lutzroeder--netron
wehub-resource-sync
46 行
3.7 KiB
Markdown
46 行
3.7 KiB
Markdown
<!-- WEHUB_ZH_README -->
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> [!NOTE]
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> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。
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> [English](./README.en.md) · [原始项目](https://github.com/lutzroeder/netron) · [上游 README](https://github.com/lutzroeder/netron/blob/HEAD/README.md)
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> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
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<div align="center">
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<img width="400px" height="100px" src="https://github.com/lutzroeder/netron/raw/main/.github/logo-light.svg#gh-light-mode-only">
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<img width="400px" height="100px" src="https://github.com/lutzroeder/netron/raw/main/.github/logo-dark.svg#gh-dark-mode-only">
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</div>
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Netron 是一款用于查看神经网络、深度学习与机器学习模型的查看器。
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Netron 支持 ONNX、TensorFlow Lite、PyTorch、torch.export、ExecuTorch、TorchScript、TensorFlow、Core ML、OpenVINO、Keras、Caffe、Darknet、Safetensors 和 NumPy。
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Netron 对 MLIR、JAX、GGUF、RKNN、ncnn、MNN、PaddlePaddle 和 scikit-learn 提供实验性支持。
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<p align='center'><a href='https://www.lutzroeder.com/ai'><img src='.github/screenshot.png' width='800'></a></p>
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## 安装
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**浏览器**：[**启动**](https://netron.app) 浏览器版本。
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**macOS**：[**下载**](https://github.com/lutzroeder/netron/releases/latest) `.dmg` 文件，或运行 `brew install --cask netron`。
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**Linux**：[**下载**](https://github.com/lutzroeder/netron/releases/latest) `.deb` 或 `.rpm` 文件。
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**Windows**：[**下载**](https://github.com/lutzroeder/netron/releases/latest) `.exe` 安装程序，或运行 `winget install -s winget netron`。
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**Python**：`pip install netron`，然后运行 `netron [FILE]` 或 `netron.start('[FILE]')`。
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## 模型
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可使用浏览器版本下载或打开的示例模型文件：
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* **ONNX**：[squeezenet](https://github.com/onnx/models/raw/main/validated/vision/classification/squeezenet/model/squeezenet1.0-3.onnx) [[打开](https://netron.app?url=https://github.com/onnx/models/raw/main/validated/vision/classification/squeezenet/model/squeezenet1.0-3.onnx)]
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* **TorchScript**：[traced_online_pred_layer](https://github.com/ApolloAuto/apollo/raw/master/modules/prediction/data/traced_online_pred_layer.pt) [[打开](https://netron.app?url=https://github.com/ApolloAuto/apollo/raw/master/modules/prediction/data/traced_online_pred_layer.pt)]
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* **TensorFlow Lite**：[yamnet](https://huggingface.co/thelou1s/yamnet/resolve/main/lite-model_yamnet_tflite_1.tflite) [[打开](https://netron.app?url=https://huggingface.co/thelou1s/yamnet/blob/main/lite-model_yamnet_tflite_1.tflite)]
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* **TensorFlow**：[chessbot](https://github.com/srom/chessbot/raw/master/model/chessbot.pb) [[打开](https://netron.app?url=https://github.com/srom/chessbot/raw/master/model/chessbot.pb)]
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* **Keras**：[mobilenet](https://github.com/aio-libs/aiohttp-demos/raw/master/demos/imagetagger/tests/data/mobilenet.h5) [[打开](https://netron.app?url=https://github.com/aio-libs/aiohttp-demos/raw/master/demos/imagetagger/tests/data/mobilenet.h5)]
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* **MLIR**：[edge_detection](https://github.com/iree-org/iree/raw/main/tests/e2e/stablehlo_models/edge_detection.mlir) [[打开](https://netron.app?url=https://github.com/iree-org/iree/blob/main/tests/e2e/stablehlo_models/edge_detection.mlir)]
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* **Core ML**：[exermote](https://github.com/Lausbert/Exermote/raw/master/ExermoteInference/ExermoteCoreML/ExermoteCoreML/Model/Exermote.mlmodel) [[打开](https://netron.app?url=https://github.com/Lausbert/Exermote/raw/master/ExermoteInference/ExermoteCoreML/ExermoteCoreML/Model/Exermote.mlmodel)]
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* **Darknet**：[yolo](https://github.com/AlexeyAB/darknet/raw/master/cfg/yolo.cfg) [[打开](https://netron.app?url=https://github.com/AlexeyAB/darknet/raw/master/cfg/yolo.cfg)]
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