• Create models for AI tasks, including classification, object detection, semantic segmentation, or anomaly detection.
  • Annotate data with as little as 20 to 30 images, and then let active learning help you teach the model as it learns.
  • Train your model into a multistep, smart application by chaining two or more tasks without the need to write additional code.
  • Expedite data annotation and easily segment images with professional drawing features like a pencil, polygon tool, and OpenCV GrabCut.
  • Output deep learning models in TensorFlow or PyTorch formats (where available) or as an optimized model for the OpenVINO toolkit to run on Intel® architecture CPUs, GPUs, and VPUs.
     
 
  • More than 200 pretrained models from Open Model Zoo for the OpenVINO toolkit for a wide variety of use cases
  • Use optimizations directly from the Hugging Face repository for an expansive range of generative AI (GenAI) models and large language models (LLM)
  • Option to import custom models from PyTorch*, TensorFlow*, and ONNX* (Open Neural Network Exchange)
  • Built-in OpenVINO toolkit AI inference runtime optimizations and benchmarking
  • Performance data for different topologies and layers
     
 
  • Standardized development interfaces: JupyterLab and Microsoft Visual Studio* code IDEs for elevated coding experience.
  • Ready-to-use reference implementations: Preconfigured, use-case-specific applications with the complete stack of reusable software.
  • OpenVINO toolkit samples and notebooks: Computer vision, generative AI, and LLM use cases.
  • Diverse component integration: Importing source code and native applications, Docker* containers, and Helm* charts directly through any popular repositories.

 

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