深度残差网络:ResNet
2016-11-23 15:30
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ResNet:
一、介绍
caffe-fast-rcnn(Caffe、FSRCNN、FastRCNN)
shape {
dim: 1 #num,可自行定义
dim: 3 #通道数,表示RGB三个通道
dim: 32 #图像的长和宽,通过 _train_test.prototxt文件中数据输入层的crop_size获取
dim: 32}
二、训练
http://www.cnblogs.com/ml-cv/p/5719531.html 深度残差网(deep residual networks)的训练过程
1、下载基于python的训练代码:
https://github.com/dnlcrl/deep-residual-networks-pyfunt
2、pyfunt需要安装:
3、
https://www.cs.toronto.edu/%7Ekriz/cifar.html The CIFAR-10 dataset
参考资料:
http://blog.csdn.net/forest_world/article/details/53035009 LeNet、AlexNet、GoogLeNet、VGG、ResNet
http://www.cnblogs.com/daihengchen/p/5761304.html 使用caffe测试自己的图片
http://blog.csdn.net/lg1259156776/article/details/52550865 神经网络与深度学习 Caffe部署中的几个train-test-solver-prototxt-deploy等说明<三>
http://www.kaiminghe.com/ Kaiming He
http://blog.csdn.net/sunbaigui/article/details/50906002 [caffe]深度学习之MSRA图像分类模型Deep Residual Network(深度残差网络)解读
http://blog.csdn.net/yichenmoyan/article/details/51885433 使用Keras搭建深度残差网络
http://blog.csdn.net/heyongluoyao8/article/details/52478715 梯度下降优化算法综述
http://mp.weixin.qq.com/s?__biz=MzIzNDQyNjI5Mg==&mid=100000125&idx=1&sn=72ba0e3e301281c13349f1a1821bad0d&chksm=68f7dba65f8052b0762594489c785ed67f19e111cf2c44dc4522e941989e85d8ee2a03203d26&mpshare=1&scene=23&srcid=1202GeZsjHGcHixoK41RU2mS#rd 深度学习所有文章
一、介绍
caffe-fast-rcnn(Caffe、FSRCNN、FastRCNN)
name: "ResNet_50_1by2" layer { name: "data" type: "Input" top: "data" input_param { shape: { dim: 1 dim: 3 dim: 224 dim: 224 } } // 第一个维度是图片数,第二个是通道数,后面的是图片的长宽 } layer { name: "conv_1" type: "Convolution" bottom: "data" top: "conv_1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 64 pad: 3 kernel_size: 7 stride: 2 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } }
shape {
dim: 1 #num,可自行定义
dim: 3 #通道数,表示RGB三个通道
dim: 32 #图像的长和宽,通过 _train_test.prototxt文件中数据输入层的crop_size获取
dim: 32}
二、训练
http://www.cnblogs.com/ml-cv/p/5719531.html 深度残差网(deep residual networks)的训练过程
1、下载基于python的训练代码:
https://github.com/dnlcrl/deep-residual-networks-pyfunt
2、pyfunt需要安装:
@ubuntu:~$ sudo pip install git+git://github.com/dnlcrl/PyFunt.git Downloading/unpacking git+git://github.com/dnlcrl/PyFunt.git Cloning git://github.com/dnlcrl/PyFunt.git to /tmp/pip-MS88tP-build customize UnixCCompiler warning: no files found matching 'setupegg.py' warning: no files found matching 'bscript' warning: no files found matching 'bento.info' warning: no files found matching '*' under directory 'doc' warning: no files found matching 'tox.ini' warning: no previously-included files matching '*_subr_*.f' found under directory 'pyfunt/linalg/src/id_dist/src' no previously-included directories found matching 'doc/build' no previously-included directories found matching 'doc/source/generated' no previously-included directories found matching '*/__pycache__' warning: no previously-included files matching '*~' found anywhere in distribution warning: no previously-included files matching '*.bak' found anywhere in distribution warning: no previously-included files matching '*.swp' found anywhere in distribution warning: no previously-included files matching '*.pyo' found anywhere in distribution Successfully installed numpy tqdm cython torchfile pyfunt Cleaning up...
3、
@ubuntu:~/deep-residual-networks-pyfunt$ git clone https://github.com/dnlcrl//PyDatSet Cloning into 'PyDatSet'... remote: Counting objects: 185, done. remote: Total 185 (delta 0), reused 0 (delta 0), pack-reused 185 Receiving objects: 100% (185/185), 29.90 KiB | 11.00 KiB/s, done. Resolving deltas: 100% (111/111), done. Checking connectivity... done.
