Lutz Roeder
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Update tf-metadata.json
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README.md
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, predict_net.pbtxt), Darknet (.cfg), MXNet (.model, -symbol.json), ncnn (.param) 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), OpenVINO (.xml), PaddlePaddle (.zip, __model__), scikit-learn (.pkl), Tengine (.tmfile), TensorFlow.js (model.json, .pb) and TensorFlow (.pb, .meta, .pbtxt, .ckpt, .index).
Install
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.
Browser: Start the browser version.
Python Server: Run pip install netron and netron [FILE] or import netron; netron.start('[FILE]').
Models
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]