<ul data-eligibleForWebStory="true"><li>Vision Transformers (ViTs) outperform Convolutional Neural Networks (CNNs) in image classification due to factors like scalability and the ability to learn richer and more complex features with large datasets.</li><li>ViTs require more data or regularization to train effectively initially but demonstrate superior performance when pre-trained on massive image corpora compared to CNNs, achieving better efficiency at scale.</li><li>ViTs have been successful in various computer vision tasks beyond simple classification, such as object detection and image segmentation, where they have reached state-of-the-art results by capturing global context and long-range dependencies.</li><li>ViTs excel in fine-grained vision problems by focusing on subtle image details, making them valuable for tasks like fine-grained classification, biodiversity image recognition, and attribute classification, establishing themselves as a powerful approach in computer vision.</li></ul>

Vision Transformers Outperform CNNs in Image Classification

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