DeshiFoodBD

Traditional Bangladeshi food image classification has become immensely relevant for a variety of reasons, including restaurant selection, travel destination selection, dietary caloric intake, and cultural awareness. However, this is highly challenging to design an effective and useable traditional labelled (English and Bengali) food dataset of Bangladesh for the research purpose. The ‘DeshiFoodBD’ dataset is presented in this article for traditional Bangladeshi food classification purposes. The food images come from two different sources: 1) web scraping and 2) camera (digital, smartphone). The dataset contains 5425-labelled images of 19 famous Bangladeshi foods such as biriyani, kalavuna, roshgolla, hilsha fish, nehari, and so on. The dataset can be used with a variety of CNN architectures, including ResNet50, YOLO, VGG-16, R-CNN, and DPM.

传统孟加拉国美食图像分类因诸多原因而显得尤为关键，其中包括餐厅选择、旅行目的地挑选、饮食卡路里摄入以及文化认知等方面。然而，为研究目的设计一个有效且实用的孟加拉国传统标记（英语和孟加拉语）的美食数据集极具挑战性。本文提出‘DeshiFoodBD’数据集，旨在用于传统孟加拉国美食分类。该数据集的美食图像来源于两个不同的渠道：1）网络爬取和2）相机（数字、智能手机）。数据集包含5425张标记的孟加拉国19种著名美食图像，如烩饭、卡劳纳、罗什古拉、海鲈鱼、内哈里等。该数据集适用于多种卷积神经网络架构，包括ResNet50、YOLO、VGG-16、R-CNN和DPM。