Netron
Visualizer for neural network, deep learning and machine learning models
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What is Netron?
Netron is a viewer for neural network, deep learning, and machine learning models. It visualizes model architecture in an interactive format, displaying layer details including shapes, parameters, and connections. The tool shows model metadata like input/output dimensions, weights, and quantization information, making it easier for developers to understand and debug AI models.
Key features include support for 40+ model formats such as ONNX, TensorFlow, PyTorch, Keras, TensorFlow Lite, CoreML, Caffe, Darknet, MXNet, PaddlePaddle, ncnn, MNN, and TensorFlow.js. It offers a drag-and-drop interface where users simply upload their model file. Users can click nodes to see layer details, hover over connections to see tensor shapes, scroll to zoom, drag to pan, and use search to find specific layers.
Netron is designed for machine learning engineers, data scientists, AI researchers, and students who need to inspect, analyze, and explain model internals. It helps teams understand complex neural network architectures layer by layer, troubleshoot model components, and communicate model structure visually instead of through code. The tool is developed by Lutz Roeder and is widely used in modern AI pipelines.
The tool is available as a browser version at netron.app, desktop applications for macOS, Windows, and Linux, and as a Python package. It supports cross-platform use without complex dependencies, making neural network visualization accessible to anyone working with ML models regardless of their operating system.
Netron pricing
Pricing model: Freemium
Netron is completely free and open-source under MIT license. There is no paid tier or subscription. The browser version at netron.app is free to use, desktop applications for macOS, Windows, and Linux are free downloads, and the Python package via pip is free. All features including support for 40+ model formats are included in the free version.
Netron pros
- Supports 40+ model formats including ONNX, TensorFlow, PyTorch, Keras, CoreML
- Open-source and completely free with no paid plans
- Interactive browser-based visualization without installation required
- Drag-and-drop interface for instant model file uploading
- Cross-platform support on Windows, macOS, Linux, and browser
- Click nodes to see detailed layer information and parameters
- Hover over connections to view tensor shapes
- Shows model metadata including input/output dimensions and weights
- Lightweight with no complex dependencies to install
- Search functionality (Ctrl/Cmd+F) to find specific layers quickly
- Right-click options for properties and layer search
- Zoom and pan controls for navigating large model graphs
- Quantization information displayed for supported models
- Python package available for server-side visualization
- Extensive sample model files available in browser version
Netron cons
- No model editing capabilities - view-only tool
- PyTorch support is experimental with limited fidelity
- Large graphs can have performance issues and be hard to read
- Some PyTorch save formats lose detail depending on file writing
- No batch image export functionality for visualizations
- Single main contributor keeps project development slow
- Very complex nested architectures can be difficult to navigate
- No Jupyter widget support yet (feature requested)
Frequently asked questions about Netron
What is Netron used for?
Netron is a viewer for neural network, deep learning, and machine learning models. It is used to visualize and analyze model architecture, layer structure, and parameter flow. Developers and researchers use it to understand model internals, debug AI models, inspect layer details including shapes and parameters, and communicate model structure visually.
What model formats does Netron support?
Netron supports 40+ model formats including ONNX, TensorFlow Lite, Core ML, Keras, Caffe, Darknet, PyTorch, TensorFlow.js, Safetensors, and NumPy. It has experimental support for TorchScript, torch.export, ExecuTorch, TensorFlow, OpenVINO, RKNN, ncnn, MNN, PaddlePaddle, GGUF, and scikit-learn.
Is Netron free to use?
Yes, Netron is completely free and open-source under the MIT license. There are no paid plans or subscriptions. The browser version, desktop applications for all major operating systems, and the Python package are all free to use with full feature access.
How do I use Netron?
To use Netron, visit netron.app and drag and drop your model file into the browser. Alternatively, download the desktop app for your operating system or install via pip. Once opened, click nodes to see layer details, hover over connections to see tensor shapes, scroll to zoom, drag to pan, use right-click for options, and use Ctrl/Cmd+F to search for specific layers.
Can I edit models in Netron?
No, Netron is a view-only tool and does not support model editing. It is designed for visualizing and inspecting model architecture, not modifying it. Model editing must be done through framework-specific tools like the ONNX Python API.
Does Netron work on mobile devices?
Netron is available as desktop applications for macOS, Windows, and Linux, and as a browser-based version. The browser version works on devices with a modern web browser. There is also an Apple Watch app called Netron by Hemanth Alluri for network diagnostics, but this is a different app from the neural network visualizer.
Who created Netron?
Netron was created in 2018 by Lutz Roeder as an open-source tool to address the need for a user-friendly interface to visualize and debug machine learning models. The project is hosted on GitHub and evolves with community contributions.
Can I use Netron without installing anything?
Yes, you can use Netron entirely in your browser at netron.app without any installation. Simply drag and drop your model file into the browser window to start visualizing. This works for most model formats and is ideal for quick inspection without setting up software.
What are the system requirements for Netron?
Netron has minimal system requirements. The browser version works on any device with a modern web browser. Desktop applications run on macOS, Windows, and Linux without complex dependencies. The Python package requires Python and can be installed via pip. The tool is lightweight and designed to be efficient.
Does Netron support large models?
Netron can load large models, but very large graphs may have performance issues and can be difficult to read. There are open feature requests for hierarchical expansion of large graphs and improved large graph layout performance. For some files with fewer than 8 million elements, it can auto-compute min, max, and std for floating-point tensors.