  In response to the increasing demand for efficient and non-invasive methods
to estimate food weight, this paper presents a vision-based approach utilizing
2D images. The study employs a dataset of 2380 images comprising fourteen
different food types in various portions, orientations, and containers. The
proposed methodology integrates deep learning and computer vision techniques,
specifically employing Faster R-CNN for food detection and MobileNetV3 for
weight estimation. The detection model achieved a mean average precision (mAP)
of 83.41\%, an average Intersection over Union (IoU) of 91.82\%, and a
classification accuracy of 100\%. For weight estimation, the model demonstrated
a root mean squared error (RMSE) of 6.3204, a mean absolute percentage error
(MAPE) of 0.0640\%, and an R-squared value of 98.65\%. The study underscores
the potential applications of this technology in healthcare for nutrition
counseling, fitness and wellness for dietary intake assessment, and smart food
storage solutions to reduce waste. The results indicate that the combination of
Faster R-CNN and MobileNetV3 provides a robust framework for accurate food
weight estimation from 2D images, showcasing the synergy of computer vision and
deep learning in practical applications.
