Objective assessment of melasma is a critical indicator for evaluating treatment efficacy. However, current clinical practice primarily relies on subjective physician judgment for assessment, which results in significant inter-rater variability. Establishing a reliable automated segmentation method for melasma is essential to achieve precise assessment, and this requires support from high-quality datasets. The Melasma Image Dataset (MEMI-DS) was constructed to address this limitation, comprising 716 images with corresponding annotation files, for training melasma segmentation models. To validate the effectiveness of the dataset, 10 segmentation models (including UNet, UNet++, DeepLabV3, HRNet, DeepLabV3+, LinkNet, MANet, PAN, TransUet, and SegFormer) were systematically evaluated. Experimental results demonstrated that MEMI-DS can effectively distinguish the segmentation performance of different models. Notably, the segmentation performance of models represented by SegFormer was significantly improved by applying data augmentation techniques. These findings not only confirm the effectiveness and practicality of the dataset but also provide reliable technical support for the objective assessment of melasma.