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Ver_0.72
- CNN Class, ver 0.72. Change log: Ver 0.72: Sample GUI added, demonstrating use of convolutional network for handwriten digits recognition. Training runs 20 faster. Ver 0.71: Bug fix: training was stop
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
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
py-faster-rcnn-master
- faster-r-cnn实现 可用 网上还有大量教程(faster-r-cnn source code)
keras-frcnn-master
- keras实现fcnn源代码,用于视觉目标检测(fcnn source code based on keras)
R-FCN
- 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)
labelImg-master
- LabelImg 是一个可视化的图像标定工具。使用该工具前需配置环境python + lxml。Faster R-CNN，YOLO，SSD等目标检测网络所需要的数据集，均需要借此工具标定图像中的目标。(LabelImg is a visual image calibration tool. You need to configure the environment Python + lxml before using this tool
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