{"id":21802504,"url":"https://github.com/qengineering/tensorflow_lite_segmentation_rpi_32-bit","last_synced_at":"2025-04-13T18:42:06.568Z","repository":{"id":112948319,"uuid":"247318177","full_name":"Qengineering/TensorFlow_Lite_Segmentation_RPi_32-bit","owner":"Qengineering","description":"TensorFlow Lite segmentation on Raspberry Pi 4 aka Unet at 4.2 FPS","archived":false,"fork":false,"pushed_at":"2023-01-25T09:45:17.000Z","size":12076,"stargazers_count":8,"open_issues_count":0,"forks_count":3,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-27T09:23:14.564Z","etag":null,"topics":["armv7","armv8","cpp","deep-learning","raspberry-pi-4","segmentation","semantic-segmentation","tensorflow-examples","tensorflow-lite","unet","unet-image-segmentation","unet-segmentation","unet-tensorflow"],"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":"2020-03-14T17:03:51.000Z","updated_at":"2024-10-17T20:49:12.000Z","dependencies_parsed_at":null,"dependency_job_id":"7b9ddc75-8335-43ef-a5cc-57edaac93c64","html_url":"https://github.com/Qengineering/TensorFlow_Lite_Segmentation_RPi_32-bit","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_Segmentation_RPi_32-bit","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2FTensorFlow_Lite_Segmentation_RPi_32-bit/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2FTensorFlow_Lite_Segmentation_RPi_32-bit/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2FTensorFlow_Lite_Segmentation_RPi_32-bit/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Qengineering","download_url":"https://codeload.github.com/Qengineering/TensorFlow_Lite_Segmentation_RPi_32-bit/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248764530,"owners_count":21158104,"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","cpp","deep-learning","raspberry-pi-4","segmentation","semantic-segmentation","tensorflow-examples","tensorflow-lite","unet","unet-image-segmentation","unet-segmentation","unet-tensorflow"],"created_at":"2024-11-27T11:29:07.550Z","updated_at":"2025-04-13T18:42:06.556Z","avatar_url":"https://github.com/Qengineering.png","language":"C++","readme":"# TensorFlow_Lite_Segmentation_RPi_32\n![output image]( https://qengineering.eu/images/Unet_32.jpg )\n## TensorFlow Lite Segmentation on a bare Raspberry Pi 32-bit OS. \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\nA fast C++ implementation of TensorFlow Lite Unet on a bare Raspberry Pi 4.\u003cbr/\u003e\nOnce overclocked to 1900 MHz, the app runs at 4.0 FPS!\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\nPapers: https://arxiv.org/abs/1606.00915 \u003cbr/\u003e\nTraining set: VOC2017 \u003cbr/\u003e\nSize: 257x257 \u003cbr/\u003e\n\n------------\n\n## Benchmark.\nFrame rate Unet Lite : 4.0 FPS (RPi 4 @ 1900 MHz - 32 bits OS) \u003cbr/\u003e\nFrame rate Unet Lite : 7.2 FPS (RPi 4 @ 1850 MHz - 64 bits OS) \u003cbr/\u003e\n\n------------\n\n## Dependencies.\u003cbr/\u003e\nTo run the application, you have to:\n- TensorFlow Lite framework installed. [Install TensorFlow Lite](https://qengineering.eu/install-tensorflow-2-lite-on-raspberry-pi-4.html) \u003cbr/\u003e\n- OpenCV installed. [Install OpenCV 4.5](https://qengineering.eu/install-opencv-4.5-on-raspberry-pi-4.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/TensorFlow_Lite_Segmentation_RPi_32/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 \ncat.jpg.mp4 \u003cbr/\u003e\ndeeplabv3_257_mv_gpu.tflite \u003cbr/\u003e\nTestUnet.cpb \u003cbr/\u003e\nUnet.cpp\u003cbr/\u003e\n\n------------\n\n## Running the app.\nRun TestUnet.cpb with Code::Blocks. 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