{"id":"https://openalex.org/W4390978746","doi":"https://doi.org/10.1145/3630138.3630418","title":"Garment Fabric Pattern Classification via ResNet-34","display_name":"Garment Fabric Pattern Classification via ResNet-34","publication_year":2023,"publication_date":"2023-09-24","ids":{"openalex":"https://openalex.org/W4390978746","doi":"https://doi.org/10.1145/3630138.3630418"},"language":"en","primary_location":{"id":"doi:10.1145/3630138.3630418","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3630138.3630418","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3630138.3630418","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Conference on Power Communication Computing and Networking Technologies","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3630138.3630418","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100354421","display_name":"Zengmin Geng","orcid":"https://orcid.org/0000-0003-2975-5266"},"institutions":[{"id":"https://openalex.org/I102882674","display_name":"Beijing Institute of Fashion Technology","ror":"https://ror.org/03hgxtg28","country_code":"CN","type":"education","lineage":["https://openalex.org/I102882674"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zengmin Geng","raw_affiliation_strings":["School of Arts and Science, Beijing Institute of Fashion Technology, China"],"raw_orcid":"https://orcid.org/0000-0003-2975-5266","affiliations":[{"raw_affiliation_string":"School of Arts and Science, Beijing Institute of Fashion Technology, China","institution_ids":["https://openalex.org/I102882674"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044549798","display_name":"Bijun Lin","orcid":"https://orcid.org/0009-0002-9695-2446"},"institutions":[{"id":"https://openalex.org/I102882674","display_name":"Beijing Institute of Fashion Technology","ror":"https://ror.org/03hgxtg28","country_code":"CN","type":"education","lineage":["https://openalex.org/I102882674"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bijun Lin","raw_affiliation_strings":["Information Center, Beijing Institute of Fashion Technology, China"],"raw_orcid":"https://orcid.org/0009-0002-9695-2446","affiliations":[{"raw_affiliation_string":"Information Center, Beijing Institute of Fashion Technology, China","institution_ids":["https://openalex.org/I102882674"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055900537","display_name":"Ye Yuan","orcid":"https://orcid.org/0009-0006-4839-846X"},"institutions":[{"id":"https://openalex.org/I102882674","display_name":"Beijing Institute of Fashion Technology","ror":"https://ror.org/03hgxtg28","country_code":"CN","type":"education","lineage":["https://openalex.org/I102882674"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ye Yuan","raw_affiliation_strings":["School of Arts and Science, Beijing Institute of Fashion Technology, China"],"raw_orcid":"https://orcid.org/0009-0006-4839-846X","affiliations":[{"raw_affiliation_string":"School of Arts and Science, Beijing Institute of Fashion Technology, China","institution_ids":["https://openalex.org/I102882674"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102844547","display_name":"Shiyu Liu","orcid":"https://orcid.org/0009-0008-0817-3041"},"institutions":[{"id":"https://openalex.org/I102882674","display_name":"Beijing Institute of Fashion Technology","ror":"https://ror.org/03hgxtg28","country_code":"CN","type":"education","lineage":["https://openalex.org/I102882674"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shiyu Liu","raw_affiliation_strings":["Information Center, Beijing Institute of Fashion Technology, China"],"raw_orcid":"https://orcid.org/0009-0008-0817-3041","affiliations":[{"raw_affiliation_string":"Information Center, Beijing Institute of Fashion Technology, China","institution_ids":["https://openalex.org/I102882674"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044987843","display_name":"Dandan Gao","orcid":"https://orcid.org/0009-0006-0542-4795"},"institutions":[{"id":"https://openalex.org/I102882674","display_name":"Beijing Institute of Fashion Technology","ror":"https://ror.org/03hgxtg28","country_code":"CN","type":"education","lineage":["https://openalex.org/I102882674"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dandan Gao","raw_affiliation_strings":["Museum of Ethnic Costumes, Beijing Institute of Fashion Technology, China"],"raw_orcid":"https://orcid.org/0009-0006-0542-4795","affiliations":[{"raw_affiliation_string":"Museum of Ethnic Costumes, Beijing Institute of Fashion Technology, China","institution_ids":["https://openalex.org/I102882674"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070447726","display_name":"Jianxia Du","orcid":"https://orcid.org/0009-0007-6480-3864"},"institutions":[{"id":"https://openalex.org/I102882674","display_name":"Beijing Institute of Fashion Technology","ror":"https://ror.org/03hgxtg28","country_code":"CN","type":"education","lineage":["https://openalex.org/I102882674"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianxia Du","raw_affiliation_strings":["School of Fashion Art and Engineering, Beijing Institute of Fashion Technology, China"],"raw_orcid":"https://orcid.org/0009-0007-6480-3864","affiliations":[{"raw_affiliation_string":"School of Fashion Art and Engineering, Beijing Institute of Fashion Technology, China","institution_ids":["https://openalex.org/I102882674"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I102882674"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.35791663,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11595","display_name":"Textile materials and evaluations","score":0.9782999753952026,"subfield":{"id":"https://openalex.org/subfields/2507","display_name":"Polymers and Plastics"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11666","display_name":"Color Science and Applications","score":0.9584000110626221,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/backbone-network","display_name":"Backbone network","score":0.7915672063827515},{"id":"https://openalex.org/keywords/residual-neural-network","display_name":"Residual neural