基于ubuntu14.04的Mobilenet_SSD环境搭建
Data: 2017.11.22
Author: cjh
Theme: 基于ubuntu14.04的Mobilenet_SSD环境搭建
Caffe for SSD：https://github.com/weiliu89/caffe/tree/ssd
MobileNet-SSD：https://github.com/chuanqi305/MobileNet-SSD
1. Caffe for SSD部分
git clone https://github.com/weiliu89/caffe.git caffe-ssd
cd caffe-ssd
git checkout ssd
cpMakefile.config.example Makefile.config
vim Makefile.config
修改：
USE_CUDNN := 1
CUDA_DIR := /usr/local/cuda-8.0
WITH_PYTHON_LAYER := 1
错误：
解决：
升级g++ 和gcc
sudo add-apt-repositoryppa:ubuntu-toolchain-r/test
sudo apt-get update
sudo apt-get install gcc-5 g++-5
sudo update-alternatives --install/usr/bin/gcc gcc /usr/bin/gcc-5 60 --slave /usr/bin/g++ g++ /usr/bin/g++-5
修改MakeFile:
-COMMON_FLAGS += $(foreachincludedir,$(INCLUDE_DIRS),-isystem $(includedir)) #- means delete
+COMMON_FLAGS += $(foreachincludedir,$(INCLUDE_DIRS),-I$(includedir)) #+ means add
修改MakeFile.config:
-INCLUDE_DIRS := $(PYTHON_INCLUDE)/usr/local/include
-LIBRARY_DIRS := $(PYTHON_LIB)/usr/local/lib /usr/lib
+INCLUDE_DIRS := $(PYTHON_INCLUDE)/usr/local/include /usr/include/hdf5/serial/
+LIBRARY_DIRS := $(PYTHON_LIB)/usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial/
执行sudo apt-get installlibopenblas-dev
执行：
make clean
make –j8
make py
make test -j8
在home/usr目录下创建data目录下载数据集图片，这里只使用voc2007
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
解压
tar -xvf VOCtrainval_06-Nov-2007.tar
tar -xvf VOCtest_06-Nov-2007.tar
cd ~/caffe-ssd
//这里没有使用voc2012数据，需修改create_list.sh，将2012去掉
./data/VOC0712/create_list.sh
//生成LMDB文件
./data/VOC0712/create_data.sh
下载上述预训练文件
将MobileNet-SSD复制到caffe的example目录下
git clone https://github.com/chuanqi305/MobileNet-SSD.gitmobilenet-ssd
执行：python demo.py
可看见识别效果
训练：
建立训练集的软连接
ln -s PATH_TO_YOUR_TRAIN_LMDB trainval_lmdb
ln -s PATH_TO_YOUR_TEST_LMDB test_lmdb
创建lmdb文件的同时会生成labelmap.prototxt，并将其复制到当前文件夹，即MobileNet-SSD目录
执行gen_model.sh生成自己的training prototxt，此时会生成example目录
4.Download the training weights from thelink above, and run train.sh, after about 30000 iterations, the loss should be1.5 - 2.5.
5.Run test.sh to evaluate the result.
6.Run merge_bn.py to generate your owndeploy caffemodel.
记得修改文件的对应路径和名称
