{"id":14959066,"url":"https://github.com/qengineering/tensorflow_lite_ssd_jetson-nano","last_synced_at":"2026-02-22T12:11:52.530Z","repository":{"id":112948344,"uuid":"335302891","full_name":"Qengineering/TensorFlow_Lite_SSD_Jetson-Nano","owner":"Qengineering","description":"TensorFlow Lite SSD on a Jetson Nano 28.5 FPS","archived":false,"fork":false,"pushed_at":"2021-12-27T09:37:45.000Z","size":22074,"stargazers_count":12,"open_issues_count":1,"forks_count":2,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-01-31T03:01:37.377Z","etag":null,"topics":["aarch64","cpp","gpu-acceleration","gpu-delegate","jetson-nano","mobilenet-ssd","ssd-mobilenet","tensorflow-examples","tensorflow-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":"2021-02-02T13:44:47.000Z","updated_at":"2024-04-16T13:50:51.000Z","dependencies_parsed_at":null,"dependency_job_id":"d9ac8518-1b59-41c6-b2e6-c1d0b83d274b","html_url":"https://github.com/Qengineering/TensorFlow_Lite_SSD_Jetson-Nano","commit_stats":{"total_commits":12,"total_committers":1,"mean_commits":12.0,"dds":0.0,"last_synced_commit":"36e55a03be6994159ca9627157692ec896955246"},"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_Jetson-Nano","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2FTensorFlow_Lite_SSD_Jetson-Nano/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2FTensorFlow_Lite_SSD_Jetson-Nano/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2FTensorFlow_Lite_SSD_Jetson-Nano/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Qengineering","download_url":"https://codeload.github.com/Qengineering/TensorFlow_Lite_SSD_Jetson-Nano/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":237999554,"owners_count":19399903,"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","cpp","gpu-acceleration","gpu-delegate","jetson-nano","mobilenet-ssd","ssd-mobilenet","tensorflow-examples","tensorflow-lite"],"created_at":"2024-09-24T13:18:47.423Z","updated_at":"2026-02-22T12:11:52.515Z","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_Jetson-Nano\n![output image]( https://qengineering.eu/images/SSD_Jetson.webp )\u003cbr/\u003e\n## TensorFlow Lite SSD running on a Jetson Nano \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\n\nA fast C++ implementation of TensorFlow Lite SSD on a Jetson Nano.\u003cbr/\u003e\nOnce overclocked to 2015 MHz, the app runs at 28.5 FPS.\u003cbr/\u003e\n\nhttps://arxiv.org/abs/1611.10012 \u003cbr/\u003e\nTraining set: COCO \u003cbr/\u003e\nSize: 300x300 \u003cbr/\u003e\n\n## Benchmark.\n| CPU 2015 MHz | GPU 2015 MHz | CPU 1479 MHz | GPU 1479 MHZ | RPi 4 64os 1950 MHz |\n|  :------------: | :-------------: | :-------------:  | :-------------: | :-------------: |\n|  28.5 FPS |  -- FPS  | 21.8 FPS | -- FPS  | 24 FPS |\n\nSpecial made for a Jetson Nano see [Q-engineering deep learning examples](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html) \u003cbr/\u003e\n\u003cbr/\u003e\n## Dependencies.\nTo run the application, you have to:\n- TensorFlow Lite framework installed. [Install TensorFlow Lite](https://qengineering.eu/install-tensorflow-2-lite-on-jetson-nano.html) \u003cbr/\u003e\n- Optional OpenCV installed. [Install OpenCV 4.5](https://qengineering.eu/install-opencv-4.5-on-jetson-nano.html) \u003cbr/\u003e\n- Code::Blocks installed. (```$ sudo apt-get install codeblocks```)\n## Running 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_SSD_Jetson-Nano/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 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.\u003cbr/\u003e\nYou may need to adapt the specified library locations in *TestTensorFlow_Lite.cpb* to match your directory structure.\u003cbr/\u003e\u003cbr/\u003e\nWith the `#define GPU_DELEGATE` uncommented, the TensorFlow Lite will deploy GPU delegates, if you have, of course, the appropriate libraries compiled by bazel. [Install GPU delegates](https://qengineering.eu/install-tensorflow-2-lite-on-jetson-nano.html) \u003cbr/\u003e\u003cbr/\u003e\nSee the RPi 4 movie at: https://vimeo.com/393889226\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\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqengineering%2Ftensorflow_lite_ssd_jetson-nano","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fqengineering%2Ftensorflow_lite_ssd_jetson-nano","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqengineering%2Ftensorflow_lite_ssd_jetson-nano/lists"}