This repository is the home of a Faster R-CNN implementation for Music Symbols to implement a fast and reliable Music Symbol detector with Deep Learning.
Note my previous projects that classified entire sheets or learnt to classify different music symbols.
An extensive overview of the results of different parameters is documented in this Google Spreadsheet.
This repository contains several scripts that can be used independently of each other. Before running them, make sure that you have the necessary requirements installed.
- Python 3.6
- Keras 2.0.8
- Tensorflow 1.3.0 (or optionally tensorflow-gpu 1.3.0)
- Microsoft Visual C++ Build Tools 2015 (for faster data_generator)
Optional: If you want to print the graph of the model being trained, install GraphViz on Windows via http://www.graphviz.org/Download_windows.php and add /bin to the PATH or run sudo apt-get install graphviz on Ubuntu (see keras-team/keras#3210)
For installing Tensorflow and Keras we recommend using Anaconda or Miniconda as Python distribution (we did so for preparing Travis-CI and it worked).
To accelerate training even further, you can make use of your GPU, by installing tensorflow-gpu instead of tensorflow via pip (note that you can only have one of them) and the required Nvidia drivers. For Windows, we recommend the excellent tutorial by Phil Ferriere. For Linux, we recommend using the official tutorials by Tensorflow and Keras.
The easiest way to start the training is to run TrainModel.ps from the PowerShell.
For manually starting the training, make sure to first compile the tools
cd keras_frcnn/py_faster_rcnn
python setup.py build_ext --inplace
then run TrainModel like this
MusicObjectDetector> python TrainModel.py --network resnet50 --output_weight_path "resnet50.hdf5"
Published under MIT License,
Copyright (c) 2017 Alexander Pacha, TU Wien
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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