{"id":26291540,"url":"https://github.com/larswaechter/quickdraw-cnn","last_synced_at":"2025-05-08T01:41:44.807Z","repository":{"id":43344581,"uuid":"461238964","full_name":"larswaechter/quickdraw-cnn","owner":"larswaechter","description":"A convolutional neural network using Tensorflow and Google's Quick, Draw! dataset to recognize hand drawn images.","archived":false,"fork":false,"pushed_at":"2023-03-07T15:54:14.000Z","size":7893,"stargazers_count":13,"open_issues_count":2,"forks_count":3,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-03-31T16:14:16.459Z","etag":null,"topics":["artificial-intelligence","machine-learning","quickdraw-dataset","tensorflow"],"latest_commit_sha":null,"homepage":"https://larswaechter.dev/blog/recognizing-hand-drawn-doodles/","language":"JavaScript","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/larswaechter.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}},"created_at":"2022-02-19T15:55:25.000Z","updated_at":"2025-03-12T07:37:29.000Z","dependencies_parsed_at":"2023-01-24T19:17:30.802Z","dependency_job_id":null,"html_url":"https://github.com/larswaechter/quickdraw-cnn","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/larswaechter%2Fquickdraw-cnn","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/larswaechter%2Fquickdraw-cnn/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/larswaechter%2Fquickdraw-cnn/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/larswaechter%2Fquickdraw-cnn/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/larswaechter","download_url":"https://codeload.github.com/larswaechter/quickdraw-cnn/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252983761,"owners_count":21835758,"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":["artificial-intelligence","machine-learning","quickdraw-dataset","tensorflow"],"created_at":"2025-03-15T00:39:25.004Z","updated_at":"2025-05-08T01:41:44.767Z","avatar_url":"https://github.com/larswaechter.png","language":"JavaScript","funding_links":[],"categories":[],"sub_categories":[],"readme":"# quickdraw-cnn\n\nA convolutional neural network using Tensorflow and Google's Quick, Draw! [dataset](https://github.com/googlecreativelab/quickdraw-dataset) to recognize hand drawn images including a webapp to draw them.\n\nRead my [blog post](https://larswaechter.dev/blog/recognizing-hand-drawn-doodles/) for more information. You can find a webapp demo [here](https://quickdraw-cnn.fly.dev/).\n\n![Preview](./webapp.png)\n\n## Setup\n\n### cnn\n\nSwitch to the `cnn` directory, create a new virtual environment and install the required packages:\n\n```\npython -m venv ./venv\nsource ./venv/bin/activate\npip install -r requirements.txt\n```\n\nThen, launch Jupyter in the target directory:\n\n```\njupyter notebook\n```\n\n### webapp\n\n#### Native\n\nSwitch to the `webapp` directory, create another venv and install the requirements as mentioned above. You can run the webapp using the following command:\n\n```\nuvicorn main:app\n```\n\nThe webapp should be available at [http://127.0.0.1:8000](http://127.0.0.1:8000).\n\n#### Docker\n\nAlternatively, you can also run it via Docker:\n\n```\ndocker build . -t quickdraw-webapp\ndocker run -p 443:443 quickdraw-webapp\n```\n\nThe webapp should be available at [http://0.0.0.0:443](http://0.0.0.0:443).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flarswaechter%2Fquickdraw-cnn","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flarswaechter%2Fquickdraw-cnn","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flarswaechter%2Fquickdraw-cnn/lists"}