开源Fast R-CNN代码实现物体识别[通俗易懂]

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参考链接：https://blog.csdn.net/linolzhang/article/details/70306003

1.资源链接

代码下载链接：https://github.com/CharlesShang/TFFRCNN

训练好的网络下载链接：

在 TFFRCNN-master 下新建文件夹 model，存放要下载入的 net（参考 Github 下载地址），推荐下载：

2.VGG16 – TFFRCNN (0.689 mAP on VOC07)：https://drive.google.com/file/d/0B_xFdh9onPagX0JWRlR0cTZ5OGc/view

3.VGG16 – TFFRCNN (0.748 mAP on VOC07)：https://drive.google.com/file/d/0B_xFdh9onPagVmt5VHlCU25vUEE/view

5.Resnet50 – TFFRCNN (0.712 mAP on VOC07)：https://drive.google.com/file/d/0B_xFdh9onPagbXk1b0FIeDRJaU0/view

2.Requirements: software

1. Requirements for Tensorflow (see: Tensorflow)
2. Python packages you might not have: cython, python-opencv, easydict (recommend to install: Anaconda）

3.Requirements: hardware

1. For training the end-to-end version of Faster R-CNN with VGG16, 3G of GPU memory is sufficient (using CUDNN)

4.Installation (sufficient for the demo)

1.Clone the Faster R-CNN repository

git clone https://github.com/CharlesShang/TFFRCNN.git

2.Build the Cython modules

           cd TFFRCNN/lib
           make # compile cython and roi_pooling_op, you may need to modify make.sh for your platform

我在编译过程中遇到了一些错误需要修改lib/make.sh文件(修改如下图红色部分), 我的文件内容如下：

—– Begin ——

#!/usr/bin/env bash TF_INC=(python -c ‘import tensorflow as tf; print(tf.sysconfig.get_include())’) echo TF_INC

TF_LIB=(python -c ‘import tensorflow as tf; print(tf.sysconfig.get_lib())’) echo TF_LIB

CUDA_PATH=/usr/local/cuda/

cd roi_pooling_layer

/usr/local/cuda-9.0/bin/nvcc -std=c++11 -c -o roi_pooling_op.cu.o roi_pooling_op_gpu.cu.cc \ -I $TF_INC -D GOOGLE_CUDA=1 -x cu -Xcompiler -fPIC -arch=sm_52

## if you install tf using already-built binary, or gcc version 4.x, uncomment the two lines below #g++ -std=c++11 -shared -D_GLIBCXX_USE_CXX11_ABI=0 -o roi_pooling.so roi_pooling_op.cc \ # roi_pooling_op.cu.o -I TF_INC -fPIC -lcudart -L CUDA_PATH/lib64

# for gcc5-built tf #g++ -std=c++11 -shared -D_GLIBCXX_USE_CXX11_ABI=1 -o roi_pooling.so roi_pooling_op.cc \g++ -std=c++11 -shared -D_GLIBCXX_USE_CXX11_ABI=0 -o roi_pooling.so roi_pooling_op.cc \ roi_pooling_op.cu.o -I TF_INC -fPIC -lcudart -L CUDA_PATH/lib64 -L/usr/local/lib/python2.7/dist-packages/tensorflow -ltensorflow_framework \ -L/usr/local/cuda-9.0/targets/x86_64-linux/lib/ -L

# add building psroi_pooling layer cd psroi_pooling_layer /usr/local/cuda-9.0/bin/nvcc -std=c++11 -c -o psroi_pooling_op.cu.o psroi_pooling_op_gpu.cu.cc \ -I $TF_INC -D GOOGLE_CUDA=1 -x cu -Xcompiler -fPIC -arch=sm_52

g++ -std=c++11 -shared -D_GLIBCXX_USE_CXX11_ABI=0 -o psroi_pooling.so psroi_pooling_op.cc \ psroi_pooling_op.cu.o -I TF_INC -fPIC -lcudart -L CUDA_PATH/lib64i -L/usr/local/lib/python2.7/dist-packages/tensorflow -ltensorflow_framework \ -L/usr/local/cuda-9.0/targets/x86_64-linux/lib/ -L

## if you install tf using already-built binary, or gcc version 4.x, uncomment the two lines below #g++ -std=c++11 -shared -D_GLIBCXX_USE_CXX11_ABI=0 -o psroi_pooling.so psroi_pooling_op.cc \ # psroi_pooling_op.cu.o -I TF_INC -fPIC -lcudart -L CUDA_PATH/lib64

cd ..

—– End —–

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