Machine learningDeep learning / NLP / CV
Image Classification
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.
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Sources
- 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. link ↗
- He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778. DOI: 10.1109/CVPR.2016.90 ↗
Related methods
Referenced by
Domain-adaptive Convolutional Neural NetworkDomain-adaptive image classificationExplainable Image ClassificationExplainable Vision TransformerFine-Tuned Convolutional Neural NetworkFine-Tuned Image ClassificationFine-Tuned Vision TransformerInstance SegmentationMultilingual Image ClassificationMultimodal Convolutional Neural NetworkMultimodal Image ClassificationMultimodal Object DetectionMultimodal TransformerMultimodal Vision TransformerObject DetectionSemantic SegmentationSemi-supervised Image ClassificationSemi-supervised Vision TransformerTransfer Learning with Convolutional Neural NetworkTransfer Learning with Image ClassificationWeakly supervised convolutional neural networkWeakly Supervised Image ClassificationWeakly Supervised Object Detection