Release for storing benchmark results

This release contains the SQLite database files from our benchmark runs, allowing you to analyze the results without having to run the benchmarks yourself.

The files include the raw predicted and expected outputs, so results can be fully recomputed from scratch.

Release for storing onnx checkpoints for downloads

Pretrained onnx models for `homr´

Release for storing datasets for downloads

Dataset

This data set consists of public domain pieces, photographed and splitted into individual staff images.

This data set is only used to calculate the symbol error rate (SER). For the SER calculation we want to use a new dataset and not just the validation data
as both PrIMuS and convert_grandstaff add artificial distortions to the images and during training the network will likely optimize to read images which have
been distorted in exactly this way. That however doesn't mean in generalizes well, so this data set uses real camera data.

A downside of this data set is that it doesn't contain grandstaff data, that's simply because music_xml hasn't yet code to convert grandstaff MusicXml into two semantic parts.

Source list

https://musescore.com/user/35814389/scores/6428972
http://musescore.com/score/2872906
https://musescore.com/user/13502736/scores/6259652

Open an issue if one of the pieces must not be in this data set. We checked that the license is creative commons.

Release for storing checkpoints for downloads

Pretrained models for `homr