{"id":16838393,"url":"https://github.com/olafenwamoses/model-playgrounds","last_synced_at":"2025-10-12T00:06:01.954Z","repository":{"id":110143808,"uuid":"119113911","full_name":"OlafenwaMoses/Model-Playgrounds","owner":"OlafenwaMoses","description":"A project developed and maintained as part of the aim at bringing current capabilities in machine learning and artificial intelligence into practical use for non-programmers and average computer users.","archived":false,"fork":false,"pushed_at":"2018-02-25T17:29:24.000Z","size":4044,"stargazers_count":14,"open_issues_count":0,"forks_count":12,"subscribers_count":0,"default_branch":"master","last_synced_at":"2025-10-12T00:03:30.324Z","etag":null,"topics":["artificial-intelligence","artificial-neural-networks","densenet","inbuilt-api","inceptionv3","machine-learning","model-playgrounds","playgrounds","resnet","resnet-50","squeezenet"],"latest_commit_sha":null,"homepage":"https://moses.aicommons.science","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/OlafenwaMoses.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,"zenodo":null}},"created_at":"2018-01-26T23:20:59.000Z","updated_at":"2023-08-23T12:59:35.000Z","dependencies_parsed_at":"2023-04-19T20:18:49.799Z","dependency_job_id":null,"html_url":"https://github.com/OlafenwaMoses/Model-Playgrounds","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/OlafenwaMoses/Model-Playgrounds","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OlafenwaMoses%2FModel-Playgrounds","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OlafenwaMoses%2FModel-Playgrounds/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OlafenwaMoses%2FModel-Playgrounds/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OlafenwaMoses%2FModel-Playgrounds/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/OlafenwaMoses","download_url":"https://codeload.github.com/OlafenwaMoses/Model-Playgrounds/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/OlafenwaMoses%2FModel-Playgrounds/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279009475,"owners_count":26084609,"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","status":"online","status_checked_at":"2025-10-11T02:00:06.511Z","response_time":55,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["artificial-intelligence","artificial-neural-networks","densenet","inbuilt-api","inceptionv3","machine-learning","model-playgrounds","playgrounds","resnet","resnet-50","squeezenet"],"created_at":"2024-10-13T12:22:58.168Z","updated_at":"2025-10-12T00:06:01.936Z","avatar_url":"https://github.com/OlafenwaMoses.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cbr\u003e\n\u003ch1 style=\"font-family: Calibri;\" \u003e\u003cb\u003e \u0026nbsp \u0026nbsp \u0026nbsp \u0026nbsp \u0026nbsp Machine Learning Model Playgrounds \u003c/b\u003e\u003c/h1\u003e\n\n\u003chr\u003e \u003chr\u003e\n\n\u003cdiv style=\"margin-left: 20px; max-width: 600px;\"  \u003e \u003cp  style=\"font-family: Calibri;\" \u003e \u0026nbsp \u0026nbsp \u0026nbsp \u0026nbsp The \u003cb\u003e Machine Learning Model Playgrounds\u003c/b\u003e is a project that is part of the dream of\n         a team of Moses Olafenwa and John Olafenwa to bring current capabilities in machine learning and artificial intelligence into practical \n        use for non-programmers and average computer users. This project is the first step in what we hope will become\n        mainstream application in modern technology in which Computers, Smartphones, Edge Devices and Systems will \n        have in-built state-of-the-art Machine Learning and Artificial Intelligence capabilities without having to\n        connect to cloud based services. \u003cbr\u003e\n        \u0026nbsp \u0026nbsp \u0026nbsp \u0026nbsp The \u003cb\u003eMachine Learning Model Playgrounds \u003c/b\u003e is a series of Windows programs built using pure \n        python libraries and code. Each of the programs is a user-friendly demo of Image Classification powered by\n        a specific image classification model of popular Machine Learning Algorithms trained on the ImageNet (1000 object classes )\n        dataset. Each program provides a user interface where users can select a picture from their Windows system folder\n        while the program process the selected picture and give top-10 possible results of the objects detected with\n        percentage probability per each