A NOVEL DATASET FOR VIETNAMESE NEW YEAR FOOD CLASSIFICATION - Duy Nguyen Vo; Van Tan Luu Ngo; Thi Phuong Vy Le; Nguyen Ngoc Huyen Van; Duc Anh Phuc Nguyen; Van Tuan Kiet Ngo; Thanh Thang Truong; Tan Tai Pham; Nhat Minh Dinh; Thai Ngoc Ho; Tan Tran Minh Khang Nguyen (Tạp chí Khoa học Đại học Đà Lạt, 2024)
Food classification has always piqued the interest of both domestic and international researchers, but numerous challenges remain. We present the dataset UIT-TASTET21, which contains over 77,000 color images of 18 traditional Vietnamese Lunar New Year dishes. We have experimented with classification using feature vectors from network architectures such as VGG16, Inception-v3, ResNet-50, Xception, and MobileNet-v2 to train support vector machines (SVMs), meeting the dataset’s challenges and laying the groundwork for the development of many optimal methods in the future that promise scientific breakthroughs in the service and commercial industries. At the same time, the authors desire to share a piece of Vietnamese cuisine’s distinctiveness with worldwide friends.