tensorflow implementation of 'YOLO : Real-Time Object Detection'(train and test)
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README.md

tensorflow-yolo

Require

tensorflow-1.0

download pretrained model

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

	mv yolo_tiny.ckpt models/pretrain/ 

Train

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

train

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