Tutorials

To get started with Aidge, please follow the Aidge 101 tutorial. This tutorial demonstrates the basic features of the Aidge Framework, importing an ONNX, transforming a neural network graph, performing inference and a cpp export.

For an advanced usage of the Aidge Framework, please refer to the following tutorials.

Aidge DNN fonctionnalities

• Manipulating databases and creating batches of data

• 🚧 Train a Deep Neural Network

• Provide an operator implementation using Python or atomic operators

• Perform advanced graph matching with the Graph Regular Expression tool

Optimize your DNN with Aidge

• 🚧 Optimize your neural network with Post Training Quantization

• Optimize the inference of your neural network with Tiling

Export your DNN with Aidge

• Add a custom implementation for a cpp export

• Export your DNN with TensorRT

• 🚧 Export your DNN for an STM32

Contributing to Aidge

You will find all information for contributing to Aidge in the wiki. For example, you can help us extend our operator coverage by adding an operator and its implementation in the Aidge library. The Add an operator and its implementation Tutorial details the steps to follow.

If you encounter any difficulty with the Tutorials, please create an issue here.