Keras implementation of YOLO - real-time object detection system.
For now this package's functionality implementation is in progress.
Current plan:
- add weights loading from darknet config (in progress)
- [?] add script to compare run results vs darknet (currently I compare results with darknet manually).
Weights loading is more complex than it looks: there are several differences between keras and darknet, which should be carefully examined. For example, darknet allows batch normalization to be incorporated to layer, keras layers have no such an option and provides distinct BatchNormalization class instead.
To check currently implemented, run
python scripts/rebuild_and_load_weights.py
from the main project directory. This scripts expects to tiny-yolo.weights to be located at data folder, it also uses corresponding network config to create it and to load weights. But before that it builds necessary cython library with distutils.
If you run
python scripts/rebuild_and_load_weights.py ../darknet/dog.jpg
it tries to load image from given path and to predict its class (this is in progress).
As a result of this command, file predicted.jpg will be generated and it will look similar to this:
There still remains a pythonish problem with precision - that's why detection borders
at resulting image slightly differ from what you see when you run darknet.
- keras
- distutils
- cython
For development I use python 2, but this implementation is planned to be run with both python 2 & 3.