  In contemporary society, the application of artificial intelligence for
automatic food recognition offers substantial potential for nutrition tracking,
reducing food waste, and enhancing productivity in food production and
consumption scenarios. Modern technologies such as Computer Vision and Deep
Learning are highly beneficial, enabling machines to learn automatically,
thereby facilitating automatic visual recognition. Despite some research in
this field, the challenge of achieving accurate automatic food recognition
quickly remains a significant research gap. Some models have been developed and
implemented, but maintaining high performance swiftly, with low computational
cost and low access to expensive hardware accelerators, still needs further
exploration and research. This study employs the pretrained MobileNetV2 model,
which is efficient and fast, for food recognition on the public Food11 dataset,
comprising 16643 images. It also utilizes various techniques such as dataset
understanding, transfer learning, data augmentation, regularization, dynamic
learning rate, hyperparameter tuning, and consideration of images in different
sizes to enhance performance and robustness. These techniques aid in choosing
appropriate metrics, achieving better performance, avoiding overfitting and
accuracy fluctuations, speeding up the model, and increasing the generalization
of findings, making the study and its results applicable to practical
applications. Despite employing a light model with a simpler structure and
fewer trainable parameters compared to some deep and dense models in the deep
learning area, it achieved commendable accuracy in a short time. This
underscores the potential for practical implementation, which is the main
intention of this study.
