Fast Region-based Convolutional Networks for object detection. Fast R-CNN** is a fast framework for object detection with deep ConvNets. Fast R-CNN - trains state-of-the-art models, like VGG16, 9x faster than traditional R-CNN and 3x faster than

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

R-FCN全卷积网络的目标检测神经网络，算法性能优于Faster R-CNN(R-FCN is an full convultional network, the performance of R-FCN outperform the Faster R-CNN)

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以实现end-to-end learning，这个可以认为是