<p>Computer vision can transform wildlife monitoring by automating phenotyping and individual identification. Achieving this, however, depends on access to large, well-curated image datasets that capture natural variation across individuals and years. Here, we present Melops, a longitudinal dataset comprising 24 578 images of 9 861 individual corkwing wrasse, <i>Symphodus melops</i>, collected over seven years through a capture–mark–recapture survey. Each fish was PIT-tagged for re-identification and photographed from both sides against a standardized white background with a colour reference. Alongside the images, we provide metadata including body length, sex, and reproductive state. To support deep learning applications, the dataset includes both the original photographs and automatically cropped images focusing on the whole fish or specific body regions. Together, these resources provide a foundation for developing computer vision methods for individual re-identification, colour pattern analysis, sex classification and other visual phenotyping tasks. Beyond this species, Melops can serve as a model for similar datasets in other taxa. Because it contains thousands of individuals with repeated observations, it provides a rare opportunity to explore temporally aware re-identification and phenotypic change in wild fish.</p>