Educational Resources about DL Workbench

Publications

2021

  • EN The No-Code Approach to Deploying Deep Learning Models on Intel® Hardware

    • Part One: Intel® Tools for Deep Learning Inference Deployment. Learn the basics of OpenVINO™ Deep Learning Workbench and Intel® DevCloud for the Edge. Link.

    • Part Two: Import, Convert, and Benchmark a TensorFlow Model on Intel Hardware with OpenVINO Deep Learning Workbench. Link.

    • Part Three: Recalibrate Precision and Package Your TensorFlow Model for Deployment with OpenVINO™ Deep Learning Workbench. Link.

  • EN Gorbachev Y., Demidovskij A., Fedorov M. Exploring model performance on remote targets with OpenVINO™ Deep Learning Workbench. Link.

  • EN Heath C., Solving the Problem of Squirrels Stealing from the Bird feeder: Prototyping Image Classification with the Deep Learning Workbench in Intel® DevCloud for the Edge. 2021. Intel® AI Blog. Link.

  • EN Demidovskij A., Tugaryov A., Kashchikhin, A., Suvorov A., Tarkan Y., Fedorov M., and Gorbachev Y. OpenVINO Deep Learning Workbench: Towards Analytical Platform for Neural Networks Inference Optimization. 2021. In Journal of Physics: Conference Series. Vol. 1828. IOP Publishing. Link. doi.

2020

  • EN Demidovskij, A., Tugaryov, A., Suvorov A., Tarkan Y., Fatekhov M., Salnikov I., Kashchikhin A., Golubenko V., Dedyukhina G., Alborova A., Palmer R., Fedorov M., and Gorbachev Y. OpenVINO Deep Learning Workbench: A Platform for Model Optimization, Analysis and Deployment. 2020. 32nd International Conference on Tools with Artificial Intelligence (ICTAI). IEEE. doi.

  • EN Gorbachev Y., Demidovskij A., and Fedorov M. Streamline your Intel® Distribution of OpenVINO™ Toolkit development with Deep Learning Workbench. 2020. Intel® AI Blog. Link.

  • RU Demidovskij A., Tugaryov, A., Analysis of the accuracy and performance of neural networks in OpenVINO Deep Learning Workbench. In Proceedings of the I National Congress on Cognitive Research, Artificial Intelligence and Neuroinformatics. 2020.

2019

  • EN Demidovskij A., Gorbachev Y., Fedorov M., Slavutin I., Tugarev A., Fatekhov M., and Tarkan Y. OpenVINO Deep Learning Workbench: Comprehensive analysis and tuning of neural networks inference. 2019. In Proceedings of the IEEE International Conference on Computer Vision Workshop. IEEE. doi

  • RU Demidovskij A. Software and Hardware Optimization Peculiarities of Neural Networks Inference. 2019. In Proceedings of the XXI International Conference Neuroinformatics-2019. Link.

DL Workbench Community Articles

2021

  • EN Sovit Rath, Aditya Sharma, Introduction to OpenVINO Deep Learning Workbench. Link.

  • EN Dagli R., Eken S., Deploying a smart queuing system on edge with Intel OpenVINO toolkit. 2021. Soft Computing. Link. doi

  • RU Vasiliev E., Techniques for improving the performance of deep model inference with DL Workbench. Part 1. Introduction and Installation. Link. Part 2. Quantization and Throughput mode. Link.

  • СH 許哲豪（Jack), 不用寫程式也能玩轉深度學習模型 ─ OpenVINO™ DL Workbench圖形化介面工具簡介 (Experiment with Deep learning Models without Programming ─ Introduction to the OpenVINO™ DL Workbench GUI Tool) Link.

  • СH Louis Chuang, OpenVINO DL Workbench 輕鬆完成AI模型的分析與部署工作 (Use DL Workbench to easily complete the analysis of AI models) Link.

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