yolo caffe

Comparison between Caffe, TensorFlow, and MXnet open source libraries Recently, Google opened up its internal deep learning framework TensorFlow [1] and discussed the three open-source libraries in combination with the open-source MXNet [2] and Caffe [3, among them, only Caffe has carefully read the source code. The other two libraries only read the official docu

Importerror:no module named Skimage "Issue 1" for dual-card notebook only, through the *.run way to install Cuda, after the restart will be black screen appears unable to enter the desktop, only into the TTY workaround : Use the *.deb method to install CUDA, do not need to install the Nvidia driver (CUDA-7.0 comes with the latest driver), but also do not need to shut down the LIGHTDM service into the TTY terminal to install. Download the Deb offline package installation at Cuda official website

After long-term verification, this blog installed caffe success, in order to facilitate everyone and themselves, recorded, to the later reference1. Install dependent packages2. Disable the original graphics driver Nouveau3. Download cuda8.04, Installation cuda8.05, Installation Cudnn6. Configure Environment variables7, installation OpenCV8, installation Caffe9, install Pycaffe interface environmentThe first step installs the dependency packageInstall

after successfully compiling the Caffe source code, you can use Caffe in a Python environment. In an Ubuntu environment, Importerror:no module named may appear when you open the Python interpreter and enter import Caffe Caffe >>>import Caffe Traceback (most recent call

This article is in the implementation of GitHub on the user Farmingyard posted accelerated version shufflenet. The following are the included files: As a small white in the depth of learning, the beginning is really confused, in the previous Caffe framework used, but simply will put someone else's deploy.prototxt,train.prototxt, Solver.prototxt to use, make a data set run, a little bit of change, for example, some network GitHub only to a deploy.prot

Consolidated Source: angle_cal 2016-12-19 17:32 624 ℃ 0 Reviews The BLVC version of Caffe-windows already supports visual Studio 2015, and the following configuration process is integrated with the experience of others and is validated to ensure effectiveness. Download Caffe-windows (BLVC): GitHubDownload good unzip. install vs2015,cuda,cudnn,anaconda,cmake VS2015 Installation Please do it you

http://blog.csdn.net/naaaa/article/details/52118437Tags: windowsvs2013caffecifar102016-08-04 15:33 1316 People read Comments (1) favorite reports Classification:CaffeCopyright NOTICE: This article for Bo Master original article, without Bo Master permission not reproduced.1. Download vs2013, installHttp://download.microsoft.com/download/0/7/5/0755898A-ED1B-4E11-BC04-6B9B7D82B1E4/VS2013_RTM_ULT_CHS.iso2. Download Caffe source code, unzipHttps://gith

Original linkDeep Neural Network (DNN) training is a computationally intensive project that takes days or weeks to complete on a modern computing platform. In a recent article on Intel? Xeon? In single-node Caffe scoring and training for the E5 product family, we demonstrated a 10 times-fold performance improvement in the caffe* framework based on the AlexNet topology and reduced the single-node training ti

Has not been the habit of blogging, and later found that the previous work if not pay attention to timely collation and records are often lost quickly. For me this is an important article, good habits to persevere, future days I will be resident blog Park! Because this cock level is limited, the IQ is slightly low, welcome big God come to shoot brick. End of nonsense, here is the dry goods:First of all, I spit a bit of Ben's notebook, my current notebook is still a freshman bought Dell INSPIRON

Building environmental referenceshttp://blog.csdn.net/ubunfans/article/details/47724341This tutorial is basically correct.One thing to add isMake All-j4 After that, a lot of *.bin files are generated below build/bin/to prove that the compilation was successful.The following is the run Mnist, performed to create_mnist.sh this step of the time encountered a problem:./create_mnist.sh:build/examples/mnist/convert_mnist_data.bin:not foundIt's going to change the catalog.Note: The new

Refer to the two-bit bloghttp://caffe.berkeleyvision.org/official websitehttp://blog.csdn.net/u013476464/article/details/38071075 caffe+ubunutu14.04 +cuda 6.5 Installation Guidehttp://blog.csdn.net/bebelemon/article/details/25567239 ubuntu12.04 under Configuration CaffeHttp://www.mintos.org/config/ubuntu-nvidia-prime.html (important for ubuntu14.04 's words)Error while loading shared libraries:xxx.so.x "causes and workarounds for" errorsThe 20+ has be

anexplicitspecialization Solution: Download NCVPIXELOPERATIONS.HPP, replace ubuntu/2.4/opencv/opencv2.4.9/moduels/gpu/src/nvidia/ NCPIXELOPERATIONS.HPP file in the core directory, re-execute the installation commandsudo sh./opencv2_4_9.shV. Installing other dependenciesUbuntu14.04 User Executionsudo apt-get install Libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev Libhdf5-serial-dev Libgflags-dev Libgoogle-glog-dev Liblmdb-dev Protobuf-compilerOther version

In the process of training and testing data sets using open-source deep learning Framework (Caffe), we will inevitably want to visualize some training data in our training process, this article mainly introduces how to use the tools of Caffe to visualize the error curve and the precision curve in the course of CIFAR10 training and testing. 0. Preparation, the CIFAR data set has been downloaded, and the form

First step, install Pycaffe Notebook interface Environment After the successful installation of Caffe in the previous step, it is possible to do the training data set through Caffe or predict various related things, just need to operate through the Caffe command at the command line, and this step Pycaffe installation and notebook environment configuration is ju

Caffe Picture feature Extraction (python/c++) 1. Caffe feature Extraction (c + + implementation)The Caffe framework provides the appropriate tools (Build/tools/extract_features.bin) tool extract features, the official tutorial, using the following methods: Extract_features.bin Xxx.caffemodel xxxx.prototxt layer-name output-path mini-batches Db-styleXxx.c

1.mnist instances# #1. The data download obtains mnist packets and executes the./data/mnist/get_mnist.sh script in the Caffe root directory. The get_mnist.sh script first downloads the sample library and unzip it to get four files.2. Generate LmdbAfter successfully extracting the downloaded sample library, then execute the./examples/mnist/create_mnist.sh. The create_mnist.sh script first takes advantage of the Convert_mnist_data.bin tool in the

About the installation of Caffe Baidu, tutorials flying around, but a little mention, dual-system dual-card (notebook) in the Ubuntu installation Nvidia graphics graphics interface may hang up, the problem in Ubuntu 16.04 get a preliminary solution, there is a notebook installed on the Internet Caffe have mentioned, But I can't find it.Debug editor for Virtual Studio code Microsoft Production Editor. It's v

with the unique display.After installation Be sure to test the driver and Cuda has not been installed successfully, there are many online tutorials, nvidia-smi command test drive, there is a test cuda do not remember.3. Install CUDNN, reference: http://blog.csdn.net/baidu_32173921/article/details/53510764http://blog.csdn.net/ai_smith/article/details/53000973http://blog.csdn.net/samylee/article/details/509226014. Now is the beginning to configure the Caffe