Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Faster R-CNN× | ResNet (Rețea Reziduală)× | |
|---|---|---|
| Domeniu | Învățare profundă | Învățare profundă |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 2015 | 2016 |
| Autorul original≠ | Ren, S.; He, K.; Girshick, R.; Sun, J. (Microsoft Research) | He, K.; Zhang, X.; Ren, S.; Sun, J. |
| Tip≠ | Two-stage object detection CNN | Deep Convolutional Neural Network with skip connections |
| Sursa seminală≠ | 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 ↗ | 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. DOI ↗ |
| Denumiri alternative≠ | Faster RCNN, Faster-RCNN, RPN-based detector, two-stage object detector | ResNet, Residual Network, Deep Residual Learning, ResNet-50 |
| Înrudite≠ | 2 | 4 |
| Rezumat≠ | 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. | 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. |
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