yolo-tf

by ruiminshen

ruiminshen / yolo-tf

TensorFlow implementation of the YOLO (You Only Look Once)

201 Stars 71 Forks Last release: over 3 years ago (v0.1) GNU Lesser General Public License v3.0 108 Commits 1 Releases

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This project is deprecated. Please see yolo2-pytorch

TensorFlow implementation of the YOLO (You Only Look Once) and YOLOv2

Dependencies

Configuration

Configurations are mainly defined in the "config.ini" file. Such as the detection model (config/model), base directory (config/basedir, which identifies the cache files (.tfrecord), the model data files (.ckpt), and summary data for TensorBoard), and the inference function ([model]/inference). Notability the configurations can be extended using the "-c" command-line argument.

Basic Usage

  • Download the PASCAL VOC 2007 (training, validation and test) and 2012 (training and validation) dataset. Extract these tars into one directory (such as "~/Documents/Database/").

  • Download the COCO 2014 (training, validation, and test) dataset. Extract these zip files into one directory (such as "~/Documents/Database/coco/").

  • Run "cache.py" to create the cache file for the training program. A verify command-line argument "-v" is strongly recommended to check the training data and drop the corrupted examples, such as the image "COCOval2014000000320612.jpg" in the COCO dataset.

  • Run "train.py" to start the training process (the model data saved previously will be loaded if it exists). Multiple command-line arguments can be defined to control the training process. Such as the batch size, the learning rate, the optimization algorithm and the maximum number of steps.

  • Run "detect.py" to detect objects in an image. Run "export CUDAVISIBLEDEVICES=" to avoid out of GPU memory error while the training process is running.

Examples

Training a 20 classes Darknet YOLOv2 model from a pretrained 80 classes model

  • Cache the 20 classes data using the customized config file argument. Cache files (.tfrecord) in "~/Documents/Database/yolo-tf/cache/20" will be created.
python3 cache.py -c config.ini config/yolo2/darknet-20.ini -v
  • Download a 80 classes Darknet YOLOv2 model (the original file name is "yolo.weights", a version from Darkflow is recommanded). In this tutorial I put it in "~/Downloads/yolo.weights".

  • Parse the 80 classes Darknet YOLOv2 model into Tensorflow format (~/Documents/Database/yolo-tf/yolo2/darknet/80/model.ckpt). A warning like "xxx bytes remaining" indicates the file "yolo.weights" is not compatiable with the original Darknet YOLOv2 model (defined in the function

    model.yolo2.inference.darknet
    ). Make sure the 80 classes data is cached before parsing.
python3 parse_darknet_yolo2.py ~/Downloads/yolo.weights -c config.ini config/yolo2/darknet-80.ini -d
  • Transferring the 80 classes Darknet YOLOv2 model into a 20 classes model (~/Documents/Database/yolo-tf/yolo2/darknet/20) except the final convolutional layer. Be ware the "-d" command-line argument will delete the model files and should be used only once when initializing the model.
python3 train.py -c config.ini config/yolo2/darknet-20.ini -t ~/Documents/Database/yolo-tf/yolo2/darknet/80/model.ckpt -e yolo2_darknet/conv -d
  • Using the following command in another terminal and opening the address "localhost:6006" in a web browser to monitor the training process.
tensorboard --logdir ~/Documents/Database/yolo-tf/yolo2/darknet/20
  • If you think your model is stabilized, press Ctrl+C to cancel and restart the training with a greater batch size.
python3 train.py -c config.ini config/yolo2/darknet-20.ini -b 16
  • Detect objects from an image file.
python3 detect.py $IMAGE_FILE -c config.ini config/yolo2/darknet-20.ini
  • Detect objects with a camera.
python3 detect_camera.py -c config.ini config/yolo2/darknet-20.ini

Checklist

  • [x] Batch normalization
  • [x] Passthrough layer
  • [ ] Multi-scale training
  • [ ] Dimension cluster
  • [x] Extendable configuration (via "-c" command-line argument)
  • [x] PASCAL VOC dataset supporting
  • [x] MS COCO dataset supporting
  • [x] Data augmentation: random crop
  • [x] Data augmentation: random flip horizontally
  • [x] Multi-thread data batch queue
  • [x] Darknet model file (.weights) parser
  • [x] Partial model transferring before training
  • [x] Detection from image
  • [x] Detection from camera
  • [ ] Multi-GPU supporting
  • [ ] Faster NMS using C/C++ or GPU
  • [ ] Performance evaluation

License

This project is released as the open source software with the GNU Lesser General Public License version 3 (LGPL v3).

Acknowledgements

This project is mainly inspired by the following projects:

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