ultralytics 8.0.210 docs updates and bug fixes (#6365)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: Ceyda Cinarel (재이다) <15624271+cceyda@users.noreply.github.com> Co-authored-by: DennisJ <106725464+DennisJcy@users.noreply.github.com> Co-authored-by: Dickson Neoh <dickson.neoh@gmail.com> Co-authored-by: Sergiu Waxmann <47978446+sergiuwaxmann@users.noreply.github.com> Co-authored-by: Luca Serra <56681681+luca-serra@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
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commit
0473da00a9
18 changed files with 125 additions and 57 deletions
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@ -78,7 +78,7 @@ The Triton Model Repository is a storage location where Triton can access and lo
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Path(onnx_file).rename(triton_model_path / '1' / 'model.onnx')
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# Create config file
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(triton_model_path / 'config.pdtxt').touch()
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(triton_model_path / 'config.pbtxt').touch()
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```
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## Running Triton Inference Server
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@ -34,10 +34,10 @@ Welcome to the Integrations guide for [Ultralytics HUB](https://hub.ultralytics.
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| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |
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| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize` |
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| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset` |
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| [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half` |
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| [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8` |
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| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |
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| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms` |
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| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras` |
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| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8` |
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| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | `imgsz` |
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| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8` |
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| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz` |
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@ -47,10 +47,10 @@ We also support a variety of model export formats for deployment in different en
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| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |
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| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize` |
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| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset` |
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| [OpenVINO](openvino.md) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half` |
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| [OpenVINO](openvino.md) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8` |
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| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |
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| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms` |
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| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras` |
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| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8` |
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| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | `imgsz` |
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| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8` |
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| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz` |
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@ -80,10 +80,10 @@ Benchmarks will attempt to run automatically on all possible export formats belo
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| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |
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| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize` |
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| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset` |
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| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half` |
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| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8` |
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| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |
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| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms` |
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| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras` |
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| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8` |
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| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | `imgsz` |
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| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8` |
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| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz` |
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@ -96,10 +96,10 @@ Available YOLOv8 export formats are in the table below. You can export to any fo
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| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |
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| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize` |
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| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset` |
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| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half` |
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| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8` |
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| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |
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| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms` |
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| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras` |
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| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8` |
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| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | `imgsz` |
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| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8` |
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| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz` |
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@ -171,10 +171,10 @@ Available YOLOv8 export formats are in the table below. You can predict or valid
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| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |
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| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize` |
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| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset` |
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| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half` |
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| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8` |
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| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |
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| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms` |
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| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras` |
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| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8` |
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| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | `imgsz` |
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| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8` |
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| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz` |
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@ -175,10 +175,10 @@ Available YOLOv8 export formats are in the table below. You can export to any fo
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| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |
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| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize` |
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| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset` |
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| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half` |
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| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8` |
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| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |
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| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms` |
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| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras` |
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| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8` |
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| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | `imgsz` |
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| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8` |
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| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz` |
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@ -22,7 +22,7 @@ keywords: Ultralytics, YOLOv5, Deep Learning, Object detection, PyTorch, Tutoria
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<br>
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<br>
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Welcome to the Ultralytics' [YOLOv5](https://github.com/ultralytics/yolov5) 🚀 Documentation! YOLOv5, the fifth iteration of the revolutionary "You Only Look Once" object detection model, is designed to deliver high-speed, high-accuracy results in real-time.
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Welcome to the Ultralytics' <a href="https://github.com/ultralytics/yolov5">YOLOv5</a>🚀 Documentation! YOLOv5, the fifth iteration of the revolutionary "You Only Look Once" object detection model, is designed to deliver high-speed, high-accuracy results in real-time.
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<br><br>
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Built on PyTorch, this powerful deep learning framework has garnered immense popularity for its versatility, ease of use, and high performance. Our documentation guides you through the installation process, explains the architectural nuances of the model, showcases various use-cases, and provides a series of detailed tutorials. These resources will help you harness the full potential of YOLOv5 for your computer vision projects. Let's get started!
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@ -16,8 +16,18 @@ const applyAutoTheme = () => {
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// Function that checks and applies light/dark theme based on the user's preference (if auto theme is enabled)
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function checkAutoTheme() {
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// Retrieve the palette from local storage
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const palette = localStorage.getItem("/.__palette");
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// Array of supported language codes -> each language has its own palette (stored in local storage)
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const supportedLangCodes = ["en", "zh", "ko", "ja", "ru", "de", "fr", "es", "pt"];
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// Get the URL path
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const path = window.location.pathname;
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// Extract the language code from the URL (assuming it's in the format /xx/...)
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const langCode = path.split("/")[1];
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// Check if the extracted language code is in the supported languages
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const isValidLangCode = supportedLangCodes.includes(langCode);
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// Construct the local storage key based on the language code if valid, otherwise default to the root key
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const localStorageKey = isValidLangCode ? `/${langCode}/.__palette` : "/.__palette";
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// Retrieve the palette from local storage using the constructed key
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const palette = localStorage.getItem(localStorageKey);
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if (palette) {
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// Check if the palette's index is 0 (auto theme)
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const paletteObj = JSON.parse(palette);
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}
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}
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}
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// ! No need to run the function when the script loads as by default the theme is determined by the user's preference (if auto theme is enabled)
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// checkAutoTheme();
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// Run the function when the user's preference changes (when the user changes their system theme)
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// Run function when the script loads
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checkAutoTheme();
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// Re-run the function when the user's preference changes (when the user changes their system theme)
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window.matchMedia("(prefers-color-scheme: light)").addEventListener("change", checkAutoTheme);
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window.matchMedia("(prefers-color-scheme: dark)").addEventListener("change", checkAutoTheme);
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@ -1,41 +1,42 @@
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/* Table format like GitHub ----------------------------------------------------------------------------------------- */
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th, td {
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border: 1px solid var(--md-typeset-table-color);
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border-spacing: 0;
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border-bottom: none;
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border-left: none;
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border-top: none;
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th,
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td {
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border: 1px solid var(--md-typeset-table-color);
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border-spacing: 0;
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border-bottom: none;
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border-left: none;
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border-top: none;
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}
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.md-typeset__table {
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line-height: 1;
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line-height: 1;
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}
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.md-typeset__table table:not([class]) {
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font-size: .74rem;
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border-right: none;
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font-size: 0.74rem;
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border-right: none;
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}
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.md-typeset__table table:not([class]) td,
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.md-typeset__table table:not([class]) th {
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padding: 9px;
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padding: 9px;
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}
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/* light mode alternating table bg colors */
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.md-typeset__table tr:nth-child(2n) {
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background-color: #f8f8f8;
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background-color: #f6f8fa;
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}
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/* dark mode alternating table bg colors */
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body.md-theme--slate .md-typeset__table tr:nth-child(2n) {
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background-color: hsla(207, 26%, 17%, 1);
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[data-md-color-scheme="slate"] .md-typeset__table tr:nth-child(2n) {
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background-color: #161b22;
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}
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/* Table format like GitHub ----------------------------------------------------------------------------------------- */
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/* Code block vertical scroll */
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div.highlight {
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max-height: 20rem;
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overflow-y: auto; /* for adding a scrollbar when needed */
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max-height: 20rem;
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overflow-y: auto; /* for adding a scrollbar when needed */
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}
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/* Set content width */
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