Indonesian Food Image Classification with Grad-CAM Visualization using High Performance Computing DGX A100

In recent years, the importance of nutrition has gained significant attention in Indonesia, particularly with initiatives aimed at providing nutritious meals to children. Image classification technology offers a promising solution, potentially allowing individuals to quickly identify foods and estimate their nutritional value using just a food image. In this paper, we present an approach to classifying Indonesian food images using convolutional neural network, with a focus on increasing model interpretability using visualization techniques. We used the EfficientNetV2 architecture, taking advantage of its state-of-the-art performance and computational efficiency. Our dataset consisted of 2,000 images, categorized into ten distinct classes which were pre-processed and utilized to fine-tune the pre-trained EfficientNetV2 model. Training was carried out using the NVIDIA DGX A100 machine, significantly reducing the training time. To improve the transparency of the classification process, we used Gradient-weighted Class Activation Mapping (Grad-CAM) to identify the regions of interest in the images that had the greatest influence on the model’s predictions. This helped us better understand the model’s decision-making process and suggest areas for improvement. Our experiments achieved an accuracy of 87.25% within 50 epochs, demonstrating the model’s capability to correctly classify Indonesian food images. We also discovered areas of misclassification and used Grad-CAM visualizations to determine the causes, which will guide future dataset and model enhancements.