1. 安装tensorflow, python等，再安装以下工具

apt-get install protobuf-compiler python-pil python-lxml
pip install jupyter
pip install matplotlib

2. 安装protobuf

git clone https:///protocolbuffers/protobuf.git
cd protobuf
./autogen.sh
./configure
make
make install

  配置protobuf环境变量

vim ~/.bashrc
   export PATH=$PATH:/usr/local/bin
   export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
   export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/usr/local/lib/pkgconfig
source ~/.bashrc

3. 下载tensorflow models

git clone https:///tensorflow/models.git
cd models/research
protoc object_detection/protos/*.proto --python_out=.
export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
python setup.py install

  测试（成功会显示OK)

python object_detection/builders/model_builder_test.py

4. 将LabelImg标准的xml转为csv

git clone https:///datitran/raccoon_dataset.git
cd raccoon_dataset
python xml_to_csv.py

5. 生成TFRecord

python generate_tfrecord.py --csv_input=data/train_labels.csv --output_path=data/train.record --image_dir=images/
python generate_tfrecord.py --csv_input=data/test_labels.csv --output_path=data/test.record --image_dir=images/