Bangladeshi Dessert Identification Using Deep Learning Approach

Abstract

In the area of culinary culture, the classification of desserts provides a fascinating problem, particularly due to the complicated nature and vast assortment of dessert styles found throughout many regions. This work offers a rigorously curated dataset of dessert photographs, specifically optimized for Bangladeshi dessert classification. Our dataset features a comprehensive range of high-quality photos showing the lively diversity of traditional Bangladeshi sweets, showcasing the richness and complexity of the country's culinary heritage. The project intends to construct strong dessert recognition models by leveraging multiple visual processing techniques and deep learning algorithms, including MobileNet. Through comprehensive examination utilizing established parameters, our models achieve an astonishing 98% overall test accuracy. This work offers as a significant resource for scholars and practitioners digging into culinary picture classification, with a special focus on Bangladeshi desserts. By offering access to such a rich dataset, I intend to drive improvements in machine learning techniques targeted to culinary applications, thereby contributing to the preservation of culinary legacy and the promotion of cultural diversity via the lens of technology.