#5 · Primary category： MLOps & Evaluation
netron
Visualizer for neural network, deep learning and machine learning models
Project last updated：08/29/26
GitHub Stars
33.4K
Forks
3.2K
Contributors
1
License
MIT
Why we included this project
Most model files are opaque until you open them, and Netron is built to make that quick. It parses the computational graph directly in JavaScript, so a 200MB ONNX export, a TorchScript trace, or a Core ML bundle opens and shows input shapes, output node names, layer attributes, and parameter values without installing PyTorch, TensorFlow, or any other framework. That is a relief when you inherit an undocumented model, debug a mismatched tensor shape, or just want to confirm an exported graph matches what you trained. Format support is broad, covering ONNX, TensorFlow Lite, PyTorch, Keras, Caffe, OpenVINO, Safetensors, and more, with experimental support for newer formats like GGUF and JAX. It runs as a desktop app, in the browser at netron.app, or through a small Python wrapper, so it works just as well inside a script or notebook as it does as a standalone viewer.
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