The study of fatigue fracture surfaces presents a perfect opportunity for machine learning (ML) and computer vision (CV) techniques. This work compiles a multimodal set of images taken on the fractured surfaces of fatigue crack growth experiments on Ti-6Al-4V compact tension specimens. Secondary electron (SE), back scattered electron (BSE), and scanning white-light interferometry (SWLI) images were taken on the samples. A set of 102 SE images was taken from near the crack initiation site up to 14mm from the initiation site in regular 1mm increments. The other set of SE images, in addition to the BSE and SWLI, were from large area scans that were stitched together but can be cropped for future ML usage. Labeled data sets such as this can improve the development of ML tools for fractography studies.