OpenVINO™ Deep Learning Workbench User Guide¶
The purpose of this User Guide is to give you instructions to every step of DL Workbench workflow.
For information on how to start working with DL Workbench, refer to the Get Started documentation.
Import original and Open Model Zoo models and convert them to Intermediate Representation format.
Create and import annotated datasets of different supported formats, upload not annotated dataset, and enlarge datasets using augmentation.
Select a target and device and work with Remote Targets.
Accelerate model performance with INT8 Calibration or Winograd Algorithmic Tuning.
Run single or group inference, try different batch and stream combinations to accelerate the performance, visualize model architecture and compare model projects.
Measure accuracy of the model and the predictions with dataset annotations or between optimized and parent models.
Build your application with Deployment Package and learn how to use batches and streams in your application.
Download an archive with artifacts of your project.
Quick start with OpenVINO™ toolkit and learn how to use its API and command-line interface (CLI) in the preconfigured environment.
Restore and preserve DL Workbench state and explore options of working with Docker* container.