02542nas a2200373   4500000000100000008004100001260005900042653002100101653002200122653002300144653001600167653003600183653002000219653003400239653001800273653001900291653002200310653003300332653003400365653001800399653002500417653002100442653001200463653001900475653001700494100001300511700001300524700001300537700000900550245005800559856015400617520137200771020002502143                                       d  bInstitute of Electrical and Electronics Engineers Inc.10aBaseline results10aBenchmark dataset10aBenchmark datasets10aBlue calico10aClassification (of information)10aComputer Vision10aConvolutional neural networks10aDeep learning10aImage datasets10aImage recognition10aIntangible cultural heritage10aIntangible cultural heritages10aLarge dataset10aLarge-scale datasets10aLearning systems10aNantong10aPublic dataset10aPublic image1 aXiang Yu1 aLi Zhang1 aMei Shen1 aIEEE00aNantong Blue Calico Image Dataset and Its Recognition  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85138719484&doi=10.1109%2fCEC55065.2022.9870225&partnerID=40&md5=8952d911d139094354d4ac20d84c20933 aNantong blue calico is a kind of important intangible cultural heritages in China. To better safeguard and inherit it in a digital way, it is necessary to construct a large-scale dataset for Nantong blue calico. As so far, however, we could not find a public dataset for blue calico. The goal of this paper is to give a public image dataset which named N tBC consisting of Nantong blue calico patterns and provide a baseline result for the recognition of Nantong blue calico patterns. In this paper, we perform several baseline experiments on the NtBC dataset, including handcrafted and deep feature based classification methods. we compare some handcrafted methods and four kinds of popular convolutional neural networks (CNNs), including ResNet-50, AlexNet, GoogLeNet-V1 and VGGNet-16. Experimental results show that ResNet-50 yields an accuracy of 93.8\% in the recognition performance, which shows that it is efficient to classify blue calico patterns through deep learning methods. As a consequence, this result provides the current best baseline result for Nantong blue calico image recognition. We believe our N tBC will facilitate future research on Chinese traditional patterns development, fine grained visual classification, and imbalanced learning fields. We make the dataset and pre-trained models publicly available at https://github.com/facebook/react.  a9781665467087 (ISBN)