caffe

I am trying to run imagenet example in Caffe. In this( https://github.com/BVLC/caffe/tree/master/examples/imagenet ) page they say We assume that you already have downloaded the ImageNet training data and validation data, and they are stored on your disk like: /path/to/imagenet/train/n01440764/n01440764_10026.JPEG /path/to/imagenet/val/ILSVRC2012_val_00000001.JPEG Where do I find this data? It's a bit of a process. 1. Got to imagenet's download page and select "Download Image URLs". 2. Download the image URL list from the links at the bottom of the page, e.g., fall 2011's list . 3. Download

I'd like to use the Caffe library to extract image features but I'm having performance issues. I can only use the CPU mode. I was told Caffe supported batch processing mode, in which the average time required to process one image was much slower. I'm calling the following method: const vector<Blob<Dtype>*>& Net::Forward(const vector<Blob<Dtype>* > & bottom, Dtype* loss = NULL); and I'm putting in a vector of size 1, containing a single blob of the following dimensions - (num: 10, channels: 3, width: 227, height: 227). It represents a single image oversampled in the same way as in the official

In my Caffe 'train.prototxt' I'm doing some input data transformation, like this: transform_param { mirror: true crop_size: 321 mean_value: 104 # Red ? mean_value: 116 # Blue ? mean_value: 122 # Green ? } Now I want to store a modified version of my input images such that certain image regions are set to those mean values. The rational is that those regions are then set to 0 during mean subtraction. However I don't know what the order of channels is that caffe expects in such a prototxt file and I couldn't look it up in the caffe code either. Does someone now whether the 3 values given above

I struggle with Caffe compilation. Unfortunately I failed to compile it. Steps I followed: git clone https://github.com/BVLC/caffe.git cd caffe mkdir build cd build cmake .. make all Running make all fails with the following error message: [ 2%] Building NVCC (Device) object src/caffe/CMakeFiles/cuda_compile.dir/util/cuda_compile_generated_im2col.cu.o In file included from /usr/include/cuda_runtime.h:59:0, from <command-line>:0: /usr/include/host_config.h:82:2: error: #error -- unsupported GNU version! gcc 4.9 and up are not supported! #error -- unsupported GNU version! gcc 4.9 and up are not

I am trying out Google's deepdream code which makes use of Caffe. They use the GoogLeNet model pre-trained on ImageNet, as provided by the ModelZoo. That means the network was trained on images cropped to the size 224x224 pixel. From the train_val.prototext : layer { name: "data" type: "Data" ... transform_param { mirror: true crop_size: 224 ... The deploy.prototext used for processing also defines an input layer with the size of 224x224x3x10 (RGB images of size 224x224, batchsize 10). name: "GoogleNet" input: "data" input_shape { dim: 10 dim: 3 dim: 224 dim: 224 } However I can use this net

I am new in caffe framework and I would like to use caffe to implement the training with multi-label. I use two LMDB to save data and labels, respectively. The data LMDB is of dimension Nx1xHxW while the label LMDB is of dimension Nx1x1x3. Labels are float data. The text file is as follow: 5911 3 train/train_data/4224.bmp 13 0 12 train/train_data/3625.bmp 11 3 7 ... ... I use C++ to create LMDB. My main.cpp: #include <algorithm> #include <fstream> // NOLINT(readability/streams) #include <string> #include <utility> #include <vector> #include <QImage> #include "boost/scoped_ptr.hpp" #include