{"id":21802503,"url":"https://github.com/qengineering/tensorflow_lite_ssd_rpi_32-bits","last_synced_at":"2025-06-30T10:33:32.354Z","repository":{"id":112948364,"uuid":"245803400","full_name":"Qengineering/TensorFlow_Lite_SSD_RPi_32-bits","owner":"Qengineering","description":"TensorFlow Lite SSD on a bare Raspberry Pi 4 at 17 FPS","archived":false,"fork":false,"pushed_at":"2021-12-27T10:01:26.000Z","size":22082,"stargazers_count":16,"open_issues_count":1,"forks_count":5,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-03-27T09:23:13.314Z","etag":null,"topics":["armv7","armv8","bare-raspberry-pi","cpp","deep-learning","frame-rate","high-fps","jamesbond","lite","raspberry-pi-4","ssd-mobilenet","tensorflow-examples","tensorflow-lite","testtensorflow-lite"],"latest_commit_sha":null,"homepage":"https://qengineering.eu/install-tensorflow-2-lite-on-raspberry-pi-4.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":"2020-03-08T11:39:50.000Z","updated_at":"2024-10-01T17:07:44.000Z","dependencies_parsed_at":null,"dependency_job_id":"bfc40acb-ac09-4032-aae5-6f0ced540864","html_url":"https://github.com/Qengineering/TensorFlow_Lite_SSD_RPi_32-bits","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%2FTensorFlow_Lite_SSD_RPi_32-bits","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2FTensorFlow_Lite_SSD_RPi_32-bits/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2FTensorFlow_Lite_SSD_RPi_32-bits/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2FTensorFlow_Lite_SSD_RPi_32-bits/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Qengineering","download_url":"https://codeload.github.com/Qengineering/TensorFlow_Lite_SSD_RPi_32-bits/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248764537,"owners_count":21158106,"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":["armv7","armv8","bare-raspberry-pi","cpp","deep-learning","frame-rate","high-fps","jamesbond","lite","raspberry-pi-4","ssd-mobilenet","tensorflow-examples","tensorflow-lite","testtensorflow-lite"],"created_at":"2024-11-27T11:29:07.464Z","updated_at":"2025-04-13T18:42:06.584Z","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":"# TensorFlow_Lite_SSD_RPi_32-bits\n![output image]( https://qengineering.eu/images/James_17.jpg )\u003cbr/\u003e\n## TensorFlow Lite SSD running at 17 FPS on bare Raspberry Pi 4\n\nA fast C++ implementation of TensorFlow Lite on a bare Raspberry Pi 4.\nOnce overclocked to 2000 MHz, the app runs an amazing 17 FPS!\nWithout any hardware accelerator, just you and your Pi.\n\nhttps://arxiv.org/abs/1611.10012 \u003cbr/\u003e\nTraining set: COCO \u003cbr/\u003e\nSize: 300x300 \u003cbr/\u003e\nFrame rate V1 Lite : 17 FPS (RPi 4 @ 2000 MHz - 32 bits OS) \u003cbr/\u003e\nFrame rate V1 Lite : 24 FPS (RPi 4 @ 1925 MHz - 64 bits OS) see https://github.com/Qengineering/TensorFlow_Lite_SSD_RPi_64-bits \u003cbr/\u003e\n\u003cbr/\u003e\nSpecial made for a bare Raspberry Pi see: https://qengineering.eu/install-tensorflow-2-lite-on-raspberry-pi-4.html \u003cbr/\u003e\n\u003cbr/\u003e\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/TensorFlow_Lite_SSD_RPi_32-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 \nJames.mp4 \u003cbr/\u003e\nCOCO_labels.txt \u003cbr/\u003e\ndetect.tflite \u003cbr/\u003e\nTestTensorFlow_Lite.cpb \u003cbr/\u003e\nMobileNetV1.cpp\u003cbr/\u003e\n \u003cbr/\u003e\nRun TestTensorFlow_Lite.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\u003cbr/\u003e\n\nSee the movie at: https://youtu.be/uspw6KztkeQ\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%2Ftensorflow_lite_ssd_rpi_32-bits","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fqengineering%2Ftensorflow_lite_ssd_rpi_32-bits","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqengineering%2Ftensorflow_lite_ssd_rpi_32-bits/lists"}