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| Faster R-CNN× | YOLO (You Only Look Once)× | |
|---|---|---|
| Dziedzina | Uczenie głębokie | Uczenie głębokie |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2015 | 2016 |
| Twórca≠ | Ren, S.; He, K.; Girshick, R.; Sun, J. (Microsoft Research) | Redmon, J.; Divvala, S.; Girshick, R.; Farhadi, A. |
| Typ≠ | Two-stage object detection CNN | Single-shot convolutional object detector |
| Źródło pierwotne≠ | Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Advances in Neural Information Processing Systems (NeurIPS), 28, 91–99. link ↗ | 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. DOI ↗ |
| Inne nazwy | Faster RCNN, Faster-RCNN, RPN-based detector, two-stage object detector | You Only Look Once, YOLO detector, YOLOv1, single-shot detector |
| Pokrewne≠ | 2 | 1 |
| Podsumowanie≠ | Faster R-CNN is a two-stage deep convolutional object detection framework introduced by Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun (Microsoft Research) at NeurIPS 2015. It replaces the slow selective-search region proposal step used in its predecessors R-CNN and Fast R-CNN with a learned Region Proposal Network (RPN) that shares convolutional features with the detection head, enabling the first end-to-end trainable, near-real-time accurate object detector and establishing a long-standing accuracy benchmark on PASCAL VOC and MS COCO. | 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. |
| ScholarGateZbiór danych ↗ |
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