# A Baseline for General Music Object Detection with Deep Learning
## 6 Conclusions
In [28], a YOLOv3-like approach has been used to detect noteheads with joint pixel classification and bounding box regression. A post-filtering step then significantly improved precision, which is a much bigger problem for U-Nets than recall. The Deep Watershed Detector used by [27] exhibits a similar combination. For improving the Faster R-CNN results on music notation data, we would need a better understanding of the relationship between anchor hyperparameters and expected symbol density. The inability of the RetinaNet to detect small symbols is disappointing and merits further investigation, as it persisted regardless of various anchor hyperparameter settings. An idea to test the hypothesis of some minimum detectable absolute symbol size would be to upscale the image until the objects of interest reach sufficient size, and run detection on windows of the upscaled image that fit into GPU memory. The speed of this model both in training and inference would make it an attractive choice for interactive OMR, which is now probably the most viable approach towards building OMR systems that can best support creating digital editions of music, such as the Ceres system [53] or the Pixel.js editor [54]. More can also be done in terms of evaluation to make the baseline more informative regarding the outputs expected from OMR downstream. While music object detection is a critical step in OMR pipelines, it is not the final step; for evaluating a detector as part of an OMR system, one should be able to attribute downstream errors, e.g., in pitch or duration inference, to detection errors or uncertainties. For instance, Ref. [25] uses several ways of evaluating MIDI inferred on top of the object detection results, using a baseline reconstruction model. Furthermore, the graph model of MUSCIMA++ offers hope that the edges can serve as \"conduits\" from higher-level errors to their lower-level causes, but, so far, we are not aware of any method that would allow combining such structured gradient flows with the object detection architectures. Then, there are exciting challenges of transfer learning. Modern notation follows the same underlying rules, regardless of whether it is printed or handwritten: can one leverage a printed music dataset to train for handwritten object detection? At least between DeepScores and MUSCIMA++, many symbol classes can be directly mapped onto each other--experiments in this direction should be possible. In this context, the effect of image deformations and other, perhaps more realistic data augmentation can be explored. Finally, while it is obvious that merely detecting the musical elements in score images does not represent a complete OMR system, we believe that addressing music object detection in a generic machine learning manner brings a series of changes that are quite interesting for the development of the OMR field. Except for the few attempts at end-to-end OMR that are so far limited to monophonic output [7; 8; 55], all OMR systems are explicitly detecting music objects at some point in their recognition pipeline. Generic deep learning approaches may have the potential to decouple object detection from actual knowledge of music notation itself--nevertheless, users now need to be aware of how these systems learn and design them accordingly. The proposed general machine learning approach can then be used by all of them, regardless of the musical notation system (except for hyperparameter tuning and cookbook-style model choice recommendations), as opposed to approaches that exploit specific characteristics of how the music notation system works to build segmentation heuristics. Then, as the music object detection stage is done, image processing can in principle be forgotten: the only remaining link to the original image is the bounding box and potentially pixel mask features associated with the detected objects.