Сравнение методов

	Классификация изображений	Сегментация экземпляров
Область	Глубокое обучение	Глубокое обучение
Семейство	Machine learning	Machine learning
Год появления	2012 (deep CNN era); conceptual roots 1989 (LeCun)	2017
Автор метода	Krizhevsky, A.; Sutskever, I.; Hinton, G. E.	He, K., Gkioxari, G., Dollar, P., Girshick, R.
Тип	Supervised classification task	Pixel-level detection and mask prediction
Основополагающий источник	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.	He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961–2969.
Другие названия	visual classification, image recognition, CNN-based classification, visual categorization	instance-level segmentation, object instance segmentation, mask prediction, panoptic instance segmentation
Связанные	5	4
Сводка	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.	Instance segmentation is a computer vision task that simultaneously detects every distinct object in an image and produces a precise pixel-level mask for each individual object instance. Unlike semantic segmentation, which labels every pixel with a class, instance segmentation distinguishes between separate objects of the same class, enabling fine-grained spatial understanding.