{"id":22716693,"url":"https://github.com/lemariva/maixpy_yolov2","last_synced_at":"2025-04-13T17:50:19.389Z","repository":{"id":109048821,"uuid":"231117330","full_name":"lemariva/MaixPy_YoloV2","owner":"lemariva","description":"YOLOv2 object detector training for a MAix-board","archived":false,"fork":false,"pushed_at":"2020-02-13T16:58:43.000Z","size":2471,"stargazers_count":23,"open_issues_count":4,"forks_count":7,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-03-27T08:45:17.197Z","etag":null,"topics":["maix-board","maixpy","tensorflow","yolov2"],"latest_commit_sha":null,"homepage":"https://lemariva.com/blog/2020/01/maixpy-object-detector-mobilenet-and-yolov2-sipeed-maix-dock","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/lemariva.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}},"created_at":"2019-12-31T16:24:05.000Z","updated_at":"2025-01-07T13:50:11.000Z","dependencies_parsed_at":"2023-03-27T08:46:32.973Z","dependency_job_id":null,"html_url":"https://github.com/lemariva/MaixPy_YoloV2","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/lemariva%2FMaixPy_YoloV2","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lemariva%2FMaixPy_YoloV2/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lemariva%2FMaixPy_YoloV2/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lemariva%2FMaixPy_YoloV2/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/lemariva","download_url":"https://codeload.github.com/lemariva/MaixPy_YoloV2/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248757983,"owners_count":21156954,"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":["maix-board","maixpy","tensorflow","yolov2"],"created_at":"2024-12-10T14:10:52.325Z","updated_at":"2025-04-13T17:50:19.359Z","avatar_url":"https://github.com/lemariva.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# MaixPy_YoloV2\nThis repository helps you to extend the models is to detect objects using [YOLO-V2](https://pjreddie.com/media/files/papers/YOLO9000.pdf) on a MaixPY\n\n# DIY\nInstall Docker on your machine and create a `notebooks` folder inside e.g. `Documents`:\n```sh\ncurl -sSL https://get.docker.com | sh\nmkdir ~/Documents/notebooks/\n```\nThen, deploy the `tensorflow/tensorflow:latest-py3-jupyter` image using:\n```sh\nsudo docker run -d -p 8888:8888 -v ~/Documents/notebooks/:/tf/notebooks/ tensorflow/tensorflow:latest-py3-jupyter\n```\nI explained the `-v` flag [here](https://lemariva.com/blog/2019/04/data-in-docker-analytics). But, it is a \"Bind Mount\". This means the `~/Documents/notebooks/` folder is connected to the `/tf/notebooks/` folder inside the container. This makes the data inside the folder `/tf/notebooks/` (container) persistent. Otherwise, if the container is stopped you lose the files.\n\nThen, clone this repository inside `~/Documents/notebooks/`\n```sh\ncd ~/Documents/notebooks/\ngit clone https://github.com/lemariva/MaixPy_YoloV2\n```\n\nOpen the following URL in your host web browser: `http://localhost:8888`\n\nYou need a token to log in. The token is inside the container. List the container to get the ID with following command:\n\n```\n$ docker container ls\nCONTAINER ID        IMAGE    [....]\n5082a85283bb        tensorflow/  [....]\n```\n\nThen, read the logs typing:\n\n```\n$ docker logs 5082a85283bb\n\n[...]\nThe Jupyter Notebook is running at:\n[...] http://ac64d540a1cb:8888/?token=df050fa3b53de5f9203ca862e5f3656962b665dc224243a8\n[...]\n```\nThe hash after `token=` is the token to log in.\n\nI added a training example with the brio 33594. You can train the model running the code inside `training.ipynb`. \n\nNote: Some additional libraries are required inside the container and they are installed on the first cell block of the Notebook. You don't need to run the cell every time that you compile the model. However, if you start a new container (not restart the stopped one), you need to install them again. You can extend the container to include these libraries per default. You can find more info about that on this \u003ca href=\"https://lemariva.com/blog/2018/12/analytics-docker-for-data-science-environment\" target=\"_blank\"\u003etutorial\u003c/a\u003e.\n\n# More Info\nVisit the following tutorial for more information: [MAixPy: Object detector - MobileNet and YOLOv2 on Sipeed MAix Dock](https://lemariva.com/blog/2020/01/maixpy-object-detector-mobilenet-and-yolov2-sipeed-maix-dock)\n\n# Acknowledgement\n* Ported from [penny4860/Yolo-digit-detector](https://github.com/penny4860/Yolo-digit-detector) to Jupyter Notebooks and upgraded to Tensorflow 2.0. \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flemariva%2Fmaixpy_yolov2","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flemariva%2Fmaixpy_yolov2","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flemariva%2Fmaixpy_yolov2/lists"}