{"id":20685470,"url":"https://github.com/yas-sim/openvino-workshop-en","last_synced_at":"2026-05-18T03:33:58.575Z","repository":{"id":185235198,"uuid":"255206108","full_name":"yas-sim/openvino-workshop-en","owner":"yas-sim","description":"Hands-on workshop contents to learn Intel distribution of OpenVINO toolkit - a deep learning inferencing library","archived":false,"fork":false,"pushed_at":"2021-07-04T13:14:42.000Z","size":531,"stargazers_count":2,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-09-02T08:44:27.252Z","etag":null,"topics":["classification","deep-learning","hands-on","inference","inference-engine","intel","intel-distribution","openvino-toolkit","openvno","tutorial","workshop","workshop-contents"],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/yas-sim.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2020-04-13T01:44:44.000Z","updated_at":"2021-08-18T11:36:31.000Z","dependencies_parsed_at":"2023-08-01T07:09:27.900Z","dependency_job_id":null,"html_url":"https://github.com/yas-sim/openvino-workshop-en","commit_stats":null,"previous_names":["yas-sim/openvino-workshop-en"],"tags_count":3,"template":false,"template_full_name":null,"purl":"pkg:github/yas-sim/openvino-workshop-en","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yas-sim%2Fopenvino-workshop-en","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yas-sim%2Fopenvino-workshop-en/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yas-sim%2Fopenvino-workshop-en/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yas-sim%2Fopenvino-workshop-en/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/yas-sim","download_url":"https://codeload.github.com/yas-sim/openvino-workshop-en/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yas-sim%2Fopenvino-workshop-en/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33163754,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-17T22:39:12.733Z","status":"online","status_checked_at":"2026-05-18T02:00:06.436Z","response_time":71,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["classification","deep-learning","hands-on","inference","inference-engine","intel","intel-distribution","openvino-toolkit","openvno","tutorial","workshop","workshop-contents"],"created_at":"2024-11-16T22:27:28.833Z","updated_at":"2026-05-18T03:33:58.559Z","avatar_url":"https://github.com/yas-sim.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# openvino-workshop-en\n\n## Overview\nHands-on workshop contents to learn Intel distribution of OpenVINO toolkit - a deep learning inferencing library.\nThe workshop contents are tested on Ubuntu and Windows 10 systems.\n\n## Description\n[Intel distribution of OpenVINO toolkit](https://software.intel.com/en-us/openvino-toolkit) is a library suite for computer vision applications. OpenVINO consists of following libraries and tools.  \n- Inference Engine - Efficient, high-performance and flexible deep learning inference run-time engine library\n- Model Optimizer - Convert generic deep-learning models into OpenVINO IR format\n- Model Downloader - Download OMZ (Open Model Zoo, Intel) models and popular deep learning models\n- Deep Learning Workbench - Post training model re-quantization, benchmarking, accuracy checking\n- OpenCV - High performance and feature-rich image processing library\n\nOpenVINO provides great scalability. It supports wide variety of deep learning processors and accelerators. You can use almost the same code on different hardware easily.\n- CPU - Atom to Xeon, OpenVINO supports the latest DL boot instructions\n- Integrated GPU - OpenVINO can leverage the performance of integrated GPU and off load the task from CPU\n- VPU - Vision Processing Unit (Myriad). A low power yet powerful deep-learning accelerator from Intel\n- FPGA - OpenVINO compatible FPGA acclerator cards are available\n- HDDL - High Density Deep Learning accelerator. Single or multiple Myriad devices are on a board   \n\nAlso, OpenVINO supports various operating systems.\n- Windows 10, Ubuntu, CentOS, MacOS\n\nYou will learn the basics of OpenVINO through this workshop.\n1.  Learning basic of OpenVINO API through a simple image classification program - [classification.ipynb](./classification.ipynb)\n2.  Basic of object detection program using OpenVINO - [object-detection-ssd.ipynb](./object-detection-ssd.ipynb)\n3.  Basic of asynchronous inferencing - [classification-async-single.ipynb](./classification-async-single.ipynb)\n4.  Technique for high performance inference program - asynchronous and simultaneous inferencing - [classification-async-multi.ipynb](./classification-async-multi.ipynb)\n4. \u003c Appendix \u003e Automate evaluation work on DevCloud - [automated-testing.ipynb](./automated-testing.ipynb)\n\n## How to use\n1. Go to Intel distribution of OpenVINO toolkit [web page](https://software.intel.com/en-us/openvino-toolkit) and download an OpenVINO package suitable for your operating system\n2. Install OpenVINO and setup support tools and accelerators by following the instruction in ['Get Started'](https://software.intel.com/en-us/openvino-toolkit/documentation/get-started) page\n3. Clone repository to your system\n~~~shell\n$ git clone https://github.com/yas-sim/openvino-workshop-en\n~~~\n4. Open a command terminal\n5. Set up environment variables for OpenVINO\n~~~\nLinux $ source /opt/intel/openvino/bin/setupvars.sh  \n~~~\n~~~\nWindows \u003e call \"Program Files (x86)\\IntelSWTools\\OpenVINO\\bin\\setupvars.bat\"\n~~~\n\n6. Start Jupyter notebook\n7. Open `openvino-workshop-en/START-HERE.ipynb` from Jupyter to start the workshop\n\n## Requirement\nThis workshop requires [Intel distribution of OpenVINO toolkit](https://software.intel.com/en-us/openvino-toolkit\n).　Tested with OpenVINO 2020.1 version.\n\n## Contribution\n\n## Licence\n\n[Apache2](http://www.apache.org/licenses/LICENSE-2.0.txt)\n\n## Author\n\n[Yasunori Shimura](https://github.com/yassim-intel)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyas-sim%2Fopenvino-workshop-en","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fyas-sim%2Fopenvino-workshop-en","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyas-sim%2Fopenvino-workshop-en/lists"}