You only look once, but you reimplement neural nets over and over again.

YAD2K is a 90% Keras/10% Tensorflow implementation of YOLO_v2.

Original paper: YOLO9000: Better, Faster, Stronger by Joseph Redmond and Ali Farhadi.

• Keras
• Tensorflow
• Numpy
• Pillow (For rendering test results.)
• Python 3

git clone https://github.com/allanzelener/yad2k.git
cd yad2k

# [Option 1] To replicate the conda environment:
conda env create -f environment.yml
source activate yad2k
# [Option 2] Install everything globaly.
pip install numpy

pip install tensorflow-gpu  # CPU-only: conda install -c conda-forge tensorflow
pip install keras # Possibly older release: conda install keras

## Quick Start

- Download Darknet model weights from the [official YOLO website](http://pjreddie.com/darknet/yolo/).
- Convert the Darknet YOLO_v2 model to a Keras model.
- Test the converted model on the small test set in `images/`.

```bash
wget http://pjreddie.com/media/files/yolo.weights
./yad2k.py cfg/yolo.cfg yolo.weights model_data/yolo.h5
./test_yolo.py model_data/yolo.h5  # output in images/out/

See ./yad2k.py --help and ./test_yolo.py --help for more options.

The YAD2K converter currently only supports YOLO_v2 style models, this include the following configurations: darknet19_448, tiny-yolo-voc, yolo-voc, and yolo.

yad2k.py -p will produce a plot of the generated Keras model. For example see yolo.png.

YAD2K assumes the Keras backend is Tensorflow. In particular for YOLO_v2 models with a passthrough layer, YAD2K uses tf.space_to_depth to implement the passthrough layer. The evaluation script also directly uses Tensorflow tensors and uses tf.non_max_suppression for the final output.

voc_conversion_scripts contains two scripts for converting the Pascal VOC image dataset with XML annotations to either HDF5 or TFRecords format for easier training with Keras or Tensorflow.

yad2k/models contains reference implementations of Darknet-19 and YOLO_v2.

• Add YOLO_v2 loss function. (In-progress implementation on branch allanzelener/initial_loss_implementation).
• Script to train YOLO_v2 reference model.
• Support for additional Darknet layer types.
• Tuck away the Tensorflow dependencies with Keras wrappers where possible.
• YOLO_v2 model does not support fully convolutional mode. Current implementation assumes 1:1 aspect ratio images.

YAD2K stands on the shoulders of giants.

• 🔥 Darknet 🔥
• Darknet.Keras - The original D2K for YOLO_v1.
• Darkflow - Darknet directly to Tensorflow.
• caffe-yolo - YOLO_v1 to Caffe.
• yolo2-pytorch - YOLO_v2 in PyTorch.