  Nutrition estimation is crucial for effective dietary management and overall
health and well-being. Existing methods often struggle with sub-optimal
accuracy and can be time-consuming. In this paper, we propose NuNet, a
transformer-based network designed for nutrition estimation that utilizes both
RGB and depth information from food images. We have designed and implemented a
multi-scale encoder and decoder, along with two types of feature fusion
modules, specialized for estimating five nutritional factors. These modules
effectively balance the efficiency and effectiveness of feature extraction with
flexible usage of our customized attention mechanisms and fusion strategies.
Our experimental study shows that NuNet outperforms its variants and existing
solutions significantly for nutrition estimation. It achieves an error rate of
15.65%, the lowest known to us, largely due to our multi-scale architecture and
fusion modules. This research holds practical values for dietary management
with huge potential for transnational research and deployment and could inspire
other applications involving multiple data types with varying degrees of
importance.
