Netron is a viewer for neural network, deep learning and machine learning models.
Netron supports ONNX (.onnx, .pb, .pbtxt), Keras (.h5, .keras), Core ML (.mlmodel), Caffe (.caffemodel, .prototxt), Caffe2 (predict_net.pb), Darknet (.cfg), MXNet (.model, -symbol.json), Barracuda (.nn), ncnn (.param), Tengine (.tmfile), TNN (.tnnproto), UFF (.uff) and TensorFlow Lite (.tflite).
Netron has experimental support for TorchScript (.pt, .pth), PyTorch (.pt, .pth), Torch (.t7), Arm NN (.armnn), BigDL (.bigdl, .model), Chainer (.npz, .h5), CNTK (.model, .cntk), Deeplearning4j (.zip), MediaPipe (.pbtxt), ML.NET (.zip), MNN (.mnn), PaddlePaddle (.zip, __model__), OpenVINO (.xml), scikit-learn (.pkl), TensorFlow.js (model.json, .pb) and TensorFlow (.pb, .meta, .pbtxt, .ckpt, .index).
macOS: Download the .dmg file or run brew cask install netron
Linux: Download the .AppImage file or run snap install netron
Windows: Download the .exe installer or run winget install netron
Browser: Start the browser version.
Python Server: Run pip install netron and netron [FILE] or import netron; netron.start('[FILE]').
Sample model files to download or open using the browser version:
- ONNX: squeezenet [open]
- CoreML: exermote [open]
- Darknet: yolo [open]
- Keras: mobilenet [open]
- MXNet: inception_v3 [open]
- TensorFlow: chessbot [open]
- TensorFlow Lite: hair_segmentation [open]
- TorchScript: traced_online_pred_layer [open]
- Caffe: mobilenet_v2 [open]