PithaNet: A transfer learning-based approach for traditional pitha classification

Abstract

<span lang="EN-US">Pitha, pithe, or peetha are all Bangla words referring to a native and traditional food of Bangladesh as well as some areas of India, especially the parts of India where Bangla is the primary language. Numerous types of pithas exist in the culture and heritage of the Bengali and Bangladeshi people. Pithas are traditionally prepared and offered on important occasions in Bangladesh, such as welcoming a bride grooms, or bride, entertaining guests, or planning a special gathering of family, relatives, or friends. The traditional pitha celebration and pitha culture are no longer widely practiced in modern civilization. Consequently, the younger generation is unfamiliar with our traditional pitha culture. In this study, an effective pitha image classification system is introduced. convolutional neural network (CNN) pre-trained models EfficientNetB6, ResNet50, and VGG16 are used to classify the images of pitha. The dataset of traditional popular pithas is collected from different parts of Bangladesh. In this experiment, EfficientNetB6 and ResNet50 show nearly 90% accuracy. The best classification result was obtained using VGG16 with 92% accuracy. The main motive of this study is to revive the Bengali pitha tradition among young people and people worldwide, which will encourage many other researchers to pursue research in this domain.</span>

Image classification-based transfer learning framework for image detection of IoT devices

“…The studies in [19]- [22] also apply CNN-based TL techniques to identify leukocytes as well as red blood cells for blood-related diseases and classify fundus for general retinal diseases diagnosis, respectively. The TL with pre-trained CNN models are also used for non-medical applications including Thai culture and Pitha traditional food images classification [23], [24], and other applications including land cover, fabric defect, birds' species, distracted driver classifications as well as age-invariant face recognition [25]- [29]. All the above literature studies show that the TL approach can achieve better accuracy performance than those of non-TL methods for various considered datasets and scenarios.…”