# lutzroeder/netron **Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/lutzroeder-netron).** 33,087 stars · 3,132 forks · JavaScript · MIT ## Links - GitHub: https://github.com/lutzroeder/netron - Homepage: https://netron.app - awesome-repositories: https://awesome-repositories.com/repository/lutzroeder-netron.md ## Topics `ai` `coreml` `deep-learning` `deeplearning` `keras` `machine-learning` `machinelearning` `ml` `neural-network` `numpy` `onnx` `pytorch` `safetensors` `tensorflow` `tensorflow-lite` `visualizer` ## Description Netron is a visualizer for neural network and machine learning models. It provides a graphical interface that renders model architectures as interactive node-link diagrams, allowing users to inspect internal layers, tensors, and metadata. By performing static analysis, the tool enables the examination of model definitions without executing the underlying machine learning code. The software distinguishes itself through a schema-driven parsing engine that translates diverse proprietary model formats into a unified internal graph structure. This approach ensures interoperability, allowing users to view and compare different file formats within a single interface. All processing occurs locally within the browser or a standalone desktop application, ensuring that sensitive model data remains private and offline without requiring server-side execution. The tool supports deep learning model debugging and documentation by providing clear visual representations of complex network topologies. It is available as a cross-platform desktop application, providing consistent access to model inspection capabilities across multiple operating systems. ## Tags ### Artificial Intelligence & ML - [Model Visualization Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/model-visualization-tools.md) — Analyzing sensitive machine learning model files locally within a browser or desktop application without uploading data to external servers. - [Neural Network Visualizers](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-visualizers.md) — Renders complex machine learning model architectures as interactive node-link diagrams for structural inspection. - [Model Inspection Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/model-inspection-tools.md) — Enables diagnostic viewing of internal graph structures and metadata of pre-trained models without executing code. - [Neural Network Explorers](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-explorers.md) — Exploring the internal layers and graph structure of complex machine learning models through an interactive and intuitive visual interface. - [Model Format Parsers](https://awesome-repositories.com/f/artificial-intelligence-ml/model-format-parsers.md) — Translates diverse proprietary model formats into a unified internal graph structure using declarative metadata definitions for consistent structural analysis. - [Model Parsers](https://awesome-repositories.com/f/artificial-intelligence-ml/model-parsers.md) — Parses binary and text-based model files into a unified internal graph structure without executing the underlying machine learning code. - [Model Debugging Utilities](https://awesome-repositories.com/f/artificial-intelligence-ml/model-debugging-utilities.md) — Identifies structural errors and unexpected layer configurations in complex models. - [Model Format Converters](https://awesome-repositories.com/f/artificial-intelligence-ml/model-format-converters.md) — Translates diverse machine learning file formats into a unified representation for consistent structural analysis. - [Model Interoperability Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/model-interoperability-tools.md) — Provides a unified interface for viewing and comparing diverse machine learning file formats. ### Part of an Awesome List - [AI and Machine Learning](https://awesome-repositories.com/f/awesome-lists/ai/ai-and-machine-learning.md) — Visualizer for deep learning and machine learning models. - [Machine Learning](https://awesome-repositories.com/f/awesome-lists/ai/machine-learning.md) — Visualization tool for neural networks and ML models. - [Machine Learning Frameworks](https://awesome-repositories.com/f/awesome-lists/ai/machine-learning-frameworks.md) — Visualizer tool for inspecting machine learning model architectures. - [Model Interpretation](https://awesome-repositories.com/f/awesome-lists/ai/model-interpretation.md) — Visualizer for deep learning model architectures. - [Model Visualization](https://awesome-repositories.com/f/awesome-lists/ai/model-visualization.md) — Visualizes neural network architectures and model structures. - [Observability And Monitoring](https://awesome-repositories.com/f/awesome-lists/ai/observability-and-monitoring.md) — Visualizes neural network and machine learning model architectures. - [Perception and Machine Learning](https://awesome-repositories.com/f/awesome-lists/ai/perception-and-machine-learning.md) — Visualizer for neural network model architectures. - [Visualization and Analysis](https://awesome-repositories.com/f/awesome-lists/ai/visualization-and-analysis.md) — Universal viewer for neural network and deep learning models. - [Analytics Tools](https://awesome-repositories.com/f/awesome-lists/data/analytics-tools.md) — Listed in the “Analytics Tools” section of the Awesome Selfhosted awesome list. - [Data Visualization](https://awesome-repositories.com/f/awesome-lists/data/data-visualization.md) — Visualizer for neural network models. - [Visualization Tools](https://awesome-repositories.com/f/awesome-lists/data/visualization-tools.md) — Visualizer for neural network models. - [Data Science Tooling](https://awesome-repositories.com/f/awesome-lists/devtools/data-science-tooling.md) — Visualizer for neural network models. - [Developer Tools](https://awesome-repositories.com/f/awesome-lists/devtools/developer-tools.md) — Visualizer for viewing neural network architecture graphs. - [Developer Utilities](https://awesome-repositories.com/f/awesome-lists/devtools/developer-utilities.md) — Visualizer for deep learning and machine learning models. - [Model Visualization](https://awesome-repositories.com/f/awesome-lists/devtools/model-visualization.md) — Provides a graphical interface to view and analyze model structures. - [Explainability](https://awesome-repositories.com/f/awesome-lists/more/explainability.md) — Listed in the “Explainability” section of the The Incredible Pytorch awesome list. - [Miscellaneous Tools](https://awesome-repositories.com/f/awesome-lists/more/miscellaneous-tools.md) — Deep learning model visualizer. ### Testing & Quality Assurance - [Static Analysis](https://awesome-repositories.com/f/testing-quality-assurance/code-quality-review/static-analysis.md) — Inspects the topology and parameters of neural networks by parsing definitions without execution. ### Development Tools & Productivity - [Desktop Development Utilities](https://awesome-repositories.com/f/development-tools-productivity/desktop-development-utilities.md) — A standalone software package that runs locally on multiple operating systems to provide secure and private model file analysis. ### Graphics & Multimedia - [Graph Visualization Libraries](https://awesome-repositories.com/f/graphics-multimedia/visualization-mapping/graph-visualization-libraries.md) — Maps hierarchical model data into an interactive node-link diagram for exploration. ### Security & Cryptography - [Privacy-Preserving Utilities](https://awesome-repositories.com/f/security-cryptography/privacy-preserving-utilities.md) — Processes model files directly within the local application memory to ensure data privacy and offline functionality without server-side execution.