版权声明:本文为博主原创文章,未经博主允许不得转载。 https://blog.csdn.net/sinat_31802439/article/details/52604972
本文介绍如何在Jeston TX1上编译运行python版本的Faster R-CNN代码
1.安装相关依赖库
$ sudo apt-get install libatlas-base-dev libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler libboost-all-dev libgflags2 libgflags-dev libgoogle-glog-dev liblmdb-dev libyaml-dev $ sudo apt-get install python-numpy python-setuptools python-pip cython python-opencv python-skimage python-protobuf $ sudo pip install easydict PyYAML2.克隆源码
$ cd py-faster-rcnn/lib $ sed -i -e 's/lib64/lib/g' setup.py $ make $ sed -i -e '1617s/__pyx_t_5numpy_int32_t/int/g' nms/gpu_nms.cpp $ make
3.复制修改Cmake.config文件
$ ../caffe-fast-rcnn/ $ cp Makefile.config.example Makefile.config
USE_CUDNN := 1 WITH_PYTHON_LAYER := 1
编译caffe
make all -j3
make pycaffe -j34.下载模型文件
cd $FRCN_ROOT./data/scripts/fetch_fast_rcnn_models.sh
5.测试运行demo
cd $FRCN_ROOT./tools/demo.py
结果:
ZF网络训练模型:
参考:http://www.cnblogs.com/louyihang-loves-baiyan/p/4885659.html?utm_source=tuicool&utm_medium=referral
http://qiita.com/kndt84/items/a32d07350ad8184ea25e
http://blog.csdn.net/jiajunlee/article/details/50373815