搜索资源列表
rcnnPfast-rcnnPfaster-rcnn
- 物体分割，Ross Girshick的R-CNN + fast R-CNN + faster R-C-object classification Ross Girshick. R-CNN+ fast R-CNN+ faster R-CNN
fast-rcnn-master
- Fast Region-based Convolutional Networks for object detection. Fast R-CNN** is a fast fr a mework for object detection with deep ConvNets. Fast R-CNN - trains state-of-the-art models, like VGG16, 9x faster than trad
py-faster-rcnn-master
- This an article on the depth of learning R-CNN article code, only for white learning-This is an article on the depth of learning R-CNN article code, only for white learning
caffe-ssd
- SSD和faster rcnn都是目前比较经典的基于caffe深度学习架构的一种方法,是目前比较先进的目标检测方法(SSD and faster RCNN are the most classic methods based on Caffe deep learning architecture, and they are more advanced target detection methods)
Faster-RCNN_TF-master (2)
- 机器学习 关于 faster r-cnn 进行object detection(This is an experimental Tensorflow implementation of Faster RCNN - a convnet for object detection with a region proposal network. For details about R-CNN please refer to the paper
faster_rcnn
- windows下faster_RCNN 方法用训练好的model进行目标检测的测试工程(under OS Windows , use the faster_RCNN method with a trained model for the target test of the test project)
faster_rcnn-master
- faster_rcnn的matlab试验资料，包括编译好的包，CUDA7.5，matlab2016a，VS2013(this is a good learning resource for object detection using deep learning. we use faster_rcnn algorithm and matlab coder cuda version is 7.5 VS version is 12.0
py-faster-rcnn-master
- fast-rcnn源码,可用于快速目标检测，如行人识别 车辆识别 车标识别(This is an sourcecode for python fast-rcnn)
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- 行人检测的经典论文，基于faster rcnn写的(pedestrian detection)
py-faster-rcnn-master
- faster-r-cnn实现 可用 网上还有大量教程(faster-r-cnn source code)
CrowdHumanCount
- Crowd counting based on faster-rcnn
fast-rcnn-master
- Fast R-CNN是在R-CNN的基础上进行的改进，大致框架是一致的。总体而言，Fast R-CNN相对于R-CNN而言，主要提出了三个改进策略： 1. 提出了RoIPooling，避免了对提取的region proposals进行缩放到224x224，然后经过pre-trained CNN进行检测的步骤，加速了整个网络的learning与inference过程，这个是巨大的改进，并且RoIPooling是可导的，因此使得整个网络可
深度学习之PyTorch物体检测实战
- 学习pytorch的入门书籍，非常详细。 本书是国内原创图书市场上首部系统介绍物体检测技术的图书。书 中利用PyTorch深度学习框架，从代码层面讲解了Faster RCNN、SSD及 YOLO这三大经典框架的相关知识，并进一步介绍了物体检测的细节与 难点问题，让读者可以全面、深入、透彻地理解物体检测的种种细节， 并能真正提升实战能力，从而将这些技术灵活地应用到实际开发中，享 受深度学习带来的快乐。