Go wrapper for the deep learning inference engine

Go wrapper for the deep learning inference engine

With this Go package, we will be able to utilize models optimized with Intel's Model Optimizer and accessible via the DL inference engine.

Artificial Intelligence

Description

This project will result in a deep learning inference engine for Go powered by Intel's C++ inference engine (part of the deep learning SDK). Initially, the inference will be focused on image classification, although the functionality will hopefully be expanded over time.

The project will be maintained here: https://github.com/gopherds/dlinfer

As part of this project, I have also created a Docker image for the deployment tools that provides a quick way to get everything up and running: https://github.com/dwhitena/intel_dl_deployment

Links

On GitHub.

Deployment tools docker image.

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Daniel W. created project Go wrapper for the deep learning inference engine

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Go wrapper for the deep learning inference engine

This project will result in a deep learning inference engine for Go powered by Intel's C++ inference engine (part of the deep learning SDK). Initially, the inference will be focused on image classification, although the functionality will hopefully be expanded over time.

The project will be maintained here: https://github.com/gopherds/dlinfer

As part of this project, I have also created a Docker image for the deployment tools that provides a quick way to get everything up and running: https://github.com/dwhitena/intel_dl_deployment

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I was involved in various projects. Among others, these were dealing with Grid Computing, People Tracking, Object Recognition, Face Recognition, as well as with several theses in the field of Computer Vision. Subsequently, I was following my passion for "Creative Coding" and "Real-time Interaction", and founded CtrlAltLab. This is an interactive real-time application project geared to augmented reality and virtual reality. It was realized at the Science Museum of Naples, where the project was selected as the most innovative idea to "Italian Kiics Awards for Art & Science" in 2014.

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