{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T15:35:52Z","timestamp":1772724952875,"version":"3.50.1"},"reference-count":87,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004508","name":"College of Computing, Prince of Songkla University","doi-asserted-by":"publisher","award":["COC6604007S"],"award-info":[{"award-number":["COC6604007S"]}],"id":[{"id":"10.13039\/501100004508","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004508","name":"College of Computing, Prince of Songkla University","doi-asserted-by":"publisher","award":["COC6604032S"],"award-info":[{"award-number":["COC6604032S"]}],"id":[{"id":"10.13039\/501100004508","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3503672","type":"journal-article","created":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T19:06:14Z","timestamp":1732129574000},"page":"174204-174221","source":"Crossref","is-referenced-by-count":7,"title":["Thai Food Recognition Using Deep Learning With Cyclical Learning Rates"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7594-8061","authenticated-orcid":false,"given":"Nawanol","family":"Theera-Ampornpunt","sequence":"first","affiliation":[{"name":"College of Computing, Prince of Songkla University, Phuket, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5370-6387","authenticated-orcid":false,"given":"Panisa","family":"Treepong","sequence":"additional","affiliation":[{"name":"College of Computing, Prince of Songkla University, Phuket, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00277"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00088"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.112040"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00774"},{"key":"ref5","first-page":"1","article-title":"Battle of the backbones: A large-scale comparison of pretrained models across computer vision tasks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"36","author":"Goldblum"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3329168"},{"key":"ref7","article-title":"An image is worth 16\u00d716 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020","journal-title":"arXiv:2010.11929"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2017.58"},{"issue":"2","key":"ref9","first-page":"63","article-title":"NU-InNet: Thai food image recognition using convolutional neural networks on smartphone","volume":"9","author":"Termritthikun","year":"2017","journal-title":"J. Telecommun., Electron. Comput. Eng. (JTEC)"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3237871"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3414031"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3343031.3350948"},{"key":"ref13","article-title":"ChineseFoodNet: A large-scale image dataset for Chinese food recognition","author":"Chen","year":"2017","journal-title":"arXiv:1705.02743"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.66"},{"key":"ref15","article-title":"FoodX-251: A dataset for fine-grained food classification","author":"Kaur","year":"2019","journal-title":"arXiv:1907.06167"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10599-4_29"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.2478\/ausi-2018-0002"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-16199-0_1"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2019.03.011"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2021.107686"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-70742-6_36"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICME.2012.157"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2016.07.006"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2009.5413511"},{"key":"ref25","article-title":"Mining discriminative food regions for accurate food recognition","author":"Qiu","year":"2022","journal-title":"arXiv:2207.03692"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-16199-0_41"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-23222-5_54"},{"key":"ref28","article-title":"Computer vision-based food calorie estimation: Dataset, method, and experiment","author":"Liang","year":"2017","journal-title":"arXiv:1705.07632"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2013.5"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2015.2419251"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2016.2636441"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-68548-9_20"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104972"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330734"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2018.00068"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/s44230-023-00057-9"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2016.2642792"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00182"},{"issue":"7","key":"ref39","doi-asserted-by":"crossref","first-page":"1180","DOI":"10.1017\/S136898001400007X","article-title":"Image-based food portion size estimation using a smartphone without a fiducial marker","volume":"22","author":"Yang","year":"2019","journal-title":"Public Health Nutrition"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW63382.2024.00378"},{"key":"ref41","first-page":"369","article-title":"Super-convergence: Very fast training of neural networks using large learning rates","volume-title":"Proc. SPIE","volume":"11006","author":"Smith"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(98)00116-6"},{"issue":"3","key":"ref43","first-page":"543","article-title":"A method for unconstrained convex minimization problem with the rate of convergence O (1\/k2)","volume":"269","author":"Nesterov","year":"1983","journal-title":"Dokl. Akad. Nauk. SSSR"},{"issue":"7","key":"ref44","first-page":"2121","article-title":"Adaptive subgradient methods for online learning and stochastic optimization","volume":"12","author":"Duchi","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref45","volume-title":"Neural networks for machine learning lecture 6a overview of mini-batch gradient descent","author":"Hinton","year":"2012"},{"key":"ref46","article-title":"ADADELTA: An adaptive learning rate method","author":"Zeiler","year":"2012","journal-title":"arXiv:1212.5701"},{"key":"ref47","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"arXiv:1412.6980"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref49","first-page":"10096","article-title":"EfficientNetv2: Smaller models and faster training","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Tan"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref52","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"Howard","year":"2017","journal-title":"arXiv:1704.04861"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00140"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-73661-2_5"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00907"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01044"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01170"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref63","article-title":"A disciplined approach to neural network hyper-parameters: Part 1\u2013learning rate, batch size, momentum, and weight decay","author":"Smith","year":"2018","journal-title":"arXiv:1803.09820"},{"key":"ref64","volume-title":"Cyclical Learning Rate (CLR)","author":"Kenstler","year":"2024"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/ICMEW.2015.7169816"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.146"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-39601-9_4"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.325"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052663"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00432"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00571"},{"key":"ref72","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Tan"},{"key":"ref73","article-title":"MixConv: Mixed depthwise convolutional kernels","author":"Tan","year":"2019","journal-title":"arXiv:1907.09595"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.5555\/3495724.3497510"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525087"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00945"},{"key":"ref77","article-title":"Learning with feature-dependent label noise: A progressive approach","author":"Zhang","year":"2021","journal-title":"arXiv:2103.07756"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02305"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/TAFE.2024.3386713"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654869"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1145\/2986035.2986042"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2016.7900117"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2018.09.001"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2991810"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-68821-9_47"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfoodeng.2024.112134"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.18280\/ts.400335"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10380310\/10759670.pdf?arnumber=10759670","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T05:46:40Z","timestamp":1732772800000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10759670\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":87,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3503672","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}