{"id":22862766,"url":"https://github.com/nedap/dev-day-rfidtags","last_synced_at":"2026-03-19T23:51:23.704Z","repository":{"id":46037086,"uuid":"225394619","full_name":"nedap/dev-day-rfidtags","owner":"nedap","description":"Nedap DevDay 2019 retail case: automatic RFID label classification using machine learning","archived":false,"fork":false,"pushed_at":"2022-11-28T11:53:53.000Z","size":4091,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-03-25T04:07:54.570Z","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":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/nedap.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2019-12-02T14:31:52.000Z","updated_at":"2022-03-23T02:54:28.000Z","dependencies_parsed_at":"2023-01-23T11:31:31.292Z","dependency_job_id":null,"html_url":"https://github.com/nedap/dev-day-rfidtags","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/nedap%2Fdev-day-rfidtags","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nedap%2Fdev-day-rfidtags/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nedap%2Fdev-day-rfidtags/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nedap%2Fdev-day-rfidtags/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/nedap","download_url":"https://codeload.github.com/nedap/dev-day-rfidtags/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246443525,"owners_count":20778247,"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":[],"created_at":"2024-12-13T10:14:42.733Z","updated_at":"2026-03-19T23:51:23.648Z","avatar_url":"https://github.com/nedap.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# DevDay 2019 - Retail RFID Label Scanning\n\nIn this data science mini-hackathon we are going to work with a real-life problem from Nedap Retail. In the world of RFID label scanning, it is very hard to isolate products since the entire store is filled with RFID tags (see Figure 1 and Figure 2). Our challenge is to come up with an algorithm to do this separation automatically when scanning a batch of newly arriving products.\n\nYou are going to develop and evaluate a basic machine learning pipeline to address the case above. We give you all the tools needed to get started with basic machine learning to show you the power of these algorithms.\n\n**Figure 1: scanning RFID labels in a typical retail store**\n\n![](./pictures/figure1.png)\n\n**Figure 2: label scanning can be noisy**\n\n![](./pictures/figure2.png)\n\n\n## Environment\n\nFirst, you can clone this repository to a location of your choice:\n\n```sh\ngit clone https://github.com/nedap/dev-day-rfidtags.git\n```\n\nIf you don't have git installed, you can also download the repository [here](https://github.com/nedap/dev-day-rfidtags/archive/master.zip).\n\nAfterwards, we will setup the environment.\n\n### Option 1: Anaconda\n\nDownload anaconda here: [installation instructions](https://www.anaconda.com/distribution/#download-section).\n\n#### Linux/macOS\n\nAfterwards, run the following commands in the root of this repository.\n\n```sh\nconda env update -f environment.yml\nconda activate rfidtags\n\npip install --quiet ipykernel autopep8\npython -m ipykernel install --user --name rfidtags --display-name \"Python (rfidtags)\"\n\njupyter notebook\n```\n\nJupyter should automatically open a page in your browser.\n\n#### Windows\n\nOpen the Anaconda Navigator. Then, create a new virtual environment called `rfidtags` by importing the `environment.yml` in this repository.\n\nOnce this is done, select that environment and launch Jupyter.\n\n### Option 2: Docker\n\nMake sure you have docker installed: [installation instructions](https://docs.docker.com/v17.09/engine/installation/#supported-platforms).\n\nStart the docker container and visit http://localhost:8888/notebooks/work/notebooks/rfid-classification.ipynb (token: `devday`).\n\n```sh\ncd dev-day-rfidtags/\ndocker-compose up\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnedap%2Fdev-day-rfidtags","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnedap%2Fdev-day-rfidtags","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnedap%2Fdev-day-rfidtags/lists"}