tensorflow implementation of 'YOLO : Real-Time Object Detection'(train and test)
Switch branches/tags
Nothing to show
Clone or download
Latest commit b68ddcf Mar 26, 2017
Failed to load latest commit information.
conf first commit Oct 12, 2016
data first commit Oct 12, 2016
models first commit Oct 12, 2016
tools first commit Oct 12, 2016
yolo fix yolo_net loss bug Mar 15, 2017
README.md adapt readme Mar 26, 2017
cat.jpg first commit Oct 12, 2016
demo.py Revert "Python3" Feb 21, 2017





download pretrained model

yolo_tiny: https://drive.google.com/file/d/0B-yiAeTLLamRekxqVE01Yi1RRlk/view?usp=sharing

	mv yolo_tiny.ckpt models/pretrain/ 


Train on pascal-voc2007 data

Download pascal-Voc2007 data
  1. Download the training, validation and test data

    wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
    wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
  2. Extract all of these tars into one directory named VOCdevkit

    tar xvf VOCtrainval_06-Nov-2007.tar
    tar xvf VOCtest_06-Nov-2007.tar
  3. It should have this basic structure

    $VOCdevkit/                           # development kit
    $VOCdevkit/VOCcode/                   # VOC utility code
    $VOCdevkit/VOC2007                    # image sets, annotations, etc.
    # ... and several other directories ...
  4. Create symlinks for the PASCAL VOC dataset

    cd $YOLO_ROOT/data
    ln -s $VOCdevkit VOCdevkit2007

    Using symlinks is a good idea because you will likely want to share the same PASCAL dataset installation between multiple projects.

convert the Pascal-voc data to text_record file

python tools/preprocess_pascal_voc.py


python tools/train.py -c conf/train.cfg

Train your customer data

  1. transform your training data to text_record file(the format reference to pascal_voc)

  2. write your own train-configure file

  3. train (python tools/train.py -c $your_configure_file)

test demo

python demo.py