विधियों की तुलना करें

	छवि वर्गीकरण	विजन ट्रांसफार्मर
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2012 (deep CNN era); conceptual roots 1989 (LeCun)	2021
प्रवर्तक	Krizhevsky, A.; Sutskever, I.; Hinton, G. E.	Dosovitskiy, A. et al.
प्रकार	Supervised classification task	Transformer architecture for images (self-attention over patches)
मौलिक स्रोत	Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems (NeurIPS), 25, 1097–1105.	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
उपनाम	visual classification, image recognition, CNN-based classification, visual categorization	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
संबंधित	5	5
सारांश	Image classification is the task of assigning a single semantic label to an entire image from a fixed set of categories. Modern approaches rely on deep convolutional neural networks (CNNs) or Vision Transformers (ViTs) trained end-to-end on large labeled datasets such as ImageNet, achieving superhuman accuracy on many benchmarks and underpinning applications from medical imaging to autonomous vehicles.	The Vision Transformer (ViT), introduced by Dosovitskiy and colleagues in 2021, splits an image into fixed-size patches, treats those patches as a sequence, and applies the Transformer self-attention mechanism to image classification. Given enough training data, it surpasses convolutional neural networks (CNNs).