{"id":15020545,"url":"https://github.com/qengineering/rpi-image","last_synced_at":"2026-02-21T01:03:49.178Z","repository":{"id":54528540,"uuid":"347944655","full_name":"Qengineering/RPi-image","owner":"Qengineering","description":"Raspberry Pi 4 Buster 64-bit OS with deep learning examples","archived":false,"fork":false,"pushed_at":"2023-09-18T09:20:28.000Z","size":86,"stargazers_count":133,"open_issues_count":7,"forks_count":21,"subscribers_count":6,"default_branch":"main","last_synced_at":"2024-10-11T02:02:12.415Z","etag":null,"topics":["aarch64","armv8","computer-vision","cpp","deep-learning","face-recognition","mnn","ncnn","opencv","paddle-lite","pose-estimation","raspberry-pi-4","raspberry-pi-64-os","raspberry-pi-image","sd-card-image","ssd","tensorflow","tensorflow-lite"],"latest_commit_sha":null,"homepage":"https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html","language":null,"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":"2021-03-15T11:36:01.000Z","updated_at":"2024-08-02T12:05:34.000Z","dependencies_parsed_at":"2024-10-11T02:01:31.107Z","dependency_job_id":null,"html_url":"https://github.com/Qengineering/RPi-image","commit_stats":{"total_commits":37,"total_committers":1,"mean_commits":37.0,"dds":0.0,"last_synced_commit":"b64006b19e553eefff9df045d7eb3c6973adba5b"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2FRPi-image","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2FRPi-image/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2FRPi-image/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2FRPi-image/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Qengineering","download_url":"https://codeload.github.com/Qengineering/RPi-image/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":219864036,"owners_count":16555943,"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","armv8","computer-vision","cpp","deep-learning","face-recognition","mnn","ncnn","opencv","paddle-lite","pose-estimation","raspberry-pi-4","raspberry-pi-64-os","raspberry-pi-image","sd-card-image","ssd","tensorflow","tensorflow-lite"],"created_at":"2024-09-24T19:55:15.033Z","updated_at":"2026-02-21T01:03:49.143Z","avatar_url":"https://github.com/Qengineering.png","language":null,"readme":"# Raspberry Pi 4 Buster DNN image\n![output image]( https://qengineering.eu/images/Water7.webp )\u003cbr/\u003e\n## A Raspberry Pi 4 Buster 64-OS image with deep-learning examples\n[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)\u003cbr/\u003e\u003cbr/\u003e\n### June 12, 2023\n- Release of the [**Bullseye version**](https://github.com/Qengineering/RPi-Bullseye-DNN-image).\n\n### Update 7-26-2022. \n- New download site (Gdrive has a limited number of downloads per day).\u003cbr/\u003e\n\n### February 19, 2022\n- Use [PiShrink](https://github.com/Drewsif/PiShrink) to support of different SD sizes. Reduced the file from 4.83 to 2.68 GByte \u003cbr/\u003e\n\n### January 24, 2022\n- Updated and upgraded to the latest Debian 10 **Buster** release.\u003cbr/\u003e\n\nRegularly, we get the question if we have an image of our Raspberry Pi with some frameworks and [our deep-learning examples](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html). We are happy to comply with this request.\n\n------------\n\n## Installation.\n\n- Get a 16 GB SD card which will hold the image. \n- Download the image RPi_64OS_DNN.xz (2.68 GByte!) from [Sync](https://ln5.sync.com/dl/00118ac90/hvi2wsfy-i7dus6ch-ae4q94ya-x5k9ir3i).\n- Flash the image on the SD card with the [Imager](https://www.raspberrypi.org/software/) or [balenaEtcher](https://www.balena.io/etcher/).\n- Insert the SD card into your Raspberry Pi 4.\n- Wait a few minutes, while the image will expand to the full size of your SD card.\n- No WiFi installed. Password: ***3.14***\n- RPi_64OS_DNN.xz md5sum: c4c7b4e6571f690d4f6c156ca5df9444\n\n------------\n\n## Tips.\n\n* You can [overclock the Raspberry Pi](https://qengineering.eu/overclocking-the-raspberry-pi-4.html) if your SD-card is not too worn out. 1800 MHz is no problem. Most deep learning examples even work at 1950 MHz.\u003cbr/\u003e\n* If you are in need of extra space, you can delete the opencv and the opencv_contrib folder from the SD card. They are no longer needed since all libraries are placed in the /usr/local directory.\n\n------------\n\n## Contents.\n\nClicking on the links below will direct you to our GitHub repo.