A Multi-Microscope Annotated Dataset of Real Microscopy Images of Microorganisms for Classification, Detection, and Segmentation

This dataset contains 100,000 microscopic images of microorganisms belonging to five classes (micrococci, diplococci, streptococci, bacilli, and backgrounds), along with corresponding annotations in the form of bounding boxes and binary segmentation masks. The dataset is designed for the training, validation, and comparative testing of computer vision models aimed at analyzing microbiological images acquired under diverse, real-world-like imaging conditions. Dataset structure The dataset is organized into two folders: images/ — microscopy images in PNG format masks/ — corresponding binary segmentation masks in PNG format Each image and its mask share the same filename. On the masks, white pixels with a value of 255 correspond to foreground regions, whereas black pixels with a value of 0 represent the background. The dataset is specifically constructed to reflect the variability encountered in real-world diagnostic laboratories, with images acquired using different microscopes, magnification settings, illumination configurations, and staining procedures. This heterogeneity ensures that models trained on this dataset generalize effectively to diverse clinical and research settings. Number of image/mask pairs100,000 Images per class20000 Image formatJPG, PNG Mask formatPNG