quhezheng / caffe_yolo_v2

YOLO v2 code ported to Caffe

YOLO: Real-Time Object Detection (Caffe)

This is the caffe version YOLO V2 ported directly from darknet YOLO

Detection Using A Pre-Trained VOC Model

• Step 0 BUILD this reporsitory caffe

  git clone https://github.com/quhezheng/caffe_yolo_v2
  cd caffe_yolo_v2
  mkdir build
  cd build
  cmake ..
  make  
  

• Step 1 Download the trained model from Baidu disk https://pan.baidu.com/s/1jJ9emNW

  cd ..  #back to project root folder
  cd examples/yolo
  mkdir model_voc
  cp DOWNLOAD/PATH/OF/yolo_voc_iter_120000.caffemodel model_voc
  python detect.py
  

  

  detect.py use CPU do do predition by default, please change the script if want GPU

Train the VOC data

• Step 0 Build caffe in [YOUR_LOCAL_REPOSITORY_PATH]/build

• Step 1 Download VOC2007 & 2012 dataset(here)

  cd [YOUR_LOCAL_REPOSITORY_PATH]/data/yolo
  wget http://pjreddie.com/media/files/VOCtrainval_06-Nov-2007.tar
  wget http://pjreddie.com/media/files/VOCtest_06-Nov-2007.tar
  wget http://pjreddie.com/media/files/VOCtrainval_11-May-2012.tar
  wget http://pjreddie.com/media/files/VOC2012test.tar
  tar -xvf VOCtest_06-Nov-2007.tar
  tar -xvf VOCtrainval_06-Nov-2007.tar
  tar -xvf VOCtrainval_11-May-2012.tar
  tar -xvf VOC2012test.tar

  There it will be 'VOCdevkit' folder

• Step 2 Create lmdb index *.txt files

  python get_list.py
  

  There it will be 'trainval.txt test_2007.txt test_2012.txt' files

• Step 3 Create lmdb

  ./convert.sh
  

  There it will be lmdb folder has all lmdb

• Step 4 Download the pre-trained model darknet19_448.conv.23.caffemodel from Baidu disk

  This model is converted directly from darknet darknet19_448.conv.23. It contain trained TOP 23 layers' weight, other layers' weight are initilized by 'xavier'

  cd ../../examples/yolo
  cp DOWNLOAD/PATH/OF/darknet19_448.conv.23.caffemodel ./
  mkdir model_voc
  ./train_voc.sh