FOOD CLASSIFICATION AND CALORIES PREDICTION USING FLASK FRAME WORK
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In recent years, the demand for personalized dietary management has increased significantly. This paper proposes a Flask – based framework that integrates machine learning techniques for food classification and calorie prediction. The system consists of three key modules: food image classification, calorie estimation and personalized adjustment based on user anthropometric data such as height and weight. A convolutional neural network (CNN) with transfer learning is employed to accurately identify food items from images. The calorie prediction module utilizes regression models to estimate calorie values based on food type and portion size. Furthermore, user-specific parameters are incorporated to improve prediction accuracy and relevance. The flask framework provides an interactive interface for users to upload images and receive personalized results. Experimental results demonstrate that the proposed system achieves reliable performance in food classification and calorie estimation. This approach offers an effective solution for prompting healthy eating habits through personalized nutrition management.
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Kumar, M. P. (2026). Food Classification and Calories Prediction using Flask Frame Work. International Journal of Science, Strategic Management and Technology, 02(05). https://doi.org/10.55041/ijsmt.v2i5.048
Kumar, M.. "Food Classification and Calories Prediction using Flask Frame Work." International Journal of Science, Strategic Management and Technology, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i5.048.
Kumar, M.. "Food Classification and Calories Prediction using Flask Frame Work." International Journal of Science, Strategic Management and Technology 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i5.048.
References
2.Karen Simonyan and Andrew Zisserman,“Very Deep Convolutional Networks for Large-Scale ImageRecognition,”
3.TensorFlow,“TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems,”(Used for model training and implementation)
4.Keras Documentation,https://keras.io◻ (Used for building CNN model easily)
5.Flask Documentation, https://flask.palletsprojects.com◻(Used for deploying the web application)
6.USDA FoodData Central, https://fdc.nal.usda.gov◻ (Used for calorie dataset reference) Kaggle,“Food Image Dataset,” https://www.kaggle.com◻ (Used for training dataset).
7.Meyers, A., Johnston, N., Rathod, V., et al., “Im2Calories: Towards an Automated Mobile Vision Food Diary,” IEEE International Conference on Computer Vision, 2015. (Used for calorie prediction concept) Bossard, L., Guillaumin, M., and Van Gool, L.,“Food-101 – Mining Discriminative Components withRandom Forests,” European Conference on Computer Vision, 2014.(Used for food classification dataset reference)Pedregosa, F., et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, 2011.(Used for preprocessing and evaluation)
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