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faster rcnn

时间: 2023-09-26 07:13:16 浏览: 82

FasterRCNN-pytorch:FasterRCNN在VGG，ResNet和FPN基础中实现

更快的RCNN-pytorch FasterRCNN在VGG，ResNet和FPN基础中实现。参考： rbg的FasterRCNN代码： : 模型表现在VOC2017上进行培训在VOC2017上进行测试骨干地图 VGG16 0.7061 ResNet101 0.754 训练模型 1.运行前，您需要：光盘./lib 在make.sh和setup.py中更改gpu_id。具体来说，您需要在make.sh的第5、12和19行以及setup.py的第143行中修改参数设置，其中包含关键字“ -arch =”取决于您的gpu模型。（选择适当的体系结构，见下表） sh make.sh GPU型号建筑学 TitanX（麦克斯韦/帕斯卡） sm_52 GTX 960M sm_50 GTX 108（钛） sm_61 网格K520（AWS g2.2xlarge

Faster R-CNN is a deep learning algorithm for object detection. It was introduced by Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun in 2015. Faster R-CNN builds on the previous Region-CNN (R-CNN) and Fast R-CNN algorithms, and improves the speed and accuracy of object detection. The key innovation of Faster R-CNN is the introduction of a Region Proposal Network (RPN) that shares convolutional features with the detection network. The RPN generates region proposals, which are then fed into the detection network to classify and refine the object detections. This approach allows for end-to-end training of the entire system and significantly improves the speed and accuracy of object detection. Faster R-CNN has been widely used in various applications such as autonomous vehicles, surveillance, and robotics.

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