{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/nu-resnet-deep-residual-networks-for-thai","title":"NU-ResNet: Deep Residual Networks for Thai Food Image Recognition","arxiv_id":null,"date":"2018-08-01","proceeding":null,"authors":["Chakkrit Termritthikun","Surachet Kanprachar"],"abstract":"To improve    the    recognition    accuracy    of    a convolutional neural network, the number of the modules inside the  network  is  normally  increased  so  that  the  whole  network becomes  a  deeper  network.  By  doing  such,  it  does  not  always guarantee  that  the  accuracy  will  be  improved.  In  addition, adding  more  modules  to  the  network,  the  required  parameter size  and  processing  time  are  certainly  increased.  These  then result in a significant drawback if such network is utilized in a smartphone  in  which  the  computational  resources  are  limited. In this paper, another technique called Identity mapping, which is  from  the  Residual  networks,  is  adopted  and  added  to  the network. This technique is applied to the Deep NU-InNet with a depth  of  4,  8,  and  12  in  order  to  increase  the  recognition accuracy while the depth is kept constant. Testing this proposed network; that is, NU-ResNet, with THFOOD-50 dataset, which contains   various   images   of   50   Thai   famous   dishes,   the improvement  in  terms  of  the  recognition  accuracy  is  obtained. With a depth of 4 for NU-ResNet, the achieved Top-1 accuracy and Top-5 accuracy are 83.07% and 97.04%, respectively. The parameter  size  of  the  network  is  only  1.48×106,  which  is  quite small for being used with a smartphone application. Moreover, the average processing time per image is 44.60 ms, which can be practically  used  in  an  image  recognition  application.  These results show a promising performance of the proposed network to be  used with a Thai food image recognition application in  a smartphone.","url_abs":"https://journal.utem.edu.my/index.php/jtec/article/view/3572","url_pdf":"http://journal.utem.edu.my/index.php/jtec/article/viewFile/3572/2467","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"nu-resnet-deep-residual-networks-for-thai","repo_url":"https://github.com/chakkritte/NU-ResNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}