yolo caffe

ippicv_linux_20151201.tgz this thing, 3.x version of the needPit two "compile Caffe":Caffe compilation requires a lot of dependent libraries, so the installation process of a variety of wonderful problems is normal. Here is the Caffe installation:Main reference URL: Reference site: http://blog.csdn.net/muzilinxi90/article/details/53673184　　Note:git clone https:/

STEP1: Installing homebrewIf there is a computer, temporarily do not install. However, if you still get an error after adding sudo to step2 (or any other situation that requires brew), you will need to reinstall homebrew. Enter the following command in the terminal:Ruby-e "$ (Curl-fssl https://raw.githubusercontent.com/Homebrew/install/master/intall)" to install.(reference URL: http://blog.csdn.net/yanzi1225627/article/details/42113661)STEP2: Installing CMakeAfter installing the homebrew, enter

Recently began to try deep learning things, brother recommended Caffe, began to try how to configure the Caffe to run the required environment.OK, get started.Find the Installation guide for Caffe official website: http://caffe.berkeleyvision.org/ Installation.html, really be installed in the pre-conditions scared a bit, a good lump, no way, installation needs, b

Ubuntu14.04+caffe+cuda Environment Building and training and testing of mnist data setsFirst, the installation of ubuntu14.04:　　Ubuntu installation is a very simple thing, here is a reference tutorial:Http://jingyan.baidu.com/article/76a7e409bea83efc3b6e1507.htmlSecond, the installation of Cuda:1, first download NVIDIA CUDA warehouse installation package (my is Ubuntu 14.04 64 bit, so download is ubuntu14.04 installation package, if you are 32 bit can

52263711Learning to use the framework of deep learning, the need to build Caffe, Theano and torch framework. After one months of unremitting struggle, finally set up the framework. Now share the simple build process, save time for the students to use the deep learning framework later, write this blog. Because there are a variety of problems with the framing process, there are hundreds of combinations of different hardware (such as laptops, desktops),

1. Installing Python:yum Install python-devel.x86_642. Install some Python library dependencies (these are the ones we found to be missing after debugging, resulting in a Python command mode import caffe error)Yum Install python-matplotlib.x86_64#pip Install Scikit-imagesudo easy_install-u scikit-image3. Install other Python dependenciesCD $CAFFE _root/pythonFor req in $ (cat requirements.txt); do pip insta

Deep Network (NET) is a composite model that is composed of many interconnected layers (layers). Caffe is the establishment of a deep network of such a tool, according to a certain strategy, a layer of one layer to build their own model. It defines all information data as blobs for convenient operation and communication. BLOBs are a standard array in the Caffe framework, a unified memory interface that desc

1 Ubuntu System Installation Ubuntu 14.04:http://www.releases.ubuntu.com/14.04/ You can download it directly by clicking the address below Http://www.releases.ubuntu.com/14.04/ubuntu-14.04.5-desktop-amd64.iso 2 Caffe Environment Installation General dependencies sudo apt-get install Libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf- Compiler sudo apt-get install--no-install-recommends

Caffe Code Guide (4): Data Set preparationCaffe There are two simple examples: Mnist and CIFAR-10, the former is used for handwritten numeral recognition, the latter for small image classification. These two datasets can be downloaded in the CAFFE source framework using scripts (caffe_root/data/mnist/get_mnist.sh and caffe_root/data/cifar10/get_cifar10.sh), as shown in:[Plain]View Plaincopyprint? $./get

Write in front:Caffe has many network layers, the latest version of the code has covered many types of network layer, however, sometimes for a variety of reasons, its given network layer can not meet our requirements, it is necessary to change it to meet their own needs, thanks to the author of Open source code and many code maintainers.Since the network layer in Caffe is given the layer base class directly or indirectly, when we need to add a new typ

Simply record your own use of the Caffe process and some of the problems encountered.Download Caffe and installation is not described in detail, you can refer to http://caffe.berkeleyvision.org/installation.html.Here's the process of preparing the dataset and training reference Imagenet: refer to Http://drubiano.github.io/2014/06/18/caffe-custom-data.html1. Divid

Because of Python's flexibility, it is more convenient to use the Python layer when adding a layer of our own definition in Caffe, and the speed of development will be faster than C + +. Now I'll just talk about it here. How to add a custom Python layer in Caffe (lenet structure when using the original network structure):First add your own defined layer function. py file in the

a few overviews of layers layers is one of the most complex and responsible components in Caffe .。 From data loading (Input layer), convolution calculation (Conv layer), the lower sampling of feature map (pooling layer), the introduction of network nonlinearity (Relu layer,sigmoid layer) or probability (Softmax Layer) and loss calculations (Softmax loss layer), all of these complex tasks are done by layers. layers is the basic computing unit in

Turn from: http://blog.csdn.net/liuheng0111/article/details/53090473 http://blog.csdn.net/thesby/article/details/51264439 Caffe uses the Boost.python module in boost to support the use of Python definition layer: adding new layer with C + + is cumbersome, time-consuming, and easily error-prone between development speed and execution speed trade-off Compile support for Python layer Caffe If this is the fir

First, the prefaceWant to write their own layers, First you have to define the parameters of your own layer in the Caffe.proto, so that you can configure the parameters in the proto configuration file, and then you have to declare in Caffe.proto that the parameters of your layer are optional, and then you have to add your own HPP header files to the Caffe include directory and Caffe SRC under the layer dire

As a beginner of Caffe, the feature extraction from the official Python tutorial is useful; but about the use of C++API, find some information, said is not a perfect feature; project requirements, get imagenet image in Caffenet network structure of the FC7 layer characteristics. Environment: Window7+caffeLanguage: C + +Objective: To test the characteristics of a layer of image and to saveCan directly execute the following command, (note that the offic

Reference article: https://www.cnblogs.com/yizhichun/p/6339789.html reliance on The prerequisite caffe has been compiled correctly. See Ubuntu Configuration Caffe Gallery sudo apt-get install python-pip sudo apt-get install python-dev python-numpy sudo apt-get ll Gfortran sudo pip install–r python/requirements.txt sudo pip install Pydot numpy scipy matplotlib sklearn skimage h5py protobuf leveldb netwo

The data interface of Caffe mainly has original image (ImageData), HDF5, Lmdb/leveldb. Since the Caffe Lmdb interface only supports but label, for multiple label tasks, it is often necessary to use HDF5. However, Caffe for HDF5 data, the entire H5 file needs to be read in advance, which is not a problem for small data, and it saves the IO overhead of training in

train the model and save the log file Start by building a script file for the training data train.sh, which reads as follows, where 2>1 | Tee Examples/mnist/mnist_train_log.log is the log log file's Save directory. #!/usr/bin/env sh set-e tools=./build/tools $TOOLS/caffe train --solver=examples/mnist/lenet_ Solver.prototxt 2>1 | tee examples/mnist/mnist_train_log.log After training is completed, the Mnist_train_log.log log is genera

1. Download the great God mtcnn source, including CaffeHttps://github.com/DaFuCoding/MTCNN_Caffe2. Installation of Caffe dependencies2.1 Basic packages and installation required for installation and development Caffe the dependency. First, install some basic packages needed for development: sudo apt-get install build-essential. If the essential package is unavailable, you can perform the following command t