# Netron — Neural Network Model Visualizer

> A cross-platform viewer for neural network, deep learning, and machine learning models supporting ONNX, TensorFlow, Keras, PyTorch, and dozens more formats.

## Install

Save as a script file and run:

# Netron — Neural Network Model Visualizer

## Quick Use
```bash
pip install netron
netron model.onnx
```

## Introduction
Netron is a lightweight, cross-platform viewer for neural network and machine learning models. It lets you inspect model architectures, layer parameters, and tensor shapes without writing any code, making debugging and documentation straightforward.

## What Netron Does
- Renders interactive graph visualizations of model architectures
- Displays layer properties, weights, and tensor dimensions
- Supports 40+ model formats including ONNX, TensorFlow SavedModel, PyTorch, Keras, TFLite, Core ML, and Caffe
- Exports model graphs to PNG or SVG for documentation
- Runs as a desktop app (Electron), browser app, or Python CLI

## Architecture Overview
Netron parses model files using format-specific deserializers written in JavaScript. Each format plugin reads the protobuf, flatbuffer, or binary schema and produces a normalized graph of nodes and edges. The rendering layer uses an HTML5 canvas with a custom layout engine to draw the computation graph. The Python package wraps a local HTTP server that serves the same browser UI.

## Self-Hosting & Configuration
- Install via pip: `pip install netron` or download the standalone desktop app
- Launch from CLI: `netron --host 0.0.0.0 --port 8080 model.onnx`
- Supports headless mode for CI pipelines: `netron --no-browser model.onnx`
- No database or external dependencies required
- Available as a snap, Homebrew cask, and Winget package

## Key Features
- Zero-config visualization with automatic format detection
- Side-by-side weight inspection for debugging quantization
- Search nodes by name or op type within large graphs
- Dark mode and customizable graph orientation (horizontal/vertical)
- Active maintenance with rapid support for new model formats

## Comparison with Similar Tools
- **TensorBoard** — focuses on training metrics and logging; Netron is purpose-built for static model inspection
- **Weights & Biases** — cloud-based experiment tracking; Netron is offline and file-based
- **ONNX GraphSurgeon** — programmatic graph editing; Netron is read-only visualization
- **Lutzroeder/netron vs. VisualDL** — VisualDL bundles PaddlePaddle ecosystem; Netron is framework-agnostic

## FAQ
**Q: Can Netron edit models?**
A: No. Netron is a read-only viewer. Use tools like ONNX GraphSurgeon or TensorFlow's graph transform for editing.

**Q: Does it work with quantized models?**
A: Yes. Netron displays quantized weights and supports INT8/FP16 formats across ONNX, TFLite, and Core ML.

**Q: Is there a web version I can use without installing?**
A: Yes. The project hosts a browser-based version that loads models client-side with no server upload.

**Q: What is the maximum model size Netron can handle?**
A: It handles models up to several GB, though very large graphs may render slowly in the browser.

## Sources
- https://github.com/lutzroeder/netron
- https://netron.app

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Source: https://tokrepo.com/en/workflows/asset-48fc02fa
Author: Script Depot