🦉 Introduction

iam Basic System

The basic system is sketched below. In the out-of-the-box state the camera performs as an common GigE-Vision device and makes it easy to install the system and finding the right camera setup.

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iam Customization

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Possibilities

iam provide 64-bit processor scalability while combining real-time control with soft and hard engines for graphics and video. With the NET SDK and toolboxes sketched below customers can start from an comfortable starting point to build their unique vision system with iam. The open system architecture of iam enables customers to use both CPU and FPGA processing resources in their application.

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In the chapters Applications we provide different example projects to transform iam into your own smart vision system while chapter Third Party Libraries provides step by step guidelineing and example codes for the most widely-used comerial vision libaries such as https://net-iam.atlassian.net/wiki/spaces/iam/pages/51511463/Halcon, MIL () and /wiki/spaces/iam/pages/844627987.

In section https://net-iam.atlassian.net/wiki/pages/resumedraft.action?draftId=79986879we brefly discribed how to optimize your own appliction code by using hardware acceleration.

iam ML - Machine Learning Ready

iam is ideal as a platform for Machine Learning tasks. The integrated hardware acceleration efficiently supports open neural networks such as Caffe, TensorFlow and MXNet. This means that users get a smart vision system that contributes decentrally to the application solution. iam enables them to develop precisely tailored solutions for their vision-based processes from an toplevel perspective.

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Section iam ML - Machine Learning ready provides step by step guidelineing and example codes from training over deploying to performing convolutional neural network processing with iam.

📚 Content