{"id":13718707,"url":"https://github.com/zae-bayern/elpv-dataset","last_synced_at":"2025-05-07T10:33:52.508Z","repository":{"id":37734899,"uuid":"124222321","full_name":"zae-bayern/elpv-dataset","owner":"zae-bayern","description":"A dataset of functional and defective solar cells extracted from EL images of solar modules","archived":false,"fork":false,"pushed_at":"2024-10-13T20:58:12.000Z","size":92994,"stargazers_count":262,"open_issues_count":0,"forks_count":80,"subscribers_count":14,"default_branch":"master","last_synced_at":"2025-04-26T13:01:47.247Z","etag":null,"topics":["computer-vision","machine-learning","photovoltaic","solar-cells","solar-energy"],"latest_commit_sha":null,"homepage":"","language":"Python","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/zae-bayern.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2018-03-07T10:53:56.000Z","updated_at":"2025-04-23T05:49:30.000Z","dependencies_parsed_at":"2024-11-06T00:43:56.682Z","dependency_job_id":null,"html_url":"https://github.com/zae-bayern/elpv-dataset","commit_stats":{"total_commits":26,"total_committers":1,"mean_commits":26.0,"dds":0.0,"last_synced_commit":"73280a3fb543f970f266733b6c00e87a8294c918"},"previous_names":[],"tags_count":3,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zae-bayern%2Felpv-dataset","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zae-bayern%2Felpv-dataset/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zae-bayern%2Felpv-dataset/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zae-bayern%2Felpv-dataset/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/zae-bayern","download_url":"https://codeload.github.com/zae-bayern/elpv-dataset/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251145337,"owners_count":21543023,"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":["computer-vision","machine-learning","photovoltaic","solar-cells","solar-energy"],"created_at":"2024-08-03T01:00:36.284Z","updated_at":"2025-05-07T10:33:52.441Z","avatar_url":"https://github.com/zae-bayern.png","language":"Python","funding_links":[],"categories":["Renewable Energy","Renewable Energy Sources"],"sub_categories":["Photovoltaics and Solar Energy","Photovoltaic"],"readme":"# A Benchmark for Visual Identification of Defective Solar Cells in Electroluminescence Imagery\n\n[![PyPI - Version](https://img.shields.io/pypi/v/elpv-dataset.svg)](https://pypi.org/project/elpv-dataset)\n[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/elpv-dataset.svg)](https://pypi.org/project/elpv-dataset)\n\nThis repository provides a dataset of solar cell images extracted from\nhigh-resolution electroluminescence images of photovoltaic modules.\n\n![An overview of images in the dataset. The darker the red is, the higher is the\nlikelihood of a defect in the solar cell overlayed by the corresponding color.](./doc/images/overview.jpg)\n\n## The Dataset\n\nThe dataset contains 2,624 samples of 300x300 pixels 8-bit grayscale images of\nfunctional and defective solar cells with varying degree of degradations\nextracted from 44 different solar modules. The defects in the annotated images\nare either of intrinsic or extrinsic type and are known to reduce the power\nefficiency of solar modules.\n\nAll images are normalized with respect to size and perspective.\nAdditionally, any distortion induced by the camera lens used to capture the EL images was\neliminated prior to solar cell extraction.\n\n## Annotations\n\nEvery image is annotated with a defect probability (a floating point value\nbetween 0 and 1) and the type of the solar module (either mono- or\npolycrystalline) the solar cell image was originally extracted from.\n\n## Usage\n\nInstall the Python package\n```console\npip install elpv-dataset\n```\n\nand load the images and the corresponding annotations as follows:\n\n```python\nfrom elpv_dataset.utils import load_dataset\nimages, proba, types = load_dataset()\n```\n\n## Citing\n\nIf you use this dataset in scientific context, please cite the following\npublications:\n\n\u003e Buerhop-Lutz, C.; Deitsch, S.; Maier, A.; Gallwitz, F.; Berger, S.; Doll, B.; Hauch, J.; Camus, C. \u0026 Brabec, C. J. A Benchmark for Visual Identification of Defective Solar Cells in Electroluminescence Imagery. European PV Solar Energy Conference and Exhibition (EU PVSEC), 2018. DOI: [10.4229/35thEUPVSEC20182018-5CV.3.15](http://dx.doi.org/10.4229/35thEUPVSEC20182018-5CV.3.15)\n\n\u003e Deitsch, S., Buerhop-Lutz, C., Sovetkin, E., Steland, A., Maier, A., Gallwitz, F., \u0026 Riess, C. (2021). Segmentation of photovoltaic module cells in uncalibrated electroluminescence images. Machine Vision and Applications, 32(4). DOI: [10.1007/s00138-021-01191-9](https://doi.org/10.1007/s00138-021-01191-9)\n\n\u003e Deitsch, S.; Christlein, V.; Berger, S.; Buerhop-Lutz, C.; Maier, A.; Gallwitz, F. \u0026 Riess, C. Automatic classification of defective photovoltaic module cells in electroluminescence images. Solar Energy, Elsevier BV, 2019, 185, 455-468. DOI: [10.1016/j.solener.2019.02.067](http://dx.doi.org/10.1016/j.solener.2019.02.067)\n\nBibTeX details:\n\n\u003cdetails\u003e\n\n```bibtex\n\n@InProceedings{Buerhop2018,\n  author    = {Buerhop-Lutz, Claudia and Deitsch, Sergiu and Maier, Andreas and Gallwitz, Florian and Berger, Stephan and Doll, Bernd and Hauch, Jens and Camus, Christian and Brabec, Christoph J.},\n  title     = {A Benchmark for Visual Identification of Defective Solar Cells in Electroluminescence Imagery},\n  booktitle = {European PV Solar Energy Conference and Exhibition (EU PVSEC)},\n  year      = {2018},\n  eventdate = {2018-09-24/2018-09-28},\n  venue     = {Brussels, Belgium},\n  doi       = {10.4229/35thEUPVSEC20182018-5CV.3.15},\n}\n\n@Article{Deitsch2021,\n  author       = {Deitsch, Sergiu and Buerhop-Lutz, Claudia and Sovetkin, Evgenii and Steland, Ansgar and Maier, Andreas and Gallwitz, Florian and Riess, Christian},\n  date         = {2021},\n  journaltitle = {Machine Vision and Applications},\n  title        = {Segmentation of photovoltaic module cells in uncalibrated electroluminescence images},\n  doi          = {10.1007/s00138-021-01191-9},\n  issn         = {1432-1769},\n  number       = {4},\n  volume       = {32},\n}\n\n@Article{Deitsch2019,\n  author    = {Sergiu Deitsch and Vincent Christlein and Stephan Berger and Claudia Buerhop-Lutz and Andreas Maier and Florian Gallwitz and Christian Riess},\n  title     = {Automatic classification of defective photovoltaic module cells in electroluminescence images},\n  journal   = {Solar Energy},\n  year      = {2019},\n  volume    = {185},\n  pages     = {455--468},\n  month     = jun,\n  issn      = {0038-092X},\n  doi       = {10.1016/j.solener.2019.02.067},\n  publisher = {Elsevier {BV}},\n}\n```\n\u003c/details\u003e\n\n## License\n\n\u003ca rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\"\u003e\u003cimg alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" /\u003e\u003c/a\u003e\u003cbr /\u003eAll the images in this work are licensed under a \u003ca rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\"\u003eCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International License\u003c/a\u003e. Accompanying Python source code is distributed under the terms of the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0.html).\n\n\nFor commercial use, please contact us for further information.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzae-bayern%2Felpv-dataset","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fzae-bayern%2Felpv-dataset","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzae-bayern%2Felpv-dataset/lists"}