So sánh phương pháp

	Phân loại ảnh tinh chỉnh	Phát hiện đối tượng
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2010–2014	2014–2016
Người khởi xướng	Yosinski, J. et al.; Pan, S. J. & Yang, Q.	Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO)
Loại	Transfer learning / fine-tuning	Supervised deep learning (region proposal or single-shot)
Công trình gốc	Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems (NeurIPS), 27, 3320–3328.	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.
Tên gọi khác	fine-tuning for image recognition, transfer learning image classifier, pretrained CNN fine-tuning, domain-specific image classifier	visual object detection, image object localization, region-based object detection, bounding-box detection
Liên quan	5	3
Tóm tắt	Fine-tuned image classification adapts a large neural network pretrained on a broad image corpus (such as ImageNet) to a specific target domain by continuing training on labeled domain images. This approach achieves strong accuracy with far fewer target-domain samples than training from scratch, making it the dominant paradigm for applied computer vision tasks.	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.