Designed By Shifeng Zhang
- Get the SSD code. We will call the directory that you cloned Caffe into
caffe-sfd
git clone https://github.com/weiliu89/caffe.git caffe-sfd
cd caffe-sfd
git checkout ssd- Build the code. Please follow Caffe instruction to install all necessary packages and build it.
- I follow this blog, and it works on my ubuntu 16.4, 1080ti, cuda 8.0, cudnn5.
# 1. Modify Makefile.config according to your Caffe installation.
cp Makefile.config.example Makefile.config
# 2. Rewrite Makefile.config
gedit Makefile.config
1.若使用cudnn，取消“# USE_CUDNN := 1” 前的注释即：USE_CUDNN := 1
2.若使用opencv3.x，取消“# OPENCV_VERSION := 3” 前的注释，即：OPENCV_VERSION := 3
3.取消“# WITH_PYTHON_LAYER := 1” 前的注释。即 WITH_PYTHON_LAYER := 1
4.加入hdf5的目录：
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
修改为：
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu /usr/lib/x86_64-linux-gnu/hdf5/serial
5.这一步我是加了，但是有些人没加。。。
PYTHON_INCLUDE := /usr/include/python2.7 \
/usr/lib/python2.7/dist-packages/numpy/core/include
修改为：
PYTHON_INCLUDE := /usr/include/python2.7 \
/usr/local/lib/python2.7/dist-packages/numpy/core/include
6.防止打印烦人的警告，CUDA8版本有点高，不支持compute_20
CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \
-gencode arch=compute_20,code=sm_21 \
-gencode arch=compute_30,code=sm_30 \
-gencode arch=compute_35,code=sm_35 \
-gencode arch=compute_50,code=sm_50 \
-gencode arch=compute_52,code=sm_52 \
-gencode arch=compute_61,code=sm_61
修改为：
CUDA_ARCH := -gencode arch=compute_30,code=sm_30 \
-gencode arch=compute_35,code=sm_35 \
-gencode arch=compute_50,code=sm_50 \
-gencode arch=compute_52,code=sm_52 \
-gencode arch=compute_61,code=sm_61
删除这两行即可：
-gencode arch=compute_20,code=sm_20 \
-gencode arch=compute_20,code=sm_21 \
删除这两行即可：
-gencode arch=compute_20,code=sm_20 \
-gencode arch=compute_20,code=sm_21 \
# 3. Rewrite Makefile
gedit Makefile
LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_hl hdf5
修改为：
LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serial
NVCCFLAGS +=-ccbin=$(CXX) -Xcompiler-fPIC $(COMMON_FLAGS)
修改为：
NVCCFLAGS += -D_FORCE_INLINES -ccbin=$(CXX) -Xcompiler -fPIC $(COMMON_FLAGS)
LIBRARIES += boost_thread stdc++后加boost_regex
修改为:
LIBRARIES += boost_thread stdc++ boost_regex
# 4. Add caffe-sfd/python to your ~/.bashrc
sudo gedit ~/.bashrc
export PYTHONPATH=~/caffe-sfd/python:$PYTHONPATH
source ~/.bashrc
# 5. Make
make -j8
# Make sure to include $CAFFE_ROOT/python to your PYTHONPATH.
make py
make test -j8
# (Optional)
make runtest -j8-
Download fully convolutional reduced (atrous) VGGNet.
By default, we assume the model is stored in$CAFFE_ROOT/examples/s3fd/ -
Create the LMDB file.
cd $CAFFE_ROOT
# Create the trainval.txt, test.txt, and test_name_size.txt in data/FACE/
./data/FACE/create_list.sh
# You can modify the parameters in create_data.sh if needed.
# It will create lmdb files for trainval and test with encoded original image:
# - $HOME/data/faces_database/FACE/lmdb/FACE_trainval_lmdb
# - $HOME/data/faces_database/FACE/lmdb/FACE_test_lmdb
# and make soft links at examples/VOC0712/
./data/FACE/create_data.sh- Train your model .
./build/tools/caffe train --solver examples/s3fd/solver.prototxt --gpu 1 --weights examples/s3fd/VGG_ILSVRC_16_layers_fc_reduced.caffemodel
-
ROC of FDDB compared with official, as follow:
-
ROC of FDDB compared with SSH/MTCNN, as follow:
-
examples
