{"id":21802526,"url":"https://github.com/qengineering/ncnn_pose_rpi_64-bits","last_synced_at":"2026-02-26T20:06:19.066Z","repository":{"id":112947061,"uuid":"581526066","full_name":"Qengineering/ncnn_Pose_RPi_64-bits","owner":"Qengineering","description":"ncnn pose estimation on bare Raspberry Pi 4 with 64-bit OS at 7.1 FPS","archived":false,"fork":false,"pushed_at":"2022-12-23T14:08:02.000Z","size":5596,"stargazers_count":3,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-13T18:55:22.237Z","etag":null,"topics":["bare-raspberry-pi","cpp","high-fps","ncnn","ncnn-framework","ncnn-model","pose-estimation","raspberry-pi-4","raspberry-pi-64-os"],"latest_commit_sha":null,"homepage":"https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html","language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Qengineering.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,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-12-23T12:54:00.000Z","updated_at":"2022-12-26T10:28:52.000Z","dependencies_parsed_at":null,"dependency_job_id":"5f438bfa-c79e-4f61-be86-ac7fada16056","html_url":"https://github.com/Qengineering/ncnn_Pose_RPi_64-bits","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Qengineering/ncnn_Pose_RPi_64-bits","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2Fncnn_Pose_RPi_64-bits","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2Fncnn_Pose_RPi_64-bits/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2Fncnn_Pose_RPi_64-bits/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2Fncnn_Pose_RPi_64-bits/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Qengineering","download_url":"https://codeload.github.com/Qengineering/ncnn_Pose_RPi_64-bits/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2Fncnn_Pose_RPi_64-bits/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29870578,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-26T18:42:30.764Z","status":"ssl_error","status_checked_at":"2026-02-26T18:41:47.936Z","response_time":89,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":["bare-raspberry-pi","cpp","high-fps","ncnn","ncnn-framework","ncnn-model","pose-estimation","raspberry-pi-4","raspberry-pi-64-os"],"created_at":"2024-11-27T11:29:18.753Z","updated_at":"2026-02-26T20:06:19.032Z","avatar_url":"https://github.com/Qengineering.png","language":"C++","funding_links":["https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick\u0026hosted_button_id=CPZTM5BB3FCYL"],"categories":[],"sub_categories":[],"readme":"# Pose Raspberry Pi 4\n![output image]( https://qengineering.eu/github/Pose_NCNN.webp )\u003cbr/\u003e\n## Pose estimation with ncnn running at 7.0 FPS on bare Raspberry Pi 4.\n[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)\u003cbr/\u003e\u003cbr/\u003e\nA fast C++ implementation of person detection and pose estimation with the ncnn framework on a bare Raspberry Pi 4 64-bit OS.\u003cbr/\u003e\nOnce overclocked to 1825 MHz, the app runs at 7.1 FPS without any hardware accelerator. Thanks [dog-qiuqiu](https://github.com/dog-qiuqiu/Ultralight-SimplePose) for all the hard work.\u003cbr/\u003e\nSpecial made for a Raspberry Pi 4 see [Q-engineering deep learning examples](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html) \u003cbr/\u003e\n\n------------\n\nPapers: https://arxiv.org/abs/1804.06208\u003cbr/\u003e\n\n------------\n\n## Dependencies.\nTo run the application, you have to:\n- A raspberry Pi 4 with a 32 or 64-bit operating system. It can be the Raspberry 64-bit OS, or Ubuntu 18.04 / 20.04. [Install 64-bit OS](https://qengineering.eu/install-raspberry-64-os.html) \u003cbr/\u003e\n- The Tencent ncnn framework installed. [Install ncnn](https://qengineering.eu/install-ncnn-on-raspberry-pi-4.html) \u003cbr/\u003e\n- OpenCV 64 bit installed. [Install OpenCV 4.5](https://qengineering.eu/install-opencv-4.5-on-raspberry-64-os.html) \u003cbr/\u003e\n- Code::Blocks installed. (```$ sudo apt-get install codeblocks```)\n\n------------\n\n## Installing the app.\nTo extract and run the network in Code::Blocks \u003cbr/\u003e\n$ mkdir *MyDir* \u003cbr/\u003e\n$ cd *MyDir* \u003cbr/\u003e\n$ wget https://github.com/Qengineering/ncnn_Pose_RPi_64-bits/archive/refs/heads/master.zip \u003cbr/\u003e\n$ unzip -j master.zip \u003cbr/\u003e\nRemove master.zip and README.md as they are no longer needed. \u003cbr/\u003e \n$ rm master.zip \u003cbr/\u003e\n$ rm README.md \u003cbr/\u003e \u003cbr/\u003e\nYour *MyDir* folder must now look like this: \u003cbr/\u003e \nDance.mp4 \u003cbr/\u003e\nperson_detectord.bin \u003cbr/\u003e\nperson_detectord.param \u003cbr/\u003e\nUltralight-Nano-SimplePose.bin \u003cbr/\u003e\nUltralight-Nano-SimplePose.param \u003cbr/\u003e\nncnn_pose.cpb \u003cbr/\u003e\nncnn_pose.cpp\u003cbr/\u003e\n\n------------\n\n## Running the app.\nRun ncnn_pose.cpb with Code::Blocks. More info or\u003cbr/\u003e \nif you want to connect a camera to the app, follow the instructions at [Hands-On](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html#HandsOn).\u003cbr/\u003e\nI fact you can run this example on any aarch64 Linux system.\n\n------------\n\n[![paypal](https://qengineering.eu/images/TipJarSmall4.png)](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick\u0026hosted_button_id=CPZTM5BB3FCYL) \n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqengineering%2Fncnn_pose_rpi_64-bits","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fqengineering%2Fncnn_pose_rpi_64-bits","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqengineering%2Fncnn_pose_rpi_64-bits/lists"}