Krahasoni metodat

	Klasifikimi i imazheve	Zbulimi i objekteve
Fusha	Mësimi i thellë	Mësimi i thellë
Familja	Machine learning	Machine learning
Viti i origjinës	2012 (deep CNN era); conceptual roots 1989 (LeCun)	2014–2016
Krijuesi	Krizhevsky, A.; Sutskever, I.; Hinton, G. E.	Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO)
Lloji	Supervised classification task	Supervised deep learning (region proposal or single-shot)
Burimi themelues	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.	Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 580–587.
Emërtime të tjera	visual classification, image recognition, CNN-based classification, visual categorization	visual object detection, image object localization, region-based object detection, bounding-box detection
Të lidhura	5	3
Përmbledhja	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.	Object detection is a computer vision task in which a deep neural network simultaneously locates and classifies every instance of one or more object categories within an image, producing a bounding box and a class label for each detected object. Modern detectors — from the R-CNN family to YOLO and DETR — achieve near-human accuracy at real-time speeds on standard benchmarks.