/yolo2_bv

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YOLOv2 in PyTorch

This is a PyTorch implementation of YOLOv2. This project is mainly based on darkflow and darknet.

For details about YOLO and YOLOv2 please refer to their project page and the paper: YOLO9000: Better, Faster, Stronger by Joseph Redmon and Ali Farhadi.

I used a Cython extension for postprocessing and multiprocessing.Pool for image preprocessing. Testing an image in VOC2007 costs about 13~20ms.

NOTE: This is still an experimental project. VOC07 test mAP is about 0.71 (trained on VOC07+12 trainval, reported by @cory8249). See longcw/yolo2-pytorch#1 and longcw/yolo2-pytorch#23 for more details about training.

BTW, I recommend to write your own dataloader using torch.utils.data.Dataset since multiprocessing.Pool.imap won't stop even there is no enough memory space.

Installation and demo

  1. Clone this repository

    git clone git@github.com:longcw/yolo2-pytorch.git

  2. Build the reorg layer (tf.extract_image_patches)

    cd yolo2-pytorch
./make.sh

  3. Download the trained model yolo-voc.weights.h5 and set the model path in demo.py

  4. Run demo python demo.py.

Training YOLOv2

You can train YOLO2 on any dataset. Here we train it on VOC2007/2012.

  1. Download the training, validation, test data and VOCdevkit

    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
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCdevkit_08-Jun-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
tar xvf VOCdevkit_08-Jun-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. Since the program loading the data in yolo2-pytorch/data by default, you can set the data path as following.

    cd yolo2-pytorch
mkdir data
cd data
ln -s $VOCdevkit VOCdevkit2007

  5. Download the pretrained darknet19 model and set the path in yolo2-pytorch/cfgs/exps/darknet19_exp1.py.

  6. (optional) Training with TensorBoard.

    To use the TensorBoard, install Crayon (https://github.com/torrvision/crayon) and set use_tensorboard = True in yolo2-pytorch/cfgs/config.py.

  7. Run the training program: python train.py.

Evaluation

Set the path of the trained_model in yolo2-pytorch/cfgs/config.py.

cd faster_rcnn_pytorch
mkdir output
python test.py