YOLOv2-tiny in PyTorch

1. Install gpu driver (ubuntu 20.04)

• NVIDIA-Linux-x86_64-460.80.run (gcc 9.x)
• cuda_9.0.176_384.81_linux.run (gcc 6.x)
• cudnn-9.0-linux-x64-v7.6.5.32.tgz

2. Install gcc 6

echo "deb http://archive.ubuntu.com/ubuntu bionic main universe" >> /etc/apt/sources.list
sudo apt update
sudo apt install g++-6
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-6 3

3. Install anaconda & create conda environment

conda create -n yolo2-tiny python=3.6 pip
conda activate yolo2-tiny
pip install -r requirement

4. Build project files

./make.sh

5. Run

python demo.py
python train.py
python test.py

Make the training, validation, test data

mkdir -p data/images/0
mkdir -p data/imagesets/0
mkdir -p data/xml/0
nano data/labels.txt
nano data/imagesets/0/test.txt
nano data/imagesets/0/trainval.txt

1. copy your images to data/images/0
2. copy your xml files to data/xml/0
3. edit labels.txt , test.txt, trainval.txt

YOLOv2 in PyTorch

NOTE: This project is no longer maintained and may not compatible with the newest pytorch (after 0.4.0).

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

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

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.

NOTE 1: This is still an experimental project. VOC07 test mAP is about 0.71 (trained on VOC07+12 trainval, reported by @cory8249). See issue1 and issue23 for more details about training.

NOTE 2: 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. An example of dataloader for VOCDataset: issue71.

NOTE 3: Upgrade to PyTorch 0.4: longcw#59

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 (link updated) 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 (link updated) and set the path in yolo2-pytorch/cfgs/exps/darknet19_exp1.py.

6. (optional) Training with TensorBoard.

   To use the TensorBoard, set use_tensorboard = True in yolo2-pytorch/cfgs/config.py and install TensorboardX (https://github.com/lanpa/tensorboard-pytorch). Tensorboard log will be saved in training/runs.

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

Training on your own data

The forward pass requires that you supply 4 arguments to the network:

• im_data - image data.
  • This should be in the format C x H x W, where C corresponds to the color channels of the image and H and W are the height and width respectively.
  • Color channels should be in RGB format.
  • Use the imcv2_recolor function provided in utils/im_transform.py to preprocess your image. Also, make sure that images have been resized to 416 x 416 pixels
• gt_boxes - A list of numpy arrays, where each one is of size N x 4, where N is the number of features in the image. The four values in each row should correspond to x_bottom_left, y_bottom_left, x_top_right, and y_top_right.
• gt_classes - A list of numpy arrays, where each array contains an integer value corresponding to the class of each bounding box provided in gt_boxes
• dontcare - a list of lists

License: MIT license (MIT)