Duke PAM is a photoacoustic microscopy (PAM) dataset collected at Duke University with the optical resolution PAM system described in (M. Chen et al., "Simultaneous photoacoustic imaging of intravascular and tissue oxygenation," Optics Letters, vol. 44, no. 15, pp. 3773-3776, 2019.) at a wavelength of 532 nm. This dataset is composed of primarily mouse brain microvasculature images, with a few images of mouse ear and tumors. Although collected in 3-D, for the purposes of viewing, collection, and storage, the images have been projected into 2-D using maximum amplitude projection (MAP). These data were collected with support from the National Institutes of Health (R01 EB028143, R01 NS111039, R01 NS115581, R21 EB027304, R43 CA243822, R43 CA239830, R44 HL138185); Duke MEDx Basic Science Grant; Duke Center for Genomic and Computational Biology Faculty Research Grant; Duke Institute of Brain Science Incubator Award; American Heart Association Collaborative Sciences Award (18CSA34080277). The "clean" subset contains PAM images of variable size that have been preprocessed and stored as uint8 .jpg images. The "patches" subset contains non-overlapping 128-by-128 pixel patches of the "clean" images stored as uint8 .jpg images. The "raw" subset contains PAM images of variable size before they have been preprocessed (stored as uint16 .png images). For general deep learning tasks, the "clean" dataset can be used with random crop (none of the images have dimensions less than 128-by-128). If uniformly-sized images are desired without needing to use random crop, the "patches" subset would work best. The "raw" subset works best for deep learning tasks that expect input images that are closer to raw/unprocessed PAM images and are willing to take on the memory burden of loading uint16 .png images.