Netron

Netron is an open-source viewer for neural network, deep learning, and machine learning models. It provides a graphical interface to visualize the architecture of these models, allowing users to inspect the structure and flow of data through various layers. The software enables the inspection of model weights, biases, and node attributes through a visual representation of the computation graph.

The software is available as a web application and as a standalone desktop application for macOS, Linux, and Windows. It can also be installed as a Python package via pip, which allows it to be launched from a command line or integrated into Python scripts using a simple function call. This multi-platform availability ensures that developers can analyze models regardless of their operating system or preferred development environment.

Key features

• Support for ONNX, TensorFlow Lite, and PyTorch model formats
• Compatibility with Core ML, OpenVINO, and Keras
• Visualization of Caffe, Darknet, Safetensors, and NumPy files
• Support for TorchScript, torch.export, and ExecuTorch
• Experimental support for MLIR, JAX, and GGUF
• Experimental support for RKNN, ncnn, MNN, and PaddlePaddle
• Experimental support for scikit-learn models

The application serves as a diagnostic and educational tool for machine learning engineers and researchers. By loading a model file, the software generates a visual graph that represents the network topology. This process allows for the verification of model architectures and the inspection of specific layer configurations across a wide variety of framework-specific formats. It handles a broad spectrum of formats, from standard industry exports like ONNX to specialized formats used in mobile and edge computing such as TensorFlow Lite and Core ML.

Netron functions as a specialized visualization utility for the machine learning ecosystem, focusing on the transparency and inspection of neural network structures.