tensorflow版本 Faster RCNN训练自己的数据集-附件资源

目标检测算法的介绍 In recent years, Convolutional Neural Network (CNN) has been widely applied in computer vision tasks and has achieved significant improvement in image object detection. The CNN methods consume more computation as well as storage, so GPU is introduced for real-time object detection. However, due to the high power consumption of GPU, it is difficult to adopt GPU in mobile applications like automatic driving. The previous work proposes some optimizing techniques to lower the power consumption of object detection on mobile GPU or FPGA. In the first Low-Power Image Recognition Challenge (LPIRC), our system achieved the best result with mAP/Energy on mobile GPU platforms. We further research the acceleration of detection algorithms and implement two more systems for real-time detection on FPGA with higher energy efficiency. In this paper, we will introduce the object detection algorithms and summarize the optimizing techniques in three of our previous energy efficient detection systems on different hardware platforms for object detection.

faster_rcnn源码详细注释版 从训练、测试的参数到每一步源码，均有详细的批注，如果在使用中有问题，可以在对应博客评论区留言，或者私信我

该文档是本人利用Faster-rcnn python版本训练VOC2007数据集时遇到的错误记录

基于tensorflow搭建Faster R-CNN实现目标检测任务 有代码 有数据 可直接运行。 Faster R-CNN 实现目标检测 tensorflow 基于tensorflow搭建Faster R-CNN实现目标检测任务 有代码 有数据 可直接运行。 Faster R-CNN 实现目标检测 tensorflow

介绍利用Faster-RCNN去训练Pascol VOC数据集或者训练自定义数据集。 参考博客：https://blog.csdn.net/weixin_38346042/article/details/125044860?spm=1001.2014.3001.5502

线性反馈移位寄存器

目标检测faster_rcnn源码

随着无人驾驶和智能驾驶技术的发展，计算机视觉对视频图像检测的实时性和准确性要求也越来越高.现有的行人检测方法在检测速度和检测精度两个方面难以权衡.针对此问题，提出一种改进的Faster R-CNN模型，在Faster R-CNN的主体特征提取网络模块中加入SE网络单元，进行道路行人检测.这种方法不仅能达到相对较高的准确率，用于视频检测时还能达到一个较好的检测速率，其综合表现比Faster R-CNN模型更好.在INRIA数据集和私有数据集上的实验表明，模型的mAP最好成绩能达到93.76%，最高检测速度达到了13.79 f/s.

本套课程用keras模型从头实现一个fasterRCNN，全程实战。通过本课程的学习，不仅能掌握fasterRCNN训练原理，而且能极大提高深度学习的代码能力。