{"id":15553572,"url":"https://github.com/turagalab/decode","last_synced_at":"2026-04-08T13:33:31.601Z","repository":{"id":43285076,"uuid":"285689796","full_name":"TuragaLab/DECODE","owner":"TuragaLab","description":"This is the official implementation of our publication \"Deep learning enables fast and dense single-molecule localization with high accuracy\" (Nature Methods)","archived":false,"fork":false,"pushed_at":"2023-06-22T11:37:19.000Z","size":48453,"stargazers_count":104,"open_issues_count":35,"forks_count":28,"subscribers_count":5,"default_branch":"master","last_synced_at":"2025-05-07T06:39:53.869Z","etag":null,"topics":["deep-learning","gpu","high-density","localization-microscopy","microscopy","pytorch","smlm"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/TuragaLab.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":null,"funding":null,"license":"LICENSE.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2020-08-06T23:13:31.000Z","updated_at":"2025-04-23T19:40:02.000Z","dependencies_parsed_at":"2024-01-05T23:45:15.629Z","dependency_job_id":"27553d97-1e99-45b0-b279-0c681888e56e","html_url":"https://github.com/TuragaLab/DECODE","commit_stats":null,"previous_names":[],"tags_count":24,"template":false,"template_full_name":null,"purl":"pkg:github/TuragaLab/DECODE","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TuragaLab%2FDECODE","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TuragaLab%2FDECODE/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TuragaLab%2FDECODE/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TuragaLab%2FDECODE/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/TuragaLab","download_url":"https://codeload.github.com/TuragaLab/DECODE/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TuragaLab%2FDECODE/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31558383,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-08T10:21:54.569Z","status":"ssl_error","status_checked_at":"2026-04-08T10:21:38.171Z","response_time":54,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: 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":["deep-learning","gpu","high-density","localization-microscopy","microscopy","pytorch","smlm"],"created_at":"2024-10-02T14:38:41.083Z","updated_at":"2026-04-08T13:33:31.573Z","avatar_url":"https://github.com/TuragaLab.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# DECODE\n[![Gateway Test](https://github.com/TuragaLab/DECODE/actions/workflows/test_gateway.yml/badge.svg)](https://github.com/TuragaLab/DECODE/actions/workflows/test_gateway.yml)\n[![Unit Tests](https://github.com/TuragaLab/DECODE/actions/workflows/unit_tests.yml/badge.svg)](https://github.com/TuragaLab/DECODE/actions/workflows/unit_tests.yml)\n[![Docs](https://readthedocs.org/projects/decode/badge/?version=master)](https://decode.readthedocs.io/en/master/?badge=master)\n\nDECODE is a Python and [Pytorch](http://pytorch.org/) based deep learning tool for single molecule \nlocalization microscopy (SMLM). It has high accuracy for a large range of imaging modalities and \nconditions. \nOn the public [SMLM 2016](http://bigwww.epfl.ch/smlm/challenge2016/) software benchmark competition,\nit [outperformed](http://bigwww.epfl.ch/smlm/challenge2016/leaderboard.html) all other fitters on \n12 out of 12 data-sets on both detection accuracy and localization error, often by a \nsubstantial margin. DECODE enables live-cell SMLM data with reduced light exposure in just 3 \nseconds and to image microtubules at ultra-high labeling density.\n\nDECODE works by training a DEep COntext DEpendent (DECODE) neural network to detect and localize \nemitters at sub-pixel resolution. Notably, DECODE also predicts detection and localization \nuncertainties, which can be used to generate superior super-resolution reconstructions.