# A Baseline for General Music Object Detection with Deep Learning
## 6 Conclusions  
In this work, we establish a baseline for detecting music notation objects with deep learning models for generic object detection. Experiments were performed over three diverse major OMR datasets: the synthesized DeepScores dataset of born-digital modern notation, the MUSCIMA++ dataset of handwritten modern notation with varying degrees of writing quality, and the Capitan dataset that contains menstrual notation which is also handwritten, but of consistently high quality. Three types of neural models have been evaluated, namely the two-stage Faster R-CNN detector, the one-stage RetinaNet detector, and the U-Net detection mechanism that combines flexible semantic segmentation with a connected component detector. The choice of experimental setup and evaluation in this paper can serve as a basis for further music object detection experiments that will, therefore, be directly comparable to these baselines and will enable drawing conclusions and model design recommendations from these direct comparisons. Based on the quantitative and qualitative results in this paper, can we already formulate tentative practical recommendations for choosing a certain detection approach over another? We are well aware that three datasets may not be enough to draw such general conclusions; however, it is the most comprehensive experimentation that the current state of the OMR concerning available data allows. The suggestions should, therefore, be treated as tentative suggestions for further targeted investigations rather than fully-fledged conclusions. U-Nets, except for merging nearby symbols of the same class, do not seem to have a problem with the recall. Because they process symbol classes independently and do not reduce the output features resolution, they cannot run into the same (hypothesized) problems as Faster R-CNN, which has a limited number of region proposals for any single region of the image that the symbols in effect compete for. The number of available proposals depends on a hyperparameter setting that might be difficult to set appropriately for areas densely populated of ground truth objects. Furthermore, the proposal merging step (such as non-maximum suppression) may also lead to false negatives in cluttered environments. None of these disadvantages concern the U-Nets. On the other hand, while these properties are ideal for very cluttered data where symbol classes are set to notation primitives, the design drawbacks of U-Nets do appear when the symbol vocabulary consists of composite symbols; conversely, this is where the cluttering that presumably hinders the bounding box-based models ceases to be an important factor, and the relative strength of these models--the ability to consider a particular region as a whole--becomes more relevant because composite symbols share visual elements that correspond to the primitives. The choice of a musical symbol detection model, therefore, seems to be based on the way the detection ground truth is defined. Now that a deep learning baseline for music object detection has been established, where can subsequent research be heading? First, one can use the first insights gained from comparing the models over various datasets to improve the music object detectors themselves. The weak point of U-Nets seems to be settings with composite objects; experiments with composites built from MUSCIMA++ primitives by leveraging their syntactic relationships would be a logical step to investigate this. In order for U-Nets to improve on datasets with composite symbols (which are cheaper to annotate, as they generally contain fewer symbol instances, and therefore more likely to be encountered during various music digitizationefforts), a combination of the pixel-wise approach, which deals very well with highly cluttered areas or occlusion, and combined properties of the resulting pixel groups can be a viable avenue, while also perhaps alleviating the problem of parallel beams.