A Python Framework for eIQ on i.MX Processors
A Python Demo Framework for eIQ on i.MX Processors
PyeIQ is written on top of eIQ™ ML Software Development Environment and provides a set of Python classes allowing the user to run Machine Learning applications in a simplified and efficiently way without spending time on cross-compilations, deployments or reading extensive guides.
- Take as a disclaimer that PyeIQ should not be considered production-ready.
- Go to the documentation page for further details pyeiq.dev.
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- General major changes on project structure.
- Split project into engine, modules, helpers, utils and apps.
- Add base class to use on all demos avoiding repeated code.
- Support for more demos and applications including Arm NN.
- Support for building using Docker.
- Support for download data from multiple servers.
- Support for searching devices and build pipelines.
- Support for appsink/appsrc for QM (not working on MPlus).
- Support for camera and H.264 video.
- Support for Full HD, HD and VGA resolutions.
- Support video and image for all demos.
- Add display info in the frame, such as: FPS, model and inference time.
- Add manager tool to launch demos and applications.
- Add document page for PyeIQ project.
- Support demos based on TensorFlow Lite (2.1.0) and image classification.
- Support inference running on GPU/NPU and CPU.
- Support file and camera as input data.
- Support SSD (Single Shot Detection).
- Support downloads on the fly (models, labels, dataset, etc).
- Support old eIQ demos from eiq_sample_apps CAF repository.
- Support model training for host PC.
- Support UI for switching inference between GPU/NPU/CPU on TensorFlow Lite.
Copyright and License
Copyright 2020 NXP Semiconductors. Free use of this software is granted under the terms of the BSD 3-Clause License. See LICENSE for details.
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