  The escalating global concern over extensive food wastage necessitates
innovative solutions to foster a net-zero lifestyle and reduce emissions. The
LILA home composter presents a convenient means of recycling kitchen scraps and
daily food waste into nutrient-rich, high-quality compost. To capture the
nutritional information of the produced compost, we have created and annotated
a large high-resolution image dataset of kitchen food waste with segmentation
masks of 19 nutrition-rich categories. Leveraging this dataset, we benchmarked
four state-of-the-art semantic segmentation models on food waste segmentation,
contributing to the assessment of compost quality of Nitrogen, Phosphorus, or
Potassium. The experiments demonstrate promising results of using segmentation
models to discern food waste produced in our daily lives. Based on the
experiments, SegFormer, utilizing MIT-B5 backbone, yields the best performance
with a mean Intersection over Union (mIoU) of 67.09. Class-based results are
also provided to facilitate further analysis of different food waste classes.
