Educational Resources about DL Workbench¶

Publications¶

EN Demidovskij A., Tugaryov A., Fatekhov M., Aidova E., Stepyreva E., Shevtsov M., Gorbachev Y. Accelerating Object Detection Models Inference within Deep Learning Workbench, 2021 International Conference on Engineering and Emerging Technologies (ICEET), 2021. Link. doi.
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.

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.

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.

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.


DL Workbench \| OpenVINO™ toolkit \| Ep. 42 \| Intel Software	DL Workbench - The Full Flow \| OpenVINO™ toolkit \| Ep. 43 \| Intel Software