Caffe-model

Python script to generate prototxt on Caffe, specially the inception_v3\inception_v4\inception_resnet\fractalnet

Models

The prototxts can be visualized by ethereon.

Every model has a bn (batch normalization) version (maybe only bn version), the paper is Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

1. Lenet-5 (lenet.py)

   Lenet-5 was presented by Yann LeCun in Backpropagation applied to handwritten zip code recognition.

2. AlexNet (and caffenet in alexnet.py)

   AlexNet was initially described in [ImageNet Classification with Deep Convolutional Neural Networks] (http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)

   Implemention of CaffeNet is referenced by caffe/caffenet.py

3. Network in network (nin.py)

   NIN model was described in Network In Network

4. Inception_v1 (inception_v1.py)

   Inception conception was described in Going Deeper with Convolutions

5. VggNet (vggnet.py)

   Vgg presented the network in Very Deep Convolutional Networks for Large-Scale Image Recognition

   The implemention of vgg_11a,vgg_11a_bn,vgg_16c,vgg_16c_bn are in vggnet.py

6. Inception_v3 (inception_v3.py)

   Inception_v3 is the improved version of inception_v1, the details are described in Rethinking the Inception Architecture for Computer Vision

7. Inception_v4 (inception_resnet.py)

   Inception_v4 is is a more uniform simplified architecture and more inception modules than Inception-v3, the details are described in Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

8. Inception_resnet (inception_resnet.py)

   Inception_resnet_v2 combines the residual connections and the latest revised version of the Inception architecture, single crop-single model top-5 error of inception_resnet_v2 is 4.9% on the non-blacklisted subset of the validation set of ILSVRC 2012. The details are described in Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

9. ResNet

   Coming soon ......

Acknowlegement

I greatly thank Yangqing Jia and BVLC group for developing Caffe

And I would like to thank all the authors of every cnn model