# A Baseline for General Music Object Detection with Deep Learning
## 4 Experimental Setup
### Datasets  
As we are considering generic object detection methods, we can evaluate all of them across a range of OMR datasets for symbol detection [48]. As a side-effect of this evaluation, we also obtain a notion of the difficulty of these datasets for object detection in general. Each dataset contains a different kind of typography, adding to the breadth of the baselines we establish. * **DeepScores**: DeepScores [49] is a very large synthetic dataset of music scores in Common Western Modern Notation (CWMN), consisting of 300,000 images along with their ground-truth annotations for performing symbol classification, image segmentation, and object detection. It is based on a large collection of freely available MusicXML files from MuseScore [50] that were converted into Lilypond files and digitally rendered into images using five different fonts to obtain a higher visual variability. The first version of this dataset only has annotations for a limited vocabulary that is missing essential glyphs, such as stems, beams, barlines, ledger lines or slurs. The second version, which is currently under development, contains these missing annotations and has been made available to us by the original authors. This set contains only 100 pages, but has full annotations for all relevant music symbols. * **MUSCIMA++:** MUSCIMA++ [14] is a dataset of handwritten music that has over 90,000 manually annotated handwritten musical symbols in CWMN. The dataset is built on the CVC-MUSCIMA dataset for staff removal [51]. The ground truth is defined as a notation graph: in addition to the individual symbols, their relationships are annotated as well, so that the semantics (pitch,duration, and onset) can be inferred and the full OMR pipeline can be trained on the dataset. However, in this paper, we only focus on symbol detection, equivalent to recovering the vertices of the notation graph. * **Capitan**: Capitan consists of 46 fully-annotated pages in Spanish mensural notation from the 16 th-18 th century. The manuscripts represent sacred music, composed for vocal interpretation. The compositions were written in music books by different copyists of that time. To preserve the integrity of the physical sources, images of the manuscripts were taken with a camera instead of scanning them in a flatbed scanner, leading to suboptimal conditions in some cases. The corpus is based on the dataset used in the work of [PERSON] and [PERSON] [24]. However, the refined version used in this work is focused on obtaining a diplomatic transcript, keeping the information of how symbols were written in the source as intact as possible. That is why there is a higher number of categories, since now symbols that have the same meaning--for example, a minima with the stem pointing up or down--are considered as different categories. An overview of the corpora considered is given in Table 1, while we show some patches extracted from their images in Figure 3. As can be observed, the characteristics of the different corpora are quite heterogeneous, which is interesting for drawing generalizable conclusions from our experiments. It is important to mention the variability in the aspects of the bounding boxes of the elements within these datasets. This variability appears not only amongst elements of different classes but also, especially in the case of handwritten notation, amongst elements of the same class. To illustrate this scenario, Figure 4 shows the different shapes of the boxes to be recognized in each dataset. The majority of objects in the DeepScores dataset are very tiny. The MUSCIMA++ dataset shows a greater variation in aspect ratios with one dominant cluster, the noteheads. In addition, the Capitan dataset contains a significant number