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lutzroeder / netron

Visualizer for neural network, deep learning and machine learning models

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Our Agentic Context Augmented Generation (Agentic CAG) engine loads full source files into context, avoiding the fragmentation of traditional RAG systems. Ask questions about the architecture, dependencies, or specific features to see it in action.

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Repository Summary (README)

Preview
<div align="center"> <img width="400px" height="100px" src="https://github.com/lutzroeder/netron/raw/main/.github/logo-light.svg#gh-light-mode-only"> <img width="400px" height="100px" src="https://github.com/lutzroeder/netron/raw/main/.github/logo-dark.svg#gh-dark-mode-only"> </div>

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

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

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

<p align='center'><a href='https://www.lutzroeder.com/ai'><img src='.github/screenshot.png' width='800'></a></p>

Install

Browser: Start the browser version.

macOS: Download the .dmg file or run brew install --cask netron.

Linux: Download the .deb or .rpm file.

Windows: Download the .exe installer or run winget install -s winget netron.

Python: pip install netron, then run netron [FILE] or netron.start('[FILE]').

Models

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