ZEN Toolkit AI
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ZEN Toolkit AI

Item no.: 410136-0169-380 (individual configuration)

ZEN Toolkit AI Make use of powerful AI tools. With this package, you can train AI models in ZEN and ...

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ZEN Toolkit AI
Item Number: 410136-0169-380 (individual configuration)
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ZEN Toolkit AI Make use of powerful AI tools. With this package, you can train AI models in ZEN and ZEN core for various applications: - Solve your most challenging segmentation tasks within ZEN and ZEN core image analysis workflows. - Classify segmented objects with ZEN Toolkit 2D using powerful AI models - De-noise your images using deep learning methods based on Noise2Void Train your AI models in a tool optimized specifically for each workflow. The ease of use allows even non-experts to fully utilize the training tools and benefit from powerful AI methods. Thanks to a seamless integration in ZEN and ZEN core, you can use your models directly in the different toolkits (e.g. 2D Toolkit, Bio Apps Toolkit, Smart Acquisition Toolkit, Technical Cleanliness Analysis, Materials Apps Toolkit) and thus easily process multidimensional datasets such as Z-stacks, time series, multichannel and tiled images. The package supports all image data formats that can be opened in ZEN and ZEN core, meaning you can also process file formats from other vendors imported via the 3rd party import. The complete ZEN platform is "AI ready", i.e. you can run the once trained models in all supported toolkits in ZEN and ZEN core. As an alternative to an own training with the ZEN Toolkit AI, you can let annotation of your data sets and training of a Deep Neural Network be performed as part of a custom solution (SCS) and just use the specifically trained networks in the various toolkits. Requirements: - The use of AI semantic segmentation requires an installation of Zeiss Python. Recommended hardware configuration is 8GB GPU and 64GB RAM. Only the CPU and Nvidia-GPUs are supported. A one-year subscription for the arivis Cloud platform is included allowing to train AI models for ZEN and ZEN core using arivis AI services.