• Adesh Avhad
• Shashank Ahire
• Tushar Ambekar
• Eknath Avhad
• Shashikant Deshmukh

This paper introduces a method for predicting the caloric content of Indian food items depicted in images using convolutional neural networks (CNNs). Leveraging a dataset of Indian food images, the proposed approach employs data augmentation, normalization, and a simple CNN architecture to train a model capable of predicting the caloric content of various dishes. Through extensive experimentation and evaluation, the model demonstrates promising accuracy in caloric prediction, laying the groundwork for automated dietary monitoring systems. The findings suggest the potential of CNN-based approaches in addressing challenges related to dietary assessment and nutritional tracking, particularly in culturally diverse culinary contexts like Indian cuisine.

Food Image Analysis, Caloric Content Prediction ,Machine Learning ,Image Classification ,Indian Cuisine insert

"Food Image Recognition & Calories Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.g610-g613, May-2024, Available :http://www.jetir.org/papers/JETIR2405675.pdf

"Food Image Recognition & Calories Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppg610-g613, May-2024, Available at : http://www.jetir.org/papers/JETIR2405675.pdf