VisualAtom-1k

VisualAtom is a cutting-edge artificial image dataset, specifically designed for pre-training deep learning models for image recognition tasks, such as Vision Transformers. Generated through the innovative synthesis of geometric contours, VisualAtom offers a rich and diverse synthetic images, achieved by assigning various stationary waveforms to the contour lines.<br> The primary goal of VisualAtom is to provide pre-training effect that rivals large real image datasets, such as ImageNet and JFT. By offering a wide variety of synthesized geometric contours, VisualAtom allows deep learning models to develop a robust understanding of diverse visual structures, thus enabling them to perform at comparable levels to models pre-trained on real images. Furthermore, the datasets and models are licensed for commercial use and are not restricted to educational or academic use only.<br> To facilitate easy access and customization, the generation scripts and usage instructions for VisualAtom are available on our GitHub page at https://github.com/masora1030/CVPR2023-FDSL-on-VisualAtom. Users are encouraged to explore the repository and generate and pre-train on VisualAtom to their specific needs, further expanding the possibilities of VisualAtom.