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ultralytics 8.0.141 create new SettingsManager (#3790)

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Glenn Jocher 2023-07-23 16:03:34 +02:00 • committed by GitHub
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docs/yolov5/tutorials/pytorch_hub_model_loading.md
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@ -4,7 +4,7 @@ description: Detailed guide on loading YOLOv5 from PyTorch Hub. Includes example
keywords: Ultralytics, YOLOv5, PyTorch, loading YOLOv5, PyTorch Hub, inference, multi-GPU inference, training
---
📚 This guide explains how to load YOLOv5 🚀 from PyTorch Hub at [https://pytorch.org/hub/ultralytics_yolov5](https://pytorch.org/hub/ultralytics_yolov5).
📚 This guide explains how to load YOLOv5 🚀 from PyTorch Hub at [https://pytorch.org/hub/ultralytics_yolov5](https://pytorch.org/hub/ultralytics_yolov5).
UPDATED 26 March 2023.
## Before You Start
@ -65,7 +65,7 @@ im2 = cv2.imread('bus.jpg')[..., ::-1] # OpenCV image (BGR to RGB)
results = model([im1, im2], size=640) # batch of images
# Results
results.print()
results.print()
results.save() # or .show()
results.xyxy[0] # im1 predictions (tensor)
@ -301,7 +301,7 @@ model = torch.hub.load('path/to/yolov5', 'custom', path='path/to/best.pt', sourc
PyTorch Hub supports inference on most YOLOv5 export formats, including custom trained models. See [TFLite, ONNX, CoreML, TensorRT Export tutorial](https://docs.ultralytics.com/yolov5/tutorials/model_export) for details on exporting models.
💡 ProTip: **TensorRT** may be up to 2-5X faster than PyTorch on [**GPU benchmarks**](https://github.com/ultralytics/yolov5/pull/6963)
💡 ProTip: **TensorRT** may be up to 2-5X faster than PyTorch on [**GPU benchmarks**](https://github.com/ultralytics/yolov5/pull/6963)
💡 ProTip: **ONNX** and **OpenVINO** may be up to 2-3X faster than PyTorch on [**CPU benchmarks**](https://github.com/ultralytics/yolov5/pull/6613)
```python
@ -328,4 +328,4 @@ YOLOv5 is designed to be run in the following up-to-date verified environments (
<a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a>
If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py) and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py) on macOS, Windows, and Ubuntu every 24 hours and on every commit.
If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py) and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py) on macOS, Windows, and Ubuntu every 24 hours and on every commit.