See ./yad2k.py --help and ./test_yolo.py --help for more options.
More Details
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.
train_overfit is a sample training script that overfits a YOLO_v2 model to a single image from the Pascal VOC dataset.
Known Issues and TODOs
Expand sample training script to train YOLO_v2 reference model on full dataset.
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.