\u003cbr\u003e\n\n- [OpenCV](https://github.com/Qengineering/OpenCV-Livecam-Raspberry-Pi)\n- [Classification](https://github.com/Qengineering/TensorFlow_Lite_Classification_RPi_64-bits)\n- [SSD](https://github.com/Qengineering/TensorFlow_Lite_SSD_RPi_64-bits)\n- [Segmentation](https://github.com/Qengineering/TensorFlow_Lite_Segmentation_RPi_64-bit)\n- [Segmentation Yolact](https://github.com/Qengineering/Yolact-ncnn-Raspberry-Pi-4)\n- [Pose](https://github.com/Qengineering/TensorFlow_Lite_Pose_RPi_64-bits)\n- [Face detection](https://github.com/Qengineering/Face-detection-Raspberry-Pi-32-64-bits)\n- [Face mask detection Paddle](https://github.com/Qengineering/Face-Mask-Detection-Raspberry-Pi-64-bits)\n- [Face mask detection TensorFlow](https://github.com/Qengineering/TensorFlow_Lite_Face_Mask_RPi_64-bits)\n- [Face recognition](https://github.com/Qengineering/Face-Recognition-Raspberry-Pi-64-bits)\n\n------------\n\n## Pre-installed frameworks.\n\nClicking on the links below will direct you to our installation guide.\u003cbr\u003e\n\n- [OpenCV](https://qengineering.eu/deep-learning-with-opencv-on-raspberry-pi-4.html) 4.5.1\n- [ncnn](https://qengineering.eu/install-ncnn-on-raspberry-pi-4.html) 20210124\n- [MNN](https://qengineering.eu/install-mnn-on-raspberry-pi-4.html) 1.1.0\n- [Paddle-Lite](https://qengineering.eu/install-paddle-lite-on-raspberry-pi-4.html) 2.7\n- [TensorFlow-Lite](https://qengineering.eu/install-tensorflow-2-lite-on-raspberry-64-os.html) 2.4.1\n- [TensorFlow](https://qengineering.eu/install-tensorflow-2.4.0-on-raspberry-64-os.html) 2.4.1\n- [TensorFlow Addons](https://qengineering.eu/install-tensorflow-2.4.0-on-raspberry-64-os.html) 0.13.0-dev\n- [Pytorch](https://qengineering.eu/install-pytorch-on-raspberry-pi-4.html) 1.8.0\n- [TorchVision](https://qengineering.eu/install-pytorch-on-raspberry-pi-4.html) 0.9.0\n\n![output image](https://qengineering.eu/images/SD_frameworks.png)\n\n------------\n\n## WiFi.\n\nSince everyone has a unique password for their WiFi connection, we have not activated the WiFi.\u003cbr/\u003e\nTo enable the wireless LAN to follow the next steps:\u003cbr/\u003e\n\n1) Left-click on the Ethernet symbol.\u003cbr/\u003e\u003cbr/\u003e\n![image](https://user-images.githubusercontent.com/44409029/124445112-8eb8e880-dd7f-11eb-80e6-121dc31fd0b8.png)\u003cbr/\u003e\u003cbr/\u003e\n2) Click \"Turn on wireless LAN\", and wait a few seconds. Your RPi will scan for available networks.\u003cbr/\u003e\u003cbr/\u003e\n![image](https://user-images.githubusercontent.com/44409029/124445876-39310b80-dd80-11eb-97ff-1ef8f8c477e8.png)\u003cbr/\u003e\u003cbr/\u003e\n3) Left-click again on the Ethernet symbol and choose your network.\u003cbr/\u003e\u003cbr/\u003e\n![image](https://user-images.githubusercontent.com/44409029/124446101-64b3f600-dd80-11eb-9385-eee4fd730268.png)\u003cbr/\u003e\u003cbr/\u003e\n4) Give your key, and wait a couple of seconds to let the RPi establish the connection.\u003cbr/\u003e\u003cbr/\u003e\n![image](https://user-images.githubusercontent.com/44409029/124447227-74800a00-dd81-11eb-9c47-bee6b2b84bc1.png)\u003cbr/\u003e\u003cbr/\u003e\n5) Success! \u003cbr/\u003e\u003cbr/\u003e\n![image](https://user-images.githubusercontent.com/44409029/124446775-063b4780-dd81-11eb-9fd8-2d597ad31cee.png)\n\n------------\n\n## OpenCV + TensorFlow.\n\nImporting both TensorFlow and OpenCV in Python can throw the error: _cannot allocate memory in static TLS block_.\u003cbr/\u003e\nThis behaviour only occurs on an aarch64 system and is caused by the OpenMP memory requirements not being met.\u003cbr/\u003e\nFor more information, see GitHub ticket [#14884](https://github.com/opencv/opencv/issues/14884).\u003cbr/\u003e\n\n![output image](https://qengineering.eu/images/SwapImportOpenCVRPi.png)\n\nThere are a few solutions. The easiest is to import OpenCV at the beginning, as shown above.\u003cbr/\u003e\nThe other is disabling OpenMP by setting the -DBUILD_OPENMP and -DWITH_OPENMP flags OFF.\u003cbr/\u003e\nWhere possible, OpenCV will now use the default pthread or the TBB engine for parallelization.\u003cbr/\u003e\nWe don't recommend it. Not all OpenCV algorithms automatically switch to pthread.\u003cbr/\u003e\nOur advice is to import OpenCV into Python first before anything else.\u003cbr/\u003e\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","funding_links":["https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick\u0026hosted_button_id=CPZTM5BB3FCYL"],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqengineering%2Frpi-image","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fqengineering%2Frpi-image","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqengineering%2Frpi-image/lists"}