قارن الطرق

	شبكة البقايا (ResNet)	يولو (أنت تنظر مرة واحدة فقط)
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2016	2016
صاحب الطريقة	He, K.; Zhang, X.; Ren, S.; Sun, J.	Redmon, J.; Divvala, S.; Girshick, R.; Farhadi, A.
النوع	Deep Convolutional Neural Network with skip connections	Single-shot convolutional object detector
المصدر التأسيسي	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.	Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779–788.
الأسماء البديلة	ResNet, Residual Network, Deep Residual Learning, ResNet-50	You Only Look Once, YOLO detector, YOLOv1, single-shot detector
ذات صلة	4	1
الملخص	ResNet (Residual Network) is a deep convolutional neural network architecture introduced by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun at CVPR 2016. By inserting shortcut (skip) connections that carry the input of a block directly to its output — defining the block's task as learning a residual correction rather than a full mapping — ResNet enabled training of networks with hundreds or even thousands of layers without the vanishing-gradient degradation that had previously made very deep networks impractical. It won the ILSVRC 2015 image recognition competition with a top-5 error of 3.57% and remains the most widely used backbone architecture in computer vision.	YOLO (You Only Look Once) is a single-shot, end-to-end convolutional object detector introduced by Redmon, Divvala, Girshick, and Farhadi at CVPR 2016. It reframes object detection as a single regression problem — predicting bounding box coordinates and class probabilities directly from an image in one forward pass — achieving real-time detection speeds that prior two-stage methods such as R-CNN could not match. The original paper spawned a widely adopted family of successors (YOLOv2 through v11) that continues to dominate applied object detection benchmarks.