# Pixel-level Annotation

FoodSeg103 is a dataset containing 7,118 food images, annotated with 104 ingredient categories, with an average of 6 ingredient labels and pixel-level masks per image.

A semantic segmentation model based on the Segformer architecture, used to assign semantic category labels to each pixel in an image.

UperNet is a framework for semantic segmentation that uses Swin Transformer as the backbone network, enabling efficient pixel-level semantic annotation.

UperNet is a framework for semantic segmentation, utilizing ConvNeXt as the backbone network, capable of predicting semantic labels for each pixel.

UperNet is a framework for semantic segmentation that uses ConvNeXt as the backbone network and can predict semantic labels for each pixel.