深度学习caffe-SSD配置

1. 首先去github上下载好代码，如果之前配置好了caffe，直接将makefile.config文件粘贴到caffe-ssd目录下即可。
   我们的makefile文件如下，大家可以参考一下，编译没有问题。：

## Refer to http://caffe.berkeleyvision.org/installation.html# Contributions simplifying and improving our build system are welcome!# cuDNN acceleration switch (uncomment to build with cuDNN).USE_CUDNN := 1# CPU-only switch (uncomment to build without GPU support).# CPU_ONLY := 1# uncomment to disable IO dependencies and corresponding data layersUSE_OPENCV := 1# USE_LEVELDB := 0USE_LMDB := 1# uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary)#   You should not set this flag if you will be reading LMDBs with any#   possibility of simultaneous read and write# ALLOW_LMDB_NOLOCK := 1# Uncomment if you're using OpenCV 3# OPENCV_VERSION := 3# To customize your choice of compiler, uncomment and set the following.# N.B. the default for Linux is g++ and the default for OSX is clang++ CUSTOM_CXX := g++# CUDA directory contains bin/ and lib/ directories that we need.CUDA_DIR := /usr/local/cuda# On Ubuntu 14.04, if cuda tools are installed via# "sudo apt-get install nvidia-cuda-toolkit" then use this instead:# CUDA_DIR := /usr# CUDA architecture setting: going with all of them.# For CUDA < 6.0, comment the lines after *_35 for compatibility.CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \             -gencode arch=compute_20,code=sm_21 \             -gencode arch=compute_30,code=sm_30 \             -gencode arch=compute_35,code=sm_35 \             -gencode arch=compute_50,code=sm_50 \             -gencode arch=compute_52,code=sm_52 \             -gencode arch=compute_61,code=sm_61# BLAS choice:# atlas for ATLAS (default)# mkl for MKL# open for OpenBlasBLAS := atlas#BLAS := open# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.# Leave commented to accept the defaults for your choice of BLAS# (which should work)!# BLAS_INCLUDE := /path/to/your/blas# BLAS_LIB := /path/to/your/blas# Homebrew puts openblas in a directory that is not on the standard search path# BLAS_INCLUDE := $(shell brew --prefix openblas)/include# BLAS_LIB := $(shell brew --prefix openblas)/lib# This is required only if you will compile the matlab interface.# MATLAB directory should contain the mex binary in /bin.# MATLAB_DIR := /usr/local# MATLAB_DIR := /Applications/MATLAB_R2012b.app# NOTE: this is required only if you will compile the python interface.# We need to be able to find Python.h and numpy/arrayobject.h.PYTHON_INCLUDE := /usr/include/python2.7 \        /usr/local/lib/python2.7/dist-packages/numpy/core/include# Anaconda Python distribution is quite popular. Include path:# Verify anaconda location, sometimes it's in root.# ANACONDA_HOME := $(HOME)/anaconda2# PYTHON_INCLUDE := $(ANACONDA_HOME)/include \        $(ANACONDA_HOME)/include/python2.7 \        $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \# Uncomment to use Python 3 (default is Python 2)# PYTHON_LIBRARIES := boost_python3 python3.5m# PYTHON_INCLUDE := /usr/include/python3.5m \#                 /usr/lib/python3.5/dist-packages/numpy/core/include# We need to be able to find libpythonX.X.so or .dylib.PYTHON_LIB := /usr/lib# PYTHON_LIB := $(ANACONDA_HOME)/lib# Homebrew installs numpy in a non standard path (keg only)# PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include# PYTHON_LIB += $(shell brew --prefix numpy)/lib# Uncomment to support layers written in Python (will link against Python libs)WITH_PYTHON_LAYER := 1# Whatever else you find you need goes here.INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serialLIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial# If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies# INCLUDE_DIRS += $(shell brew --prefix)/include# LIBRARY_DIRS += $(shell brew --prefix)/lib# Uncomment to use `pkg-config` to specify OpenCV library paths.# (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)# USE_PKG_CONFIG := 1# N.B. both build and distribute dirs are cleared on `make clean`BUILD_DIR := buildDISTRIBUTE_DIR := distribute# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171# DEBUG := 1# The ID of the GPU that 'make runtest' will use to run unit tests.TEST_GPUID := 0# enable pretty build (comment to see full commands)Q ?= @

2、如果要尝试官方给出的数据集，可以从，http://pan.baidu.com/s/1c1AwrRy下载，密码为ly70，比wget快很多。
这里感谢一下http://blog.csdn.net/u013738531/article/details/56678247

下载完之后解压放在/caffe-ssd/data下,这个时候要生成标签txt文件和lmdb文件了,一定要注意把/caffe-ssd/data/VOC0712路径下的create_list.sh文件里的路径修改一下,

root_dir=$HOME/下载/caffe/data/VOCdevkit/

3、接下来生成lmdb文件
先将craete_data.sh文件里的data路径改为真实存放VOC数据集的路径.
由于之前配置过一次caffe,直接在贾扬清大佬的github上clone下来的,不含有ssd,所以再一次配置caffe-ssd的时候,可能环境变量就出问题了,出现了这样:

Traceback (most recent call last):  File "/home/hyhuang/下载/caffe/data/VOC0712/../../scripts/create_annoset.py", line 7, in <module>    from caffe.proto import caffe_pb2ImportError: No module named caffe.protoTraceback (most recent call last):  File "/home/hyhuang/下载/caffe/data/VOC0712/../../scripts/create_annoset.py", line 7, in <module>    from caffe.proto import caffe_pb2ImportError: No module named caffe.proto

还有缺少model_libs的报错,然后我到/caffe-ssd/scripts/create_annoset.py里找了一下,大致跟caffe.proto有关,于是联想可能是caffe和python的接口没有配置好,于是

gedit ~/.bashrc

没有gedit的朋友用sudo apt-get install 安装一下.然后修改弹出的文档最后一行,将那里的路径改为自己的caffe-ssd下的python文件夹,即:

export PYTHONPATH=/home/hyhuang/下载/caffe/pythonsource ~/.bashrc

再次运行create_data.sh,成功运行,不再报错.