netron
Viewer for neural network, deep learning and machine learning models.
Decision gist · record as of 2026-08-14
Yes. Netron is a lightweight, actively maintained, dependency-free viewer for a broad range of ML model formats. It solves a genuine need for developers and researchers who need to inspect model structure without writing code. The MIT license is permissive, there are no known vulnerabilities, and the tool is widely adopted. Install it if you work with neural networks or machine learning models in any of the supported formats.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Installation is straightforward with no runtime dependencies.
- The package is actively maintained with recent releases and a large repository following (33351 stars), indicating stable, well-supported tooling.
License · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution with minimal restrictions, making this suitable for both personal and commercial projects.
last release 2026-08-14 (0 days) · last repo commit 2026-08-14 · 33,351 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 98,557 downloads/mo, #13,074 on PyPI
Alternatives
Verify before relying
pip install netron
netron path/to/model.onnx
Or programmatically:
import netron
netron.start('path/to/model.onnx')- Whether the Python API (netron.start) is fully documented and stable across versions
- Performance characteristics when opening very large model files
- Whether experimental format support (MLIR, JAX, GGUF, RKNN, ncnn, MNN, PaddlePaddle, scikit-learn) is production-ready
What it is and what it does
Netron is a standalone viewer for inspecting and visualizing neural network and machine learning model architectures. It supports a wide range of model formats spanning multiple frameworks—ONNX, TensorFlow, PyTorch, Keras, Core ML, OpenVINO, and others—making it useful for developers and researchers who need to understand model structure, layer composition, and data flow without running inference.
The package can be used as a command-line tool (run `netron [FILE]` after installation), accessed through a browser at netron.app, or called programmatically via Python. It has no runtime dependencies, making installation and use lightweight. The project is actively maintained and widely used, as evidenced by its popularity and recent releases.
Use it for
- Inspect ONNX model architecture and layer details during model development or debugging
- Visualize PyTorch or TensorFlow model graphs to understand data flow and layer connections
- Examine pre-trained models from model zoos to understand their structure before fine-tuning
- Compare model architectures across different frameworks or versions
- Explore Keras or TensorFlow Lite models for mobile or edge deployment planning
- Validate Core ML or OpenVINO models before deployment on target platforms
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Netron is a lightweight, actively maintained, dependency-free viewer for a broad range of ML model formats. It solves a genuine need for developers and researchers who need to inspect model structure without writing code. The MIT license is permissive, there are no known vulnerabilities, and the tool is widely adopted. Install it if you work with neural networks or machine learning models in any of the supported formats.
Install
netron on PyPI
Before you install
Installation is straightforward with no runtime dependencies. The package is actively maintained with recent releases and a large repository following (33351 stars), indicating stable, well-supported tooling.
License in practice
MIT license permits unrestricted use, modification, and distribution with minimal restrictions, making this suitable for both personal and commercial projects.
Quickstart
pip install netron
netron path/to/model.onnx
Or programmatically:
import netron
netron.start('path/to/model.onnx')Verify before relying
- Whether the Python API (netron.start) is fully documented and stable across versions
- Performance characteristics when opening very large model files
- Whether experimental format support (MLIR, JAX, GGUF, RKNN, ncnn, MNN, PaddlePaddle, scikit-learn) is production-ready
Package facts
| License | MIT permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 98,557 / month, #13,074 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchProgramming Language :: Python :: 3Programming Language :: Python :: 3.6Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: MathematicsTopic :: Scientific/Engineering :: VisualizationTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: netron-9.2.2-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “neural network model viewer”
- netronNetron is a viewer for neural network and machine learning models…
- tosa-adapter-model-explorerEnables visualization of TOSA neural network model files within the…
- visualdlVisualDL is a visualization tool for deep learning training that logs…
Give your agent the search over MCP, or paste the wish link into any chat.
More Software Development packages
Provides backported and experimental type hints for Python 3.9+, allowing use of newer typing features on older Python versions and enabling early experimentation with type system PEPs before they enter the standard library.
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
FastAPI is a Python web framework for building REST APIs using type hints, with automatic request validation, serialization, and interactive API documentation.
Provides a way to document function parameters, class attributes, return types, and variables inline using Python's `Annotated` type hint syntax instead of traditional docstrings.
Typer builds command-line applications from Python functions using type hints, automatically generating help text, argument parsing, and shell completion.
Install it if you are building CLIs in Python.
Distlib provides low-level packaging utilities for building, distributing, and managing Python software—including metadata handling, version specifiers, wheel support, script installation, and dependency resolution.
See also onnx2tf · ai-edge-model-explorer · keras-nightly · tosa-adapter-model-explorer · keras · onnxmltools · mnn · openvino-dev · model-archiver · fiddle