nilboy / Tensorflow Yolo
tensorflow implementation of 'YOLO : Real-Time Object Detection'(train and test)
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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
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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
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Extract all of these tars into one directory named
VOCdevkit
tar xvf VOCtrainval_06-Nov-2007.tar tar xvf VOCtest_06-Nov-2007.tar
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It should have this basic structure
$VOCdevkit/ # development kit $VOCdevkit/VOCcode/ # VOC utility code $VOCdevkit/VOC2007 # image sets, annotations, etc. # ... and several other directories ...
-
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
-
transform your training data to text_record file(the format reference to pascal_voc)
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write your own train-configure file
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train (python tools/train.py -c $your_configure_file)
test demo
python demo.py
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