yolov5s.pt --include torchscript onnx openvino engine coreml tflite ...
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$ python export.py --weights yolov5s.pt --include torchscript onnx openvino engine coreml tflite ...
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Inference:
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$ python path/to/detect.py --weights yolov5s.pt # PyTorch
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yolov5s.torchscript # TorchScript
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yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
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yolov5s.xml # OpenVINO
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yolov5s.engine # TensorRT
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yolov5s.mlmodel # CoreML (macOS-only)
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yolov5s_saved_model # TensorFlow SavedModel
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yolov5s.pb # TensorFlow GraphDef
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yolov5s.tflite # TensorFlow Lite
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yolov5s_edgetpu.tflite # TensorFlow Edge TPU
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$ python detect.py --weights yolov5s.pt # PyTorch
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yolov5s.torchscript # TorchScript
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yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
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yolov5s.xml # OpenVINO
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yolov5s.engine # TensorRT
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yolov5s.mlmodel # CoreML (macOS-only)
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yolov5s_saved_model # TensorFlow SavedModel
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yolov5s.pb # TensorFlow GraphDef
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yolov5s.tflite # TensorFlow Lite
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yolov5s_edgetpu.tflite # TensorFlow Edge TPU
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TensorFlow.js:
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$ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5/
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PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5
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Usage:
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import torch
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model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
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model = torch.hub.load('ultralytics/yolov5:master', 'custom', 'path/to/yolov5s.onnx') # file from branch
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model = torch.hub.load('ultralytics/yolov5:master', 'custom', 'path/to/yolov5s.onnx') # custom model from branch
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"""
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import torch
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$ python models/tf.py --weights yolov5s.pt
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Export:
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$ python path/to/export.py --weights yolov5s.pt --include saved_model pb tflite tfjs
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$ python export.py --weights yolov5s.pt --include saved_model pb tflite tfjs
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"""
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import argparse
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YOLO-specific modules
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Usage:
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$ python path/to/models/yolo.py --cfg yolov5s.yaml
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$ python models/yolo.py --cfg yolov5s.yaml
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"""
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import argparse
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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Train a YOLOv5 model on a custom dataset.
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Models and datasets download automatically from the latest YOLOv5 release.
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Models: https://github.com/ultralytics/yolov5/tree/master/models
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Datasets: https://github.com/ultralytics/yolov5/tree/master/data
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Tutorial: https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data
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Usage:
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$ python path/to/train.py --data coco128.yaml --weights yolov5s.pt --img 640 # from pretrained (RECOMMENDED)
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$ python path/to/train.py --data coco128.yaml --weights '' --cfg yolov5s.yaml --img 640 # from scratch
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Usage - Single-GPU training:
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$ python train.py --data coco128.yaml --weights yolov5s.pt --img 640 # from pretrained (recommended)
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$ python train.py --data coco128.yaml --weights '' --cfg yolov5s.yaml --img 640 # from scratch
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Usage - Multi-GPU DDP training:
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$ 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
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Models: https://github.com/ultralytics/yolov5/tree/master/models
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Datasets: https://github.com/ultralytics/yolov5/tree/master/data
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Tutorial: https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data
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"""
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import argparse
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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Validate a trained YOLOv5 model accuracy on a custom dataset
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Validate a trained YOLOv5 detection model on a detection dataset
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Usage:
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$ python path/to/val.py --weights yolov5s.pt --data coco128.yaml --img 640
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$ python val.py --weights yolov5s.pt --data coco128.yaml --img 640
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Usage - formats:
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$ python path/to/val.py --weights yolov5s.pt # PyTorch
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yolov5s.torchscript # TorchScript
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yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
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yolov5s.xml # OpenVINO
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yolov5s.engine # TensorRT
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yolov5s.mlmodel # CoreML (macOS-only)
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yolov5s_saved_model # TensorFlow SavedModel
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yolov5s.pb # TensorFlow GraphDef
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yolov5s.tflite # TensorFlow Lite
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yolov5s_edgetpu.tflite # TensorFlow Edge TPU
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$ python val.py --weights yolov5s.pt # PyTorch
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yolov5s.torchscript # TorchScript
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yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
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yolov5s.xml # OpenVINO
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yolov5s.engine # TensorRT
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yolov5s.mlmodel # CoreML (macOS-only)
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yolov5s_saved_model # TensorFlow SavedModel
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yolov5s.pb # TensorFlow GraphDef
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yolov5s.tflite # TensorFlow Lite
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yolov5s_edgetpu.tflite # TensorFlow Edge TPU
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"""
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import argparse
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