![]() ![]() Oemer first predicts different informations with two image semantic segmentation models: one for More details can be found in oemer/classifier.py. There are three different SVM models that are used to classify symbols. The data used to train SVM models are extracted from DeepScores-extended. To identify invidual symbol types on the predictions, SVM models are used. Both trainings leverage multiple types of image augmentation techniques to enhance the robustness (see here). The two models use different datasets for training: CvcMuscima-Distortions for training the first model, and DeepScores-extended for the second model. The training script is under oemer/train.py. There are two UNet models being used: one serves to separate stafflines and all other symbols, and the other for separating more detailed symbol types (see Model Prediction below). Notice that all descriptions below are simplfied compared to the actual implementations. The overall flow can also be found in oemer/ete.py, which is also the entrypoint for oemer command. This section describes the detail techniques for solving the OMR problem. If the problem still exists, file an issue and make sure following the template format. If you encounter errors, try adding -without-deskew first (see issue #9). ![]() If you want to use Tensorflow for running the inference,Īdd -use-tf to the command and make sure there is TF installed. ![]() Put checkpoint files start with 1st_* to oemer/checkpoints/unet_big, 2nd_* to oemer/checkpoints/seg_net, and rename the files by removing the prefix 1st_, 2nd_.ĭefault to use Onnxruntime for inference. Checkpoints can also be manually downloaded from here. ![]() For the first time running, the checkpoints will be downloaded automatically and may take up to 10 minutes to download, depending on your connection speed. With GPU, this usually takes around 3~5 minutes to finish. The oemer command will output the transcribed MusicXML file and an image of analyzed elements to current directory. # (optional) Or install the newest updates directly from Github. # (optional) Install the Tensorflow version. ![]()
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