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Improved Usage example docstrings (#9075)

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* 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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Glenn Jocher authored and GitHub committed 2022-08-22 01:06:29 +02:00
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classify/predict.py
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Run classification inference on file/dir/URL/glob
Run YOLOv5 classification inference on images, videos, directories, and globs.
Usage:
$ python classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg
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
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# 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
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# 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