result. \u003cbr\u003e\u003cbr\u003e\n        \u0026nbsp \u0026nbsp \u0026nbsp \u0026nbsp \u0026nbsp This repository contains the source code, models and builds of each of the programs in the\n        \u003cb\u003eModel Playgrounds \u003c/b\u003e series. It is provided to allow other developers outside our team to adapt, modify or extend\n         the code to produce more programs that may be specific to a social, business, economic or scientific need. \u003cbr\u003e \u003cbr\u003e\n         \u0026nbsp \u0026nbsp \u0026nbsp \u0026nbsp The dependencies used for this project are listed below: \u003cbr\u003e\n         \u0026nbsp \u0026nbsp  \u003cb\u003e- Python 3.5.2\u003c/b\u003e \u003cbr\u003e\n         \u0026nbsp \u0026nbsp  \u003cb\u003e- Tensorflow 1.4.0\u003c/b\u003e \u003cbr\u003e\n         \u0026nbsp \u0026nbsp  \u003cb\u003e- Keras 2.0.8\u003c/b\u003e \u003cbr\u003e\n         \u0026nbsp \u0026nbsp  \u003cb\u003e- Numpy 1.13.1\u003c/b\u003e \u003cbr\u003e\n         \u0026nbsp \u0026nbsp  \u003cb\u003e- Scipy 0.19.1\u003c/b\u003e \u003cbr\u003e\n         \u0026nbsp \u0026nbsp  \u003cb\u003e- wxPython 4.0.0\u003c/b\u003e \u003cbr\u003e \u003cbr\u003e\n          Below you will find the details and pictures of each of the programs in the series.\n    \u003c/p\u003e\u003cbr\u003e\u003cbr\u003e\u003ch2 style=\"font-family: Calibri;\" \u003e\u003cb\u003e \u003e\u003e ResNet Playground\u003c/b\u003e\u003c/h2\u003e\n          \u003cbr\u003e\n          \u003cimg src=\"images/resnet_demo.png\" style=\"max-width: 600px; height: auto;\" /\u003e\n          \u003cbr\u003e \u003cp  style=\"font-family: Calibri;\" \u003e \n              \u0026nbsp \u0026nbsp \u0026nbsp \u0026nbsp \u0026nbsp The \u003cb\u003eResNet Playground\u003c/b\u003e is powered by the ResNet50 model trained\n              on the ImageNet dataset. You can find its source codes in the \u003ci\u003eresnet-playground\u003c/i\u003e folder\n               of this repository or follow this \u003ca href=\"https://github.com/OlafenwaMoses/Model-Playgrounds/tree/master/resnet-playground\" \u003elink\u003c/a\u003e. You can also download the Windows Installer\n                for the program in the Release section of this project or follow this \u003ca href=\"https://github.com/OlafenwaMoses/Model-Playgrounds/releases/download/v1.0/ResNet.Playground.1.0.exe\" \u003elink\u003c/a\u003e.\u003cbr\u003e\n                \u0026nbsp \u0026nbsp \u0026nbsp \u0026nbsp \u0026nbsp This program is a Windows 64-bit software that can be installed on\n                Windows 7 and later versions of the Operating System. It has an installer size of 227mb and install\n                 size of 690mb. The program was compiled using PyInstaller 3.3 for Python 3.5 . \n          \u003c/p\u003e\u003cbr\u003e\u003cbr\u003e\u003ch2 style=\"font-family: Calibri;\" \u003e\u003cb\u003e\u003e\u003e DenseNet Playground\u003c/b\u003e\u003c/h2\u003e\n          \u003cbr\u003e\n          \u003cimg src=\"images/densenet_demo.png\" style=\"max-width: 600px; height: auto;\" /\u003e\n          \u003cbr\u003e\u003cp  style=\"font-family: Calibri;\" \u003e \n              \u0026nbsp \u0026nbsp \u0026nbsp \u0026nbsp \u0026nbsp The \u003cb\u003eDenseNet Playground\u003c/b\u003e is powered by the DenseNet121 model trained\n              on the ImageNet dataset. You can find its source codes in the \u003ci\u003edensenet-playground\u003c/i\u003e folder\n               of this repository or follow this \u003ca href=\"https://github.com/OlafenwaMoses/Model-Playgrounds/tree/master/densenet-playground\" \u003elink\u003c/a\u003e. You can also download the Windows Installer\n                for the program in the Release section of this project or follow this \u003ca href=\"https://github.com/OlafenwaMoses/Model-Playgrounds/releases/download/v1.0/DenseNet.Playground.1.0.exe\" \u003elink\u003c/a\u003e.\u003cbr\u003e\n                \u0026nbsp \u0026nbsp \u0026nbsp \u0026nbsp \u0026nbsp This program is a Windows 64-bit software that can be installed on\n                Windows 7 and later versions of the Operating System. It has an installer size of 166mb and install\n                 size of 623mb. The program was compiled using PyInstaller 3.3 for Python 3.5 . \n          \u003c/p\u003e\u003cbr\u003e\u003cbr\u003e\u003ch2 style=\"font-family: Calibri;\" \u003e\u003cb\u003e\u003e\u003e SqueezeNet Playground\u003c/b\u003e\u003c/h2\u003e\n          \u003cbr\u003e\n          \u003cimg src=\"images/squeezenet_demo.png\" style=\"max-width: 600px; height: auto;\" /\u003e\n          \u003cbr\u003e\u003cp  style=\"font-family: Calibri;\" \u003e \n              \u0026nbsp \u0026nbsp \u0026nbsp \u0026nbsp \u0026nbsp The \u003cb\u003eSqueezeNet Playground\u003c/b\u003e is powered by the SqueezeNet model trained\n              on the ImageNet dataset. You can find its source codes in the \u003ci\u003esqueezenet-playground\u003c/i\u003e folder\n               of this repository or follow this \u003ca href=\"https://github.com/OlafenwaMoses/Model-Playgrounds/tree/master/squeezenet-playground\" \u003elink\u003c/a\u003e. You can also download the Windows Installer\n                for the program in the Release section of this project or follow this \u003ca href=\"https://github.com/OlafenwaMoses/Model-Playgrounds/releases/download/v1.0/SqueezeNet.Playground.1.0.exe\" \u003elink\u003c/a\u003e.\u003cbr\u003e\n                \u0026nbsp \u0026nbsp \u0026nbsp \u0026nbsp \u0026nbsp This program is a Windows 64-bit software that can be installed on\n                Windows 7 and later versions of the Operating System. It has an installer size of 142mb and install\n                 size of 596mb. The program was compiled using PyInstaller 3.3 for Python 3.5 . \n          \u003c/p\u003e\u003cbr\u003e\u003cbr\u003e \u003ch2 style=\"font-family: Calibri;\" \u003e\u003cb\u003e\u003e\u003e Inception Playground\u003c/b\u003e\u003c/h2\u003e\n          \u003cbr\u003e\n          \u003cimg src=\"images/inception_demo.png\" style=\"max-width: 600px; height: auto;\" /\u003e\n          \u003cbr\u003e \u003cp  style=\"font-family: Calibri;\" \u003e \n              \u0026nbsp \u0026nbsp \u0026nbsp \u0026nbsp \u0026nbsp The \u003cb\u003eInception Playground\u003c/b\u003e is powered by the Inception V3 model trained\n              on the ImageNet dataset. You can find its source codes in the \u003ci\u003einception-playground\u003c/i\u003e folder\n               of this repository or follow this \u003ca href=\"https://github.com/OlafenwaMoses/Model-Playgrounds/tree/master/inception-playground\" \u003elink\u003c/a\u003e. You can also download the Windows Installer\n                for the program in the Release section of this project or follow this \u003ca href=\"https://github.com/OlafenwaMoses/Model-Playgrounds/releases/download/v1.0/Inception.Playground.1.0.exe\" \u003elink\u003c/a\u003e.\u003cbr\u003e\n                \u0026nbsp \u0026nbsp \u0026nbsp \u0026nbsp \u0026nbsp This program is a Windows 64-bit software that can be installed on\n                Windows 7 and later versions of the Operating System. It has an installer size of 221mb and install\n                 size of 686mb. The program was compiled using PyInstaller 3.3 for Python 3.5 . \n          \u003c/p\u003e \u003cbr\u003e\u003cbr\u003e \u0026nbsp \u0026nbsp \u0026nbsp \u0026nbsp \u0026nbsp We are open to comments, suggestions and questions on this project. Feel free\n           to reach to our team via our contact below: \u003cbr\u003e  \u0026nbsp \u0026nbsp \u003cb\u003e- Moses Olafenwa\u003c/b\u003e, Chief Programmer of the Playground project \u003cbr\u003e \u0026nbsp \u0026nbsp  \u0026nbsp \u003cb\u003e Email: \u003c/b\u003e \u003ca href=\"mailto:guymodscientist@gamil.com\" \u003e guymodscientist@gmail.com \u003c/a\u003e \u003cbr\u003e\n            \u0026nbsp \u0026nbsp  \u0026nbsp \u003cb\u003e Twitter: \u003c/b\u003e \u003ca target=\"_blank\" href=\"https://www.twitter.com/OlafenwaMoses\" \u003e @OlafenwaMoses \u003c/a\u003e \u003cbr\u003e\u003cbr\u003e \u0026nbsp \u0026nbsp \u003cb\u003e- John Olafenwa\u003c/b\u003e, Technical Adviser of the Playground project \u003cbr\u003e\n            \u0026nbsp \u0026nbsp  \u0026nbsp \u003cb\u003e Email: \u003c/b\u003e \u003ca href=\"mailto:johnolafenwa@gamil.com\" \u003e johnolafenwa@gmail.com \u003c/a\u003e \u003cbr\u003e\n            \u0026nbsp \u0026nbsp  \u0026nbsp \u003cb\u003e Twitter: \u003c/b\u003e \u003ca target=\"_blank\" href=\"https://www.twitter.com/johnolafenwa\" \u003e @johnolafenwa \u003c/a\u003e\u003c/div\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Folafenwamoses%2Fmodel-playgrounds","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Folafenwamoses%2Fmodel-playgrounds","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Folafenwamoses%2Fmodel-playgrounds/lists"}