Compară metode

	Clasificarea imaginilor prin ajustare fină (fine-tuning)	Detecția obiectelor
Domeniu	Învățare profundă	Învățare profundă
Familie	Machine learning	Machine learning
Anul apariției	2010–2014	2014–2016
Autorul original	Yosinski, J. et al.; Pan, S. J. & Yang, Q.	Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO)
Tip	Transfer learning / fine-tuning	Supervised deep learning (region proposal or single-shot)
Sursa seminală	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.
Denumiri alternative	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
Înrudite	5	3
Rezumat	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.