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deep-residual-networks-master
- 深度残差网络的介绍与源代码，适合深度学习爱好者学习。这是何凯明大牛的又一部大作。-The depth of the residual network is introduced with the source code , suitable for deep learning lovers to study . This is another masterpiece He Kaiming Daniel .
resnet
- 深度残差网络ResNet，分别有50,101,152,200层(The depth residual network ResNet, respectively, has 50101152200 layers)
vgg16
- 在使用深度神经网络时我们一般推荐使用大牛的组推出的和成功的网络。如最近的google团队推出的BN-inception网络和inception-v3以及微软最新的深度残差网络ResNET。(In the use of deep neural network we generally recommend the use of cattle group launched and successful network. Such as the
deep-residual-networks-master
- 深度残差网络实现代码，有何凯明在2015年提出的残差网络，映入了自我影射，解决了深度网络的退化问题(deep residual network)
fb.resnet.torch-master
- facebook公司开发的基于torch的残差网络深度学习模型(Torch based depth learning model based on residual network developed by Facebook company)
利用残差网络进行图像融合
- 利用深度学习的方法进行红外图像和可见光图像的融合，该方法受启发于最近比较流行的迁移学习，利用可见光的数据集训练网络，从而提取出红外和可见光图像的特征，最后进行图像的融合。
Residual_Neural_Network-master
- 实现了残差神经网的训练，在jubernotebook上运行(The training of residual neural network is realized and it runs on jubernotebook)
深度学习实现零件缺陷检测源代码(1)
- 结合VGG和残差网络实现工业零件的缺陷检测，基于keras和tensorflow可以直接运行使用(The defect detection of industrial parts is realized by combining VGg and residual network. Based on keras and tensorflow, it can be used directly)