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MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML. MMdnn is a comprehensive and cross-framework tool to convert, visualize and diagnose deep learning (DL) models. The "MM" stands for model management, and "dnn" is the acronym of deep neural network. We implement a universal converter to convert DL models between frameworks, which means you can train a model with one framework and deploy it with another. During the model conversion, we generate some code snippets to simplify later retraining or inference. We provide a model collection to help you find some popular models. We provide a model visualizer to display the network architecture more intuitively. We provide some guidelines to help you deploy DL models to another hardware platform.
Features
- We provide a model converter to help developers convert models between frameworks through an intermediate representation format
- MMdnn provides a docker image
- The model conversion between currently supported frameworks is tested on some ImageNet models
- One command to achieve the conversion
- We provide a guide to help you accelerate inference with TensorRT.
- We provide a local visualizer to display the network architecture of a deep learning model
Project Samples
