{"id":21802515,"url":"https://github.com/qengineering/rpi_64-bit_zero-2-image","last_synced_at":"2026-01-04T12:41:12.582Z","repository":{"id":112947941,"uuid":"431073339","full_name":"Qengineering/RPi_64-bit_Zero-2-image","owner":"Qengineering","description":"Raspberry Pi Zero 2 W 64-bit OS image with OpenCV, TensorFlow Lite and ncnn Framework.","archived":false,"fork":false,"pushed_at":"2022-02-17T10:56:01.000Z","size":12,"stargazers_count":39,"open_issues_count":0,"forks_count":7,"subscribers_count":5,"default_branch":"main","last_synced_at":"2025-01-26T03:45:46.783Z","etag":null,"topics":["arm7","armv7","ncnn","ncnn-framework","opencv4","rasberry-pi-zero-2","raspberry-pi-zero-2-w","sd-card-image","tensorflow-lite"],"latest_commit_sha":null,"homepage":"https://qengineering.eu/install-64-os-on-raspberry-pi-zero-2.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":"2021-11-23T11:25:35.000Z","updated_at":"2025-01-21T21:59:52.000Z","dependencies_parsed_at":null,"dependency_job_id":"48392890-dc15-4329-b4ff-d1f72d6b5fb7","html_url":"https://github.com/Qengineering/RPi_64-bit_Zero-2-image","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%2FRPi_64-bit_Zero-2-image","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2FRPi_64-bit_Zero-2-image/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2FRPi_64-bit_Zero-2-image/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2FRPi_64-bit_Zero-2-image/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Qengineering","download_url":"https://codeload.github.com/Qengineering/RPi_64-bit_Zero-2-image/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":["arm7","armv7","ncnn","ncnn-framework","opencv4","rasberry-pi-zero-2","raspberry-pi-zero-2-w","sd-card-image","tensorflow-lite"],"created_at":"2024-11-27T11:29:15.259Z","updated_at":"2026-01-04T12:41:12.551Z","avatar_url":"https://github.com/Qengineering.png","language":null,"funding_links":["https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick\u0026hosted_button_id=CPZTM5BB3FCYL"],"categories":[],"sub_categories":[],"readme":"# Raspbeery Pi Zero 2 64-bit OS image\n![output image]( https://qengineering.eu/images/SDcard16GBZero2.webp )\u003cbr/\u003e\n## A Raspberry Pi Zero 2 64-bit OS Bullseye with OpenCV, TensorFlow Lite and ncnn\n[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)\u003cbr/\u003e\n## Installation.\n\n- Get a SD-card which will hold the image (min 16 GB). \n- Download the image RPi_64OS_Zero_2.xz (1.7 GByte!) from our [Gdrive](https://drive.google.com/file/d/1VPpalGylbriNlVggF35PeVApsVFXwlHv/view?usp=sharing) site.\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 in your Raspberry Pi Zero 2.\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\n------------\n\n## Remarks.\n\n* You can [overclock the Raspberry Pi Zero 2](https://qengineering.eu/install-64-os-on-raspberry-pi-zero-2.html) if your SD-card is not too worn out. 1200 MHz is no problem. Most deep learning examples even work at 1300 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. There are no longer needed since all libraries are placed in the /usr/local directory.\n* Please note, the Raspberry Pi Zero 2 is a very new device. Lost of (software) updates can be expected in the coming months.\n* We commit several deep learning benchmarks. Read all about them at our [site](https://qengineering.eu/install-64-os-on-raspberry-pi-zero-2.html)\u003cbr/\u003e\n* As explained on the website, you have only 100 MB of free RAM for your applications left once the 64-bit Bullseye is loaded. To enlarge the amount of RAM to more practical sizes, we use a large swapping space. It will wear your SD card when used intensively. It also slows down the performance, as you can see in the graph below. The 64-bit Ubuntu server was faster, but it doesn't have a graphical desktop. Only a terminal input. And for those unfamiliar with Ubuntu, the desktop is only possible with 4 GB of RAM onboard.\u003cbr/\u003e\n\n![output image](https://qengineering.eu/images/gcc_timing4.png)\u003cbr/\u003e\n\n------------\n\n## Pre-installed frameworks.\n\n- [OpenCV](https://qengineering.eu/deep-learning-with-opencv-on-raspberry-pi-4.html) 4.5.4\n- [ncnn](https://qengineering.eu/install-ncnn-on-raspberry-pi-4.html) 20210124\n- [TensorFlow-Lite](https://qengineering.eu/install-tensorflow-2-lite-on-raspberry-64-os.html) 2.6.0\n\n------------\n\n## WiFi.\n\nSince everyone has a unique password on their WiFi connection, we have not activated the WiFi.\u003cbr/\u003e\nTo enable the wireless LAN 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[![paypal](https://qengineering.eu/images/TipJarSmall4.png)](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick\u0026hosted_button_id=CPZTM5BB3FCYL) \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqengineering%2Frpi_64-bit_zero-2-image","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fqengineering%2Frpi_64-bit_zero-2-image","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqengineering%2Frpi_64-bit_zero-2-image/lists"}