\n\n## Getting started\n\nThe easiest way to try out the algorithm is to have a look at the Google Colab Notebooks. \nWe provide them for training our algorithm and fitting experimental data. For installation instructions and further \ninformation please **refer to our** [**docs**](https://decode.readthedocs.io).\nYou can find these here:\n\n- [Documentation](https://decode.readthedocs.io)\n- DECODE Training (**NEW: v0.10**) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1uQ7w1zaqpy9EIjUdaLyte99FJIhJ6N8E?usp=sharing)\n- DECODE Fitting (**NEW: v0.10**) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1HAvJUL29vVuCHMZHMbU9jxd4fbLIPdhZ?usp=sharing)\n\n## Local Installation\n\nDetails about the installation can be found in the [documentation](https://decode.readthedocs.io).\n\n### DECODE cloud\nPlease reach out to Lucas (lrm@lrm.dev) if you want to use DECODE, but you do not have the right hardware, or\nwant to use it at a larger scale.\n\n## Video Tutorial\nAs part of the virtual [I2K 2020](https://www.janelia.org/you-janelia/conferences/from-images-to-knowledge-with-imagej-friends) conference we organized a workshop on DECODE.\nPlease find the video below.\n*DECODE is being actively developed, therefore the exact commands might differ from those shown in the video.*\n\n[![DECODE Video Tutorial](https://img.youtube.com/vi/zoWsj3FCUJs/0.jpg)](https://www.youtube.com/watch?v=zoWsj3FCUJs)\n\n## Paper\nThis is the *official* implementation of the [publication](https://rdcu.be/cw7uV).\n\nArtur Speiser*, Lucas-Raphael Müller*, Philipp Hoess, Ulf Matti, Christopher J. Obara, Wesley R. Legant, Anna Kreshuk, Jakob H. Macke†, Jonas Ries†, and Srinivas C. Turaga†, **Deep learning enables fast and dense single-molecule localization with high accuracy.** Nat Methods (2021). https://doi.org/10.1038/s41592-021-01236-x\n\n### Data availability\nThe data referred to in our paper can be accessed at the following locations:\n- Fig 3: Can be downloaded from the SMLM 2016 challenge [website](http://bigwww.epfl.ch/smlm/challenge2016/)\n- Fig 4: [here](https://oc.embl.de/index.php/s/SFM6Pc8RetX09pJ)\n- Fig 5: By request from the authors Wesley R Legant, Lin Shao, Jonathan B Grimm, Timothy A Brown, Daniel E Milkie, Brian B Avants, Luke D Lavis \u0026 Eric Betzig, [**High-density three-dimensional localization microscopy across large volumes**](https://www.nature.com/articles/nmeth.3797), _Nature Methods_, *13*, pages 359–365 (2016).\n\n## Contributors\nIf you want to get in touch, the best way to get your questions answered is our [**GitHub discussions page**](https://github.com/TuragaLab/DECODE/discussions)\n- Artur Speiser ([@aspeiser](https://github.com/ASpeiser), arturspeiser@gmail.com)\n- Lucas-Raphael Müller ([@haydnspass](https://github.com/Haydnspass), lucas.mueller@embl.de)\n\n## Support\n\nJakob H. Macke and Artur Speiser were supported by the German Research Foundation (DFG) through Germany’s Excellence Strategy (EXC-Number 2064/1, project no. 390727645) and the German Federal Ministry of Education and Research (BMBF, project no. [ADIMEM](https://fit.uni-tuebingen.de/Project/Details?id=9199), FKZ 01IS18052). \nSrinivas C. Turaga is supported by the Howard Hughes Medical Institute. Jonas Ries, Lucas-Raphael Mueller and Philipp Hoess were supported by the European Molecular Biology Laboratory, the European Research Council (grant no. CoG-724489 to Jonas Ries) and the National Institutes of Health Common Fund 4D Nucleome Program (grant no. U01 EB021223 to Jonas Ries). \n\n### Join us\nWe offer several open positions. Please take a look at the [pdf](https://www.embl.de/download/ries/other/Simalesam_ad.pdf) on how to apply.\n\n### Acknowledgements\n- Don Olbris ([@olbris](https://github.com/olbris), olbrisd@janelia.hhmi.org) for help with python packaging.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fturagalab%2Fdecode","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fturagalab%2Fdecode","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fturagalab%2Fdecode/lists"}