The detailed structure of LightCNN v4 is shown in light_cnn_v4.py The input is an aligned 128*128BGRface image. The input pixel value is normalized by mean ([0.0, 0.0, 0.0]) and std ([255.0, 255.0, 255.0]). The Light CNN performance on lfw 6,000 pairs. ...
RCNN 的分类和回归,用 SVM 和回归算法,Fast R-CNN 则都是用神经网络处理 对比RCNN,Fast RCNN 的训练时间从 80 多小时减少到 9 小时,检测时间从 40 多秒减少到 2 秒 Fast-RCNN 的缺点是信息损失:首先,在特征映射部分,坐标缩放如果不能整除,就会损失部分信息,对于小的候选框影响比较大;其次,在 ROI Pooli...
Ncnn deployment on mobile,support:YOLOv5s,YOLOv4-tiny,MobileNetV2-YOLOv3-nano,Simple-Pose,Yolact,ChineseOCR-lite,ENet,Landmark106,DBFace,MBNv2-FCN and MBNv3-Seg-small on camera. Android: Due to factors such as mobile phone performance and image size, FPS varies greatly on different mobile ...
对于池化层的作用现在还很难给出比较完整的解释,一般假定池化层可以通过如下三个方面来对CNN的性能产生帮助:1)p-norm(p范数)使CNN的表示更具不变性(invariance);2)降维使高层能够覆盖输入层的更多部分(receptive field);3)池化的feature-wise特性能够使得优化更为容易。假设第2)点,即降维对与CNN的性能提升至关重...
4. Feature Re-Use 5. Re-parameterization (Rep-VGG / MobileOne..) Kiwi:Review | Lightweight Efficient Vision Backbones | Part I 6. Efficient ViT on edge devices (cnn +vit) Tobe in Part III -1. References
The author added the idea of feature fusion to the faster R-CNN to improve detection performance. The improved multiscale defect detection network on the aluminum dataset achieved a higher mAP of 75.8%. Zhang et al.3 used the improved YOLOv3 to detect the surface defects of steel strips, ...
对于池化层的作用现在还很难给出比较完整的解释,一般假定池化层可以通过如下三个方面来对CNN的性能产生帮助:1)p-norm(p范数)使CNN的表示更具不变性(invariance);2)降维使高层能够覆盖输入层的更多部分(receptive field);3)池化的feature-wise特性能够使得优化更为容易。假设第2)点,即降维对与CNN的性能提升至关重...
Girshick, R.: Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp. 1440–1448. arXiv:1504.08083 (2015) Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings...
At present, the one-stage target detection algorithm represented by YOLO series and the two-stage target detection algorithm represented by R-CNN series have become two different directions in the field of target detection. Although the two-stage target detection algorithm has higher detection accuracy...
Light-HeadRCNN:InDefenseoftwo-stageobjectdetector文章基于faster-rcnn和r-fcn进行改进 本文研究two-stage慢于...类别和位置,lightheadrcnn的head部分前面是r-fcnscore map的改进,后面是fasterrcnn进行region分类和位置预测的改进。深度学习模型要提高速度主要的思路 ...