https://lutzroeder.github.io/Netron

Visualizer for neural network, deep learning and machine learning models
https://lutzroeder.github.io/Netron

ai coreml deep-learning deeplearning keras machine-learning machinelearning ml neural-network numpy onnx pytorch safetensors tensorflow tensorflow-lite visualizer

Last synced: about 2 months ago
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Visualizer for neural network, deep learning and machine learning models

• Host: GitHub
• URL: https://lutzroeder.github.io/Netron
• Owner: lutzroeder
• License: mit
• Created: 2010-12-26T12:53:43.000Z (about 14 years ago)
• Default Branch: main
• Last Pushed: 2024-10-30T03:30:53.000Z (3 months ago)
• Last Synced: 2024-10-30T03:59:10.087Z (3 months ago)
• Topics: ai, coreml, deep-learning, deeplearning, keras, machine-learning, machinelearning, ml, neural-network, numpy, onnx, pytorch, safetensors, tensorflow, tensorflow-lite, visualizer
• Language: JavaScript
• Homepage: https://netron.app
• Size: 57.4 MB
• Stars: 27,982
• Watchers: 300
• Forks: 2,773
• Open Issues: 23
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

Awesome Lists containing this project

• Awesome-CoreML-Models - Netron

README

        








Netron is a viewer for neural network, deep learning and machine learning models.

Netron supports ONNX, TensorFlow Lite, Core ML, Keras, Caffe, Darknet, PyTorch, TensorFlow.js, Safetensors and NumPy.

Netron has experimental support for TorchScript, TensorFlow, MXNet, OpenVINO, RKNN, ML.NET, ncnn, MNN, PaddlePaddle, GGUF and scikit-learn.



## Install

**macOS**: [**Download**](https://github.com/lutzroeder/netron/releases/latest) the `.dmg` file or run `brew install --cask netron`

**Linux**: [**Download**](https://github.com/lutzroeder/netron/releases/latest) the `.AppImage` file or run `snap install netron`

**Windows**: [**Download**](https://github.com/lutzroeder/netron/releases/latest) the `.exe` installer or run `winget install -s winget netron`

**Browser**: [**Start**](https://netron.app) the browser version.

**Python**: Run `pip install netron` and `netron [FILE]` or `netron.start('[FILE]')`.

## Models

Sample model files to download or open using the browser version:

 * **ONNX**: [squeezenet](https://github.com/onnx/models/raw/main/validated/vision/classification/squeezenet/model/squeezenet1.0-3.onnx) [[open](https://netron.app?url=https://github.com/onnx/models/raw/main/validated/vision/classification/squeezenet/model/squeezenet1.0-3.onnx)]

 * **TensorFlow Lite**: [yamnet](https://huggingface.co/thelou1s/yamnet/resolve/main/lite-model_yamnet_tflite_1.tflite) [[open](https://netron.app?url=https://huggingface.co/thelou1s/yamnet/blob/main/lite-model_yamnet_tflite_1.tflite)]

 * **TensorFlow**: [chessbot](https://github.com/srom/chessbot/raw/master/model/chessbot.pb) [[open](https://netron.app?url=https://github.com/srom/chessbot/raw/master/model/chessbot.pb)]

 * **Keras**: [mobilenet](https://github.com/aio-libs/aiohttp-demos/raw/master/demos/imagetagger/tests/data/mobilenet.h5) [[open](https://netron.app?url=https://github.com/aio-libs/aiohttp-demos/raw/master/demos/imagetagger/tests/data/mobilenet.h5)]

 * **TorchScript**: [traced_online_pred_layer](https://github.com/ApolloAuto/apollo/raw/master/modules/prediction/data/traced_online_pred_layer.pt) [[open](https://netron.app?url=https://github.com/ApolloAuto/apollo/raw/master/modules/prediction/data/traced_online_pred_layer.pt)]

 * **Core ML**: [exermote](https://github.com/Lausbert/Exermote/raw/master/ExermoteInference/ExermoteCoreML/ExermoteCoreML/Model/Exermote.mlmodel) [[open](https://netron.app?url=https://github.com/Lausbert/Exermote/raw/master/ExermoteInference/ExermoteCoreML/ExermoteCoreML/Model/Exermote.mlmodel)]

 * **Darknet**: [yolo](https://github.com/AlexeyAB/darknet/raw/master/cfg/yolo.cfg) [[open](https://netron.app?url=https://github.com/AlexeyAB/darknet/raw/master/cfg/yolo.cfg)]