@ubuntu:~/deep-residual-networks-pyfunt/PyDatSet$ sudo python setup.py install [sudo] password for wei: /usr/lib/python2.7/distutils/dist.py:267: UserWarning: Unknown distribution option: 'install_requires' warnings.warn(msg) running install running build running build_py running install_lib creating /usr/local/lib/python2.7/dist-packages/pydatset copying build/lib.linux-x86_64-2.7/pydatset/gtsrb.py -> /usr/local/lib/python2.7/dist-packages/pydatset copying build/lib.linux-x86_64-2.7/pydatset/__init__.py -> /usr/local/lib/python2.7/dist-packages/pydatset copying build/lib.linux-x86_64-2.7/pydatset/sfddd.py -> /usr/local/lib/python2.7/dist-packages/pydatset copying build/lib.linux-x86_64-2.7/pydatset/tiny_imagenet.py -> /usr/local/lib/python2.7/dist-packages/pydatset copying build/lib.linux-x86_64-2.7/pydatset/cifar10.py -> /usr/local/lib/python2.7/dist-packages/pydatset copying build/lib.linux-x86_64-2.7/pydatset/mnist.py -> /usr/local/lib/python2.7/dist-packages/pydatset copying build/lib.linux-x86_64-2.7/pydatset/data_augmentation.py -> /usr/local/lib/python2.7/dist-packages/pydatset byte-compiling /usr/local/lib/python2.7/dist-packages/pydatset/gtsrb.py to gtsrb.pyc byte-compiling /usr/local/lib/python2.7/dist-packages/pydatset/__init__.py to __init__.pyc byte-compiling /usr/local/lib/python2.7/dist-packages/pydatset/sfddd.py to sfddd.pyc byte-compiling /usr/local/lib/python2.7/dist-packages/pydatset/tiny_imagenet.py to tiny_imagenet.pyc byte-compiling /usr/local/lib/python2.7/dist-packages/pydatset/cifar10.py to cifar10.pyc byte-compiling /usr/local/lib/python2.7/dist-packages/pydatset/mnist.py to mnist.pyc byte-compiling /usr/local/lib/python2.7/dist-packages/pydatset/data_augmentation.py to data_augmentation.pyc running install_egg_info Writing /usr/local/lib/python2.7/dist-packages/pydatset-0.1.egg-info wei@ubuntu:~/deep-residual-networks-pyfunt/PyDatSet$
https://www.cs.toronto.edu/%7Ekriz/cifar.html The CIFAR-10 dataset
Download If you're going to use this dataset, please cite the tech report at the bottom of this page. Version Size md5sum CIFAR-10 python version 163 MB c58f30108f718f92721af3b95e74349a CIFAR-10 Matlab version 175 MB 70270af85842c9e89bb428ec9976c926 CIFAR-10 binary version (suitable for C programs) 162 MB c32a1d4ab5d03f1284b67883e8d87530
参考资料:
http://blog.csdn.net/forest_world/article/details/53035009 LeNet、AlexNet、GoogLeNet、VGG、ResNet
http://www.cnblogs.com/daihengchen/p/5761304.html 使用caffe测试自己的图片
http://blog.csdn.net/lg1259156776/article/details/52550865 神经网络与深度学习 Caffe部署中的几个train-test-solver-prototxt-deploy等说明<三>
http://www.kaiminghe.com/ Kaiming He
http://blog.csdn.net/sunbaigui/article/details/50906002 [caffe]深度学习之MSRA图像分类模型Deep Residual Network(深度残差网络)解读
http://blog.csdn.net/yichenmoyan/article/details/51885433 使用Keras搭建深度残差网络
http://blog.csdn.net/heyongluoyao8/article/details/52478715 梯度下降优化算法综述
http://mp.weixin.qq.com/s?__biz=MzIzNDQyNjI5Mg==&mid=100000125&idx=1&sn=72ba0e3e301281c13349f1a1821bad0d&chksm=68f7dba65f8052b0762594489c785ed67f19e111cf2c44dc4522e941989e85d8ee2a03203d26&mpshare=1&scene=23&srcid=1202GeZsjHGcHixoK41RU2mS#rd 深度学习所有文章
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