network","score":0.7321175932884216},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7086382508277893},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6521743535995483},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5810147523880005},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5586667656898499},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5214633345603943},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.47628918290138245},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.4141686260700226},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3221980929374695},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3145064115524292},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.2792642116546631},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.23081442713737488},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.06158551573753357}],"concepts":[{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.7915672063827515},{"id":"https://openalex.org/C2944601119","wikidata":"https://www.wikidata.org/wiki/Q43744058","display_name":"Residual neural network","level":3,"score":0.7321175932884216},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7086382508277893},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6521743535995483},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5810147523880005},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5586667656898499},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5214633345603943},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.47628918290138245},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.4141686260700226},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3221980929374695},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3145064115524292},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2792642116546631},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.23081442713737488},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.06158551573753357},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3630138.3630418","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3630138.3630418","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3630138.3630418","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Conference on Power Communication Computing and Networking Technologies","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3630138.3630418","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3630138.3630418","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3630138.3630418","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Conference on Power Communication Computing and Networking Technologies","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4390978746.pdf","grobid_xml":"https://content.openalex.org/works/W4390978746.grobid-xml"},"referenced_works_count":3,"referenced_works":["https://openalex.org/W1981276685","https://openalex.org/W2755964124","https://openalex.org/W3035316043"],"related_works":["https://openalex.org/W4312121297","https://openalex.org/W4310471641","https://openalex.org/W4401359364","https://openalex.org/W2995001409","https://openalex.org/W4396918490","https://openalex.org/W4402230879","https://openalex.org/W4313560195","https://openalex.org/W4360783045","https://openalex.org/W4212887358","https://openalex.org/W4387829094"],"abstract_inverted_index":{"This":[0,107],"article":[1],"presents":[2],"a":[3,56,60,123,131,182],"novel":[4],"automatic":[5,242],"classification":[6,66,86,148,169,188,228],"method":[7],"for":[8,63,185,214],"garment":[9,23,46,103,166,190,215,243],"fabric":[10,24,47,104,167,191,216,244],"pattern":[11,105,168,217,245],"images":[12],"using":[13],"the":[14,75,79,85,93,97,110,144,161,173,176,187,199,206,210],"vanilla":[15],"Reset.":[16],"The":[17,50,81,126,137],"study":[18,204],"begins":[19],"by":[20,113,122,143,151],"collecting":[21],"industry-standard":[22],"images,":[25],"which":[26],"are":[27,71],"further":[28,239],"subjected":[29],"to":[30,43,73,154,194,237],"preprocessing":[31],"techniques":[32],"such":[33],"as":[34,92,130,220],"cropping,":[35],"rotation,":[36],"and":[37,59,77,120,196,225],"contrast":[38],"enhancement.":[39],"These":[40,158],"steps":[41],"contribute":[42],"an":[44],"expanded":[45],"image":[48,65,218],"dataset.":[49],"dataset":[51],"is":[52,134,141],"then":[53],"divided":[54],"into":[55],"validation":[57],"set":[58,62],"training":[61],"conducting":[64],"experiments.":[67],"Different":[68],"ResNet":[69,177],"frameworks":[70],"employed":[72],"analyze":[74],"datasets":[76],"compare":[78],"results.":[80],"findings":[82,236],"demonstrate":[83],"that":[84],"model":[87],"based":[88],"on":[89],"ResNet-34,":[90],"serving":[91],"backbone":[94,115,132,156,212],"network,":[95],"achieves":[96,226],"highest":[98],"accuracy":[99,111,149,197],"of":[100,128,163,175,189,208],"91.8%":[101],"in":[102,147,165,198,241],"classification.":[106,246],"performance":[108],"surpasses":[109],"achieved":[112,150],"alternative":[114],"networks,":[116],"namely":[117],"AlexNet,":[118],"VGG16,":[119],"GoogleNet,":[121],"substantial":[124],"margin.":[125],"superiority":[127],"ResNet-34":[129,152,164,211],"network":[133,213],"thus":[135],"affirmed.":[136],"proposed":[138],"method's":[139],"effectiveness":[140],"validated":[142],"significant":[145],"improvement":[146],"compared":[153],"other":[155],"networks.":[157],"results":[159],"highlight":[160],"potential":[162],"tasks.":[170],"By":[171],"leveraging":[172],"strengths":[174],"architecture,":[178],"our":[179],"approach":[180],"offers":[181],"promising":[183],"solution":[184],"automating":[186],"patterns,":[192],"contributing":[193],"efficiency":[195],"fashion":[200],"industry.":[201],"Overall,":[202],"this":[203],"establishes":[205],"value":[207],"employing":[209],"classification,":[219],"it":[221],"outperforms":[222],"competing":[223],"networks":[224],"remarkable":[227],"accuracy.":[229],"Future":[230],"research":[231],"can":[232],"build":[233],"upon":[234],"these":[235],"explore":[238],"advancements":[240]},"counts_by_year":[],"updated_date":"2026-07-31T08:31:51.225901","created_date":"2025-10-10T00:00:00"}
