caffe

问题 I am trying to train a network on Caffe. I have image size of 512x640. Batch size is 1. I'm trying to implement FCN-8s. I am currently running this on a Amazon EC2 instance (g2.2xlarge) with 4GB of GPU memory. But when I run the solver, it immediately throws out an error Check failed: error == cudaSuccess (2 vs. 0) out of memory *** Check failure stack trace: *** Aborted (core dumped) Can someone help me proceed from here? 回答1: The error you get is indeed out of memory, but it's not the RAM,

问题 Excuse me, I have a question on using caffe for hd data? I try to run an example on the Kaggle mnist csv data with the following steps use h5py to convert it to h5 data. (I use the caffe-example.py to convert) Then modify the lenet_train_test_prototxt and train it. I am quite at a loss of this step. The only change I made here is layer { name: "mnist" type: "HDF5Data" top: "data" top: "label" include { phase: TRAIN } transform_param { scale: 0.00390625 } data_param { source: "data/mnist_train

问题 everyone,when I test ssd_pascal_video.py in windows,I got the following error: E:\caffe-ssd-microsoft\Build\x64\Release\pycaffe>python E:\caffe-ssd-microsoft\examples\ssd\ssd_pascal_webcam.py --cpu Traceback (most recent call last): File "E:\caffe-ssd-microsoft\examples\ssd\ssd_pascal_webcam.py", line 151, in <module> for file in os.listdir(snapshot_dir): WindowsError: [Error 3] : 'models/VGGNet/VOC0712/SSD_300x300/*.*' then,I check the ssd_pascal_video.py,I find the following code,I can't

问题 I'm training a small (10M weights on 12K images) FCN (see e.g. Long et al, 2015). The architecture is the following (it starts with FCN8s fc7 layer): fc7->relu1->dropout->conv2048->conv1024->conv512->deconv1->deconv2->deconv3->deconv4->deconv5->crop->softmax_with_loss When I initialized all deconv layers with Gaussian weights, I got some (though not always) reasonable result. Then I decided to do it the right way, and used the scripts provided by Shelhamer (e.g. https://github.com/zeakey

问题 I am new to caffe and Machine learning algorithms. Are there any tutorials to train and TEST LeNet on the MNIST data using caffe and python 2.7(preferably). Thanks 回答1: There's caffe's ipython notebook example Learning LeNet. The code is not limited to ipython notebook (although easier to navigate). You can copy them into the python interpreter and run them there. 来源： https://stackoverflow.com/questions/35151739/how-to-train-and-test-lenet-using-caffe-using-python

问题 when I run train_caffenet.sh , I get the following errors: I0906 10:56:42.327703 21556 solver.cpp:91] Creating training net from net file: /home/pris/caffe-master/examples/myself/train_val.prototxt [libprotobuf ERROR google/protobuf/text_format.cc:245] Error parsing text-format caffe.NetParameter: 26:12: Message type "caffe.ImageDataParameter" has no field named "backend". F0906 10:56:42.327837 21556 upgrade_proto.cpp:79] Check failed: ReadProtoFromTextFile(param_file, param) Failed to parse

问题 I am new to c++, when I was reading codes about caffe layers, I find functions like loss_weight_size() and add_loss_weight() . I searched the whole project and didn't find the definition of these functions. So what are these functions? template <typename Dtype> void LossLayer<Dtype>::LayerSetUp( const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) { // LossLayers have a non-zero (1) loss by default. if (this->layer_param_.loss_weight_size() == 0) { this->layer_param_.add_loss

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 am trying to prepare my data set for fully convolutional network. I've looked through some data sets and I'm having a really hard time figuring out how to format it. For instance, in the Kitti data set, there are these 2 images and this text file in the training folder : image 1 image 2 text P0: 7.215377000000e+02 0.000000000000e+00 6.095593000000e+02 0.000000000000e+00 0.000000000000e+00 7.215377000000e+02 1.728540000000e+02 0.000000000000e+00 0.000000000000e+00 0.000000000000e+00 1

问题 I am following this tutorial Face detection with OpenCV and deep learning to create and face detection software using OpenCV3, Caffe and Python3. This is the used code: # USAGE # python detect_faces.py --image rooster.jpg --prototxt deploy.prototxt.txt --model res10_300x300_ssd_iter_140000.caffemodel # import the necessary packages import numpy as np import argparse import cv2 # construct the argument parse and parse the arguments ap = argparse.ArgumentParser() ap.add_argument("-i", "--image"