Introducing the SkyFusion dataset, designed for detecting tiny objects in satellite images. While many datasets focus on larger structures

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A “roads segmentation dataset” typically consists of images or aerial photographs that have been annotated to highlight and classify road areas within the images. This dataset is commonly used in computer vision and machine learning tasks, particularly for training and evaluating algorithms related to road detection and segmentation. The annotations in such a dataset provide pixel-level or object-level labels to distinguish road areas from other elements in the images, making it valuable for applications like autonomous driving, urban planning, and map generation. Researchers and developers use roads segmentation datasets to train models to identify and segment roads accurately from various types of imagery.

Discover Afro-MNIST, the innovative dataset tailored for machine learning with African numeral systems including Ge`ez, Vai, Osmanya, and N’Ko.

At Globose Technology Solutions, we’re using the COIL-100 Dataset to improve object recognition solutions. Our advanced algorithms are making a difference in areas like industrial automation and security systems.

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