Сравнение методов

	Трансферное обучение для детектирования объектов	Обнаружение объектов
Область	Глубокое обучение	Глубокое обучение
Семейство	Machine learning	Machine learning
Год появления	2010–2014	2014–2016
Автор метода	Girshick, R. et al. (R-CNN line); Pan & Yang (transfer learning framework)	Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO)
Тип	Transfer learning / fine-tuning	Supervised deep learning (region proposal or single-shot)
Основополагающий источник	Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.	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.
Другие названия	pretrained object detector, fine-tuned object detection, TL-OD, domain-adapted object detection	visual object detection, image object localization, region-based object detection, bounding-box detection
Связанные	3	3
Сводка	Transfer learning with object detection starts from a deep neural network pretrained on a large image dataset — typically ImageNet for the backbone or COCO for the full detector — and adapts it to detect objects in a new domain. By reusing learned visual representations, it achieves strong detection accuracy with far fewer annotated images than training from scratch would require.	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.