{"id":21361422,"url":"https://github.com/shure-dev/ai-2022-genart","last_synced_at":"2026-07-20T17:34:38.739Z","repository":{"id":205570335,"uuid":"560003598","full_name":"shure-dev/AI-2022-genart","owner":"shure-dev","description":"AI-2022-genart","archived":false,"fork":false,"pushed_at":"2022-11-08T13:03:30.000Z","size":22719,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-10-19T04:44:58.157Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/shure-dev.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}},"created_at":"2022-10-31T14:44:02.000Z","updated_at":"2024-07-20T17:20:33.000Z","dependencies_parsed_at":null,"dependency_job_id":"54d99371-4d34-4c5d-a8e0-c24bb197376a","html_url":"https://github.com/shure-dev/AI-2022-genart","commit_stats":null,"previous_names":["shure-dev/ai-2022-genart"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/shure-dev/AI-2022-genart","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shure-dev%2FAI-2022-genart","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shure-dev%2FAI-2022-genart/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shure-dev%2FAI-2022-genart/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shure-dev%2FAI-2022-genart/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/shure-dev","download_url":"https://codeload.github.com/shure-dev/AI-2022-genart/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shure-dev%2FAI-2022-genart/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":35695246,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-07-20T02:08:10.276Z","status":"ssl_error","status_checked_at":"2026-07-20T02:08:09.736Z","response_time":111,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":[],"created_at":"2024-11-22T06:09:44.011Z","updated_at":"2026-07-20T17:34:38.711Z","avatar_url":"https://github.com/shure-dev.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# GAN for generating art\n\u003e Nov.2022 Artificial Intelligence, Home assignment\n\n\n![Test Image 1](results/results/real.png)\n![Test Image 2](results/results/2monet.png)\n![Test Image 3](results/results/3ukiyoe.png)\n![Test Image 4](results/results/4ceznne.png)\n![Test Image 5](results/results/5vangogh.png)\n\n## Members\n- Max Grönlund\n- Joona Hytönen \n- Ville Juuti\n- Juho Kemppainen\n- Yusuke Mikami\n- Simo Turunen\n\n## How to run\n1. Go file \"AI-2022-genart/Joensuu-cycleGAN.ipynb\"\n2. Run notebook \"AI-2022-genart/Joensuu-cycleGAN.ipynb\"\n\n## Abstract\n\nAim of this group project was to implement General adversarial networks (GAN) model architecture \nin our work where we suppose to transform pictures of our city (Joensuu) taken by us into art style like. \nGAN concept based on combination of two separated neural network. We used technique named CycleGAN image-to-image translation. \nThis technique provides several different ways to manipulate images. We used the one which \ntranslates real photographs into specific art style. \n\nWe used Deepnote for collaborator purposes. We pulled original CycleGAN code \nfrom Github repository to our Deepnote notebook, then for image transformation \nwe first implemented pre-trained art-style models like Monet, Ukiyoe, Cezanne and Vangogh and \nthen trained models with training data proposed by CycleGAN developing team. Image manipulation of \nreal photographs was succeeded. The number of real images is 14. Collection of real Images and results\ncan be found in the 6. Experiments and results section.  \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshure-dev%2Fai-2022-genart","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshure-dev%2Fai-2022-genart","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshure-dev%2Fai-2022-genart/lists"}