{"id":21802530,"url":"https://github.com/qengineering/lffd-ncnn-raspberry-pi-4","last_synced_at":"2026-05-10T00:33:18.135Z","repository":{"id":112946511,"uuid":"357915042","full_name":"Qengineering/LFFD-ncnn-Raspberry-Pi-4","owner":"Qengineering","description":"LFFD face detection with ncnn for Raspberry Pi 4","archived":false,"fork":false,"pushed_at":"2021-12-10T12:37:13.000Z","size":28190,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-01-26T03:45:47.473Z","etag":null,"topics":["aarch64","deep-learning","face-detection","lffd","ncnn","ncnn-model","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":"2021-04-14T13:27:53.000Z","updated_at":"2022-11-03T06:52:28.000Z","dependencies_parsed_at":"2023-03-16T17:45:42.081Z","dependency_job_id":null,"html_url":"https://github.com/Qengineering/LFFD-ncnn-Raspberry-Pi-4","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2FLFFD-ncnn-Raspberry-Pi-4","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2FLFFD-ncnn-Raspberry-Pi-4/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2FLFFD-ncnn-Raspberry-Pi-4/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2FLFFD-ncnn-Raspberry-Pi-4/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Qengineering","download_url":"https://codeload.github.com/Qengineering/LFFD-ncnn-Raspberry-Pi-4/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244752360,"owners_count":20504256,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["aarch64","deep-learning","face-detection","lffd","ncnn","ncnn-model","raspberry-pi-4","raspberry-pi-64-os"],"created_at":"2024-11-27T11:29:19.817Z","updated_at":"2026-05-10T00:33:18.102Z","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":"# LFFD face detection Raspberry Pi 4\n![output image]( https://qengineering.eu/images/result_26.jpg )\n## LFFD face detection with the ncnn framework. \u003cbr/\u003e\n[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)\u003cbr/\u003e\u003cbr/\u003e\nPaper: https://arxiv.org/pdf/1904.10633.pdf\u003cbr/\u003e\u003cbr/\u003e\nSpecial made for a bare Raspberry Pi 4 see [Q-engineering deep learning examples](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html)\n\n------------\n\n## Benchmark.\n| Model  | framework | model |size |  mAP | Jetson Nano\u003cbr/\u003e2015 MHz | RPi 4 64-OS\u003cbr/\u003e1950 MHz |\n| ------------- | :-----: | :-----:  | :-----:  | :-----:  | :-------------:  | :-------------: |\n| Ultra-Light-Fast| ncnn | slim-320 | 320x240 | 67.1  |    - FPS | 26 FPS |\n| Ultra-Light-Fast| ncnn | RFB-320 | 320x240 | 69.8  |    - FPS | 23 FPS |\n| Ultra-Light-Fast| MNN | slim-320 | 320x240 | 67.1  | 70 FPS | 65 FPS |\n| Ultra-Light-Fast| MNN | RFB-320 | 320x240 | 69.8  | 60 FPS | 56 FPS |\n| Ultra-Light-Fast| OpenCV | slim-320 | 320x240 | 67.1  | 48 FPS | 40 FPS |\n| Ultra-Light-Fast| OpenCV | RFB-320 | 320x240 | 69.8  | 43 FPS | 35 FPS |\n| Ultra-Light-Fast + Landmarks| ncnn | slim-320 | 320x240 | 67.1  | 50 FPS | 24 FPS |\n| LFFD| ncnn | 5 stage | 320x240 | 88.6 | 16.4 FPS | **4.85 FPS** |\n| LFFD| ncnn | 8 stage | 320x240 | 88.6 | 11.7 FPS | **3.45 FPS** |\n| LFFD| MNN | 5 stage | 320x240 | 88.6 | 2.6 FPS | 2.17 FPS |\n| LFFD| MNN | 8 stage | 320x240 | 88.6 | 1.8 FPS | 1.49 FPS |\n| CenterFace| ncnn | - | 320x240 | 93 | 16.5 FPS | 6.8 FPS |\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/LFFD-ncnn-Raspberry-Pi-4/archive/refs/heads/main.zip \u003cbr/\u003e\n$ unzip -j master.zip \u003cbr/\u003e\nRemove master.zip, LICENSE and README.md as they are no longer needed. \u003cbr/\u003e \n$ rm master.zip \u003cbr/\u003e\n$ rm LICENSE \u003cbr/\u003e\n$ rm README.md \u003cbr/\u003e \u003cbr/\u003e\nYour *MyDir* folder must now look like this: \u003cbr/\u003e \nimages folder\u003cbr/\u003e\nWalks2.mp4 \u003cbr/\u003e\nFaceDetection_LFFD_ncnn.cpb \u003cbr/\u003e\nmain.cpp \u003cbr/\u003e\nLFFD_ncnn.h \u003cbr/\u003e\nLFFD_ncnn.cpp \u003cbr/\u003e\ntrain_10_320_20L_5scales_v2_iter_1000000.bin \u003cbr/\u003e\ntrain_10_560_25L_8scales_v1_iter_1400000.bin \u003cbr/\u003e\nsymbol_10_320_20L_5scales_v2_deploy.param \u003cbr/\u003e\nsymbol_10_560_25L_8scales_v1_deploy.param \n\n------------\n\n## Running the app.\nTo run the application load the project file FaceDetection_LFFD_ncnn.cbp in Code::Blocks.\u003cbr/\u003e \nNext, follow the instructions at [Hands-On](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html#HandsOn).\u003cbr/\u003e\u003cbr/\u003e\n![output image]( https://qengineering.eu/images/selfie_result_8.jpg )\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","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqengineering%2Flffd-ncnn-raspberry-pi-4","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fqengineering%2Flffd-ncnn-raspberry-pi-4","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqengineering%2Flffd-ncnn-raspberry-pi-4/lists"}