netron and tensorspace
These tools are **competitors** in the realm of neural network visualization, with Netron providing a general-purpose visualizer for various model formats and Tensorspace offering a more specialized 3D interactive visualization framework for browser-based exploration, particularly focused on TensorFlow/Keras models.
About netron
lutzroeder/netron
Visualizer for neural network, deep learning and machine learning models
Supports 20+ model formats across major frameworks (ONNX, PyTorch, TensorFlow, Core ML, GGUF, Safetensors, and more), enabling unified inspection of architectures and layer details. Available as a web app, native desktop client (macOS/Linux/Windows), or Python CLI for seamless integration into development workflows. Parses and renders computational graphs with interactive visualization of operator properties, tensor shapes, and model structure.
About tensorspace
tensorspace-team/tensorspace
Neural network 3D visualization framework, build interactive and intuitive model in browsers, support pre-trained deep learning models from TensorFlow, Keras, TensorFlow.js
Constructs 3D neural network visualizations by building layer-by-layer models using a Keras-like API, then loads preprocessed weights from TensorFlow/Keras/TensorFlow.js models to render interactive tensor flows through the network. Built on Three.js for 3D rendering and TensorFlow.js for browser-based inference, it enables front-end developers to inspect intermediate layer activations and feature abstractions during model prediction without server-side computation.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work