matcaffe

问题 How can I use the pretrained e.g. AlexNet model to train a CNN which supports larger (e.g. 500x500) input image sizes? In other words, how to copy only the convolutional filters to the new network (using Matlab wrapper)? 来源： https://stackoverflow.com/questions/38244793/caffe-copy-pretrained-weights-to-train-a-network-which-supports-for-larger-input

问题 My data set is a HDF5 file consists of data with shape [129028,1,12,1024] and label of shape [129028,1,1,1] . But when I run solver.prototxt, I get the error message: I0413 08:54:34.689985 17769 hdf5.cpp:32] Datatype class: H5T_FLOAT F0413 08:54:40.661201 17769 hdf5_data_layer.cpp:53] Check failed: hdf_blobs_[i] ->shape(0) == num (1 vs. 1024) *** Check failure stack trace: *** 回答1: It looks like you saved your hdf5 from matlab, rather than python (judging by your previous question). When

问题 I have two images/patches (the larger one includes the smaller one and some context information around it) of different sizes (I dont wont to create a new image by just concatenating them). I want to create a CNN as shown in the figure. How can I create it using Caffe prototxt file? Can I directly use Caffe to train it? Both CNNs can share the same weights. 来源： https://stackoverflow.com/questions/38226378/how-to-create-this-cnn-architecture-in-caffe

问题 I thought we might be able to compile a Caffeinated description of some methods of performing multiple category classification . By multi category classification I mean: The input data containing representations of multiple model output categories and/or simply being classifiable under multiple model output categories. E.g. An image containing a cat & dog would output (ideally) ~1 for both the cat & dog prediction categories and ~0 for all others. Based on this paper, this stale and closed PR

问题 I am able to compile matcaffe but I am unable to run make mattest . My system config: Ubuntu -16.04, opencv 2.4.9, gcc-5, g++-5, Matlab2017b. Here's the crash report from matlab: Segmentation violation detected at Fri Mar 2 12:37:16 2018 ------------------------------------------------------------------------ Configuration: Crash Decoding : Disabled - No sandbox or build area path Crash Mode : continue (default) Current Graphics Driver: Unknown software Current Visual : None Default Encoding

问题 I have a data set which form is matlab file. The data set contains 600,000 samples and every sample is a matrix of 7-by-256. My data is not image but signal. I want to use CNN of caffe to train the data. So how can I convert it to LMDB as my input of CNN. I'm badly need the solution! 回答1: Converting data in matlab directly to lmdb might be a little tricky. Why don't you try exporting your data to hdf5 binary files (supported both by matlab and caffe)? Here is an answer describing how this can

问题 Following are the input layers of my fine-tuned model: layer { type: "HDF5Data" name: "data" top: "Meta" hdf5_data_param { source: "/path/to/train.txt" batch_size: 50 } include { phase: TRAIN } } layer { name: "data" type: "ImageData" top: "X" top: "Labels" include { phase: TRAIN } transform_param { mirror: true crop_size: 227 mean_file: "data/ilsvrc12/imagenet_mean.binaryproto" } image_data_param { source: "/path/to/train.txt" batch_size: 50 new_height: 256 new_width: 256 } } layer { type:

问题 Caffe is a deep learning framework that also includes a Matlab interface (Matcaffe). While Caffe supports multiple GPUs it seems that Matcaffe does not at the moment. Does anyone know of a workaround? 来源： https://stackoverflow.com/questions/33446612/caffe-multi-gpu-support-with-matlab-matcaffe

问题 I followed the instructions here to create weight matrix for classes and add the Infogainloss function, which is able to tackle my imbalance data. The few last layers .... layer { name: "score_fr" type: "Convolution" bottom: "fc7" top: "score_fr" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 5 #21 pad: 0 kernel_size: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" } } } layer { name: "upscore" type: "Deconvolution"

问题 I'm using the pretrained imagenet model provided along the caffe (CNN) library ( 'bvlc_reference_caffenet.caffemodel' ). I can output a 1000 dim vector of object scores for any images using this model. However I don't know what the actual object categories are. Did someone find a file, where the corresponding object categories are listed? 回答1: You should look for the file 'synset_words.txt' it has 1000 line each line provides a description of a different class. For more information on how to