caffe

I am working on a limited number of large size images, each of which can have 3072*3072 pixels. To train a semantic segmentation model using FCN or U-net, I construct a large sample of training sets, each training image is 128*128 . In the prediction stage, what I do is to cut a large image into small pieces, the same as trainning set of 128*128 , and feed these small pieces into the trained model, get the predicted mask. Afterwards, I just stitch these small patches together to get the mask for the whole image. Is this the right mechanism to perform the semantic segmentation against the large

In the famous Google Inceptionism article, http://googleresearch.blogspot.jp/2015/06/inceptionism-going-deeper-into-neural.html they show images obtained for each class, such as banana or ant. I want to do the same for other datasets. The article does describe how it was obtained, but I feel that the explanation is insufficient. There's a related code https://github.com/google/deepdream/blob/master/dream.ipynb but what it does is to produce a random dreamy image, rather than specifying a class and learn what it looks like in the network, as shown in the article above. Could anyone give a more

I would like to split the Blob channels in Caffe, so that I can split one Blob of (N, c, w, h) into two output Blobs of size (N, c/2, w, h) . What I have described above is very general, what I want to do actually is to separate a two-channel input image into two different images. One goes to a convolutional layer and the other goes to a pooling layer. Finally, I concatenate the outputs. So I am wondering if a Caffe layer that allows the user to do such thing exists, and how to define it in the prototxt file. Yes, the Slice layer is for that purpose. From the Layer Catalogue : The Slice layer

I am trying to port caffe (developed for Linux) source code to Windows environment. The problem is at sigaction structure at signal_handler.cpp and signal_handler.h . The source codes are shown below. My query is which library or code replacement can be done to make this sigaction works in Windows. ///Header file #ifndef INCLUDE_CAFFE_UTIL_SIGNAL_HANDLER_H_ #define INCLUDE_CAFFE_UTIL_SIGNAL_HANDLER_H_ #include "caffe/proto/caffe.pb.h" #include "caffe/solver.hpp" namespace caffe { class SignalHandler { public: // Contructor. Specify what action to take when a signal is received. SignalHandler

Is there a relatively simple way to extract weights in Python from one of the many pretrained models in Caffe Zoo WITHOUT CAFFE (nor pyCaffe)? i.e. parsing .caffemodel to hdf5/numpy or whatever format that can be read by Python? All the answers I found use C++ code with caffe classes or Pycaffe. I have looked at pycaffe's code it looks like you really need caffe to make sense of the binary is that the only solution? I had to resolve that exact issue just now. Assuming you have a .caffemodel (binary proto format), it turns out to be quite simple. Download the latest caffe.proto Compile into

What I want is, After loading a net, I will decompose some certain layers and save the new net. For example Orignial net: data -> conv1 -> conv2 -> fc1 -> fc2 -> softmax; New net: data -> conv1_1 -> conv1_2 -> conv2_1 -> conv2_2 -> fc1 -> fc2 -> softmax Therefore, during this process, I stuck in the following situation: 1. How to new a certain layer with specified layer parameters in pycaffe ? 2. How to copy the layer parameters from existing layers(such as fc1 and fc2 above)? I know by using caffe::net_spec , we can define a new net manually. But caffe::net_spec can not specify a layer from a

I am modifying a Caffe tutorial to implement a neural network, but I am struggling to identify where some of the pycaffe modules are location in order to see certain function definitions. For example, the tutorial mentions: import caffe from caffe import layers a L, params as P .... L.Convolution(bottom, kernel_size=ks, stride=stride, num_output=nout, pad=pad, group=group) L.InnerProduct(bottom, num_output=nout) L.ReLU(fc, in_place=True) ... Where can I find these function definitions and where can I see what other types of layers are pre-defined? I see that layers and params are defined here