caffe

问题 I'm trying to build a CNN that classify object in 3 main classes.The three objects consist of a lamborghini , cylinder head and a piece of plane. My data set consists of 6580 images , almost 2200 image for each class.You can see my dataset on google drive dataset. The architecture of my CNN is AlexNet , but I've modified the output of fully connected layer 8 from 1000 to 3. I have used these settings for training test_iter:1000 test_interval:1000 base_lr:0.001 lr_policy:"step" gamma:0.1

问题 I'm trying to build caffe with the python wrapper on Mac OSX 10.0, but keep getting the following error when I execute the command: make runtest (make all -j8 and make test work fine). Check failed: error == cudaSuccess (35 vs. 0) CUDA driver version is insufficient for CUDA runtime version I have updated the CUDA driver to the latest version online. I also tried uninstalling and reinstalling CUDA and the driver, but the error still persists. How can I solve this? 回答1: As was teased out in

问题 I'm trying to build caffe with the python wrapper on Mac OSX 10.0, but keep getting the following error when I execute the command: make runtest (make all -j8 and make test work fine). Check failed: error == cudaSuccess (35 vs. 0) CUDA driver version is insufficient for CUDA runtime version I have updated the CUDA driver to the latest version online. I also tried uninstalling and reinstalling CUDA and the driver, but the error still persists. How can I solve this? 回答1: As was teased out in

问题 I'm using alexnet to train my own dataset. The example code in caffe comes with bvlc_reference_caffenet.caffemodel solver.prototxt train_val.prototxt deploy.prototxt When I train with the following command: ./build/tools/caffe train --solver=models/bvlc_reference_caffenet/solver.prototxt I'd like to start with weights given in bvlc_reference.caffenet.caffemodel. My questions are How do I do that? Is it a good idea to start from the those weights? Would this converge faster? Would this be bad

问题 I can calculate the receptive field size of 500 x 500 input image for VGGNet. The receptive field sizes are as follow. Layer Name = conv1, Output size = 500, Stride = 1, RF size = 3 Layer Name = relu1_1, Output size = 500, Stride = 1, RF size = 3 Layer Name = conv1_2, Output size = 500, Stride = 1, RF size = 5 Layer Name = relu1_2, Output size = 500, Stride = 1, RF size = 5 Layer Name = pool1, Output size = 250, Stride = 2, RF size = 6 Layer Name = conv2_1, Output size = 250, Stride = 2, RF

问题 How can I get both the 4096 dim feature layer and the 1000 dim class layer in caffe after one forward pass using C++? I tried to look it up in extract_features.cpp but it uses some weird datum object, so I cannot really understand how it works. So far I was simply cropping my prototxt files up to the layer that I wanted to extract and used [...] net->ForwardPrefilled(); Blob<float> *output_layer = net->output_blobs()[0]; const float *begin = output_layer->cpu_data(); const float *end = begin

问题 I am trying to train my own network on Caffe, similar to Imagenet model. But I am confused with the crop layer. Till the point I understand about crop layer in Imagenet model, during training it will take random 227x227 image crops and train the network. But during testing it will take the center 227x227 image crop, does not we loose the information from image while we crop the center 227x27 image from 256x256 image? And second question, how can we define the number of crops to be taken

问题 I know that caffe has the so called spatial pyramid layer, which enables networks to use arbitrary image sizes. The problem I have is, that the network seems to refuse, to use arbitrary image sizes within a single batch. Do I miss something or is this the real problem?. My train_val.prototxt: name: "digits" layer { name: "input" type: "Data" top: "data" top: "label" include { phase: TRAIN } transform_param { scale: 0.00390625 } data_param { source: "/Users/rvaldez/Documents/Datasets/Digits

问题 I'm training two CNNs (AlexNet e GoogLeNet) in two differents DL libraries (Caffe e Tensorflow). The networks was implemented by dev teams of each libraries (here and here) I reduced the original Imagenet dataset to 1024 images of 1 category -- but setted 1000 categories to classify on the networks. So I trained the CNNs, varying processing unit (CPU/GPU) and batches sizes, and I observed that the losses converges fastly to near zero (in mostly times before 1 epoch be completed), like in this

问题 I am using Deep learning caffe framework for image classification. I have coins with faces. Some of them are left directed some of them are right. To classify them I am using common aproach - take weights and structure from pretrained ImageNet network that have already capture a lot of image patterns and train mostly the last layer to fit my training set. But I have found that netowork does not works on this set: I have taken some coin for example leftdirected , generated horizontally flipped