A comparison of semantic segmentation performance across five networks, with the top results highlighted in bold.
<p>A comparison of semantic segmentation performance across five networks, with the top results highlighted in bold.</p>
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2025
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| _version_ | 1852014772688519168 |
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| author | Jiajun Liu (692586) |
| author2 | Bei Zhou (5219792) Jie Liu (15128) Xike Zhang (2848757) Jiangshu Wei (22634246) Yao Zhang (134381) Junjie Wu (393541) Changping Wu (52884) Di Hu (12438) |
| author2_role | author author author author author author author author |
| author_facet | Jiajun Liu (692586) Bei Zhou (5219792) Jie Liu (15128) Xike Zhang (2848757) Jiangshu Wei (22634246) Yao Zhang (134381) Junjie Wu (393541) Changping Wu (52884) Di Hu (12438) |
| author_role | author |
| dc.creator.none.fl_str_mv | Jiajun Liu (692586) Bei Zhou (5219792) Jie Liu (15128) Xike Zhang (2848757) Jiangshu Wei (22634246) Yao Zhang (134381) Junjie Wu (393541) Changping Wu (52884) Di Hu (12438) |
| dc.date.none.fl_str_mv | 2025-11-17T18:42:05Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0336622.t004 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/A_comparison_of_semantic_segmentation_performance_across_five_networks_with_the_top_results_highlighted_in_bold_/30642209 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified throughput plant phenotyping plant phenotypic traits made publicly available based spatial clustering semantic segmentation tasks instance segmentation task div >< p canola silique segmentation counting </ p segmentation accuracy precision segmentation canola siliques counting accuracy automatic counting study proposes precision agriculture model complexity method achieves excellent balance efficient solution crop breeding contrastive learning baseline models achieves 94 accurate analysis 72m parameters 6432 /</ |
| dc.title.none.fl_str_mv | A comparison of semantic segmentation performance across five networks, with the top results highlighted in bold. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>A comparison of semantic segmentation performance across five networks, with the top results highlighted in bold.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_7443d4e6bfecbccc470f1d050dbbd5cd |
| identifier_str_mv | 10.1371/journal.pone.0336622.t004 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30642209 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A comparison of semantic segmentation performance across five networks, with the top results highlighted in bold.Jiajun Liu (692586)Bei Zhou (5219792)Jie Liu (15128)Xike Zhang (2848757)Jiangshu Wei (22634246)Yao Zhang (134381)Junjie Wu (393541)Changping Wu (52884)Di Hu (12438)Space ScienceBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedthroughput plant phenotypingplant phenotypic traitsmade publicly availablebased spatial clusteringsemantic segmentation tasksinstance segmentation taskdiv >< pcanola silique segmentationcounting </ psegmentation accuracyprecision segmentationcanola siliquescounting accuracyautomatic countingstudy proposesprecision agriculturemodel complexitymethod achievesexcellent balanceefficient solutioncrop breedingcontrastive learningbaseline modelsachieves 94accurate analysis72m parameters6432 /</<p>A comparison of semantic segmentation performance across five networks, with the top results highlighted in bold.</p>2025-11-17T18:42:05ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0336622.t004https://figshare.com/articles/dataset/A_comparison_of_semantic_segmentation_performance_across_five_networks_with_the_top_results_highlighted_in_bold_/30642209CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/306422092025-11-17T18:42:05Z |
| spellingShingle | A comparison of semantic segmentation performance across five networks, with the top results highlighted in bold. Jiajun Liu (692586) Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified throughput plant phenotyping plant phenotypic traits made publicly available based spatial clustering semantic segmentation tasks instance segmentation task div >< p canola silique segmentation counting </ p segmentation accuracy precision segmentation canola siliques counting accuracy automatic counting study proposes precision agriculture model complexity method achieves excellent balance efficient solution crop breeding contrastive learning baseline models achieves 94 accurate analysis 72m parameters 6432 /</ |
| status_str | publishedVersion |
| title | A comparison of semantic segmentation performance across five networks, with the top results highlighted in bold. |
| title_full | A comparison of semantic segmentation performance across five networks, with the top results highlighted in bold. |
| title_fullStr | A comparison of semantic segmentation performance across five networks, with the top results highlighted in bold. |
| title_full_unstemmed | A comparison of semantic segmentation performance across five networks, with the top results highlighted in bold. |
| title_short | A comparison of semantic segmentation performance across five networks, with the top results highlighted in bold. |
| title_sort | A comparison of semantic segmentation performance across five networks, with the top results highlighted in bold. |
| topic | Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified throughput plant phenotyping plant phenotypic traits made publicly available based spatial clustering semantic segmentation tasks instance segmentation task div >< p canola silique segmentation counting </ p segmentation accuracy precision segmentation canola siliques counting accuracy automatic counting study proposes precision agriculture model complexity method achieves excellent balance efficient solution crop breeding contrastive learning baseline models achieves 94 accurate analysis 72m parameters 6432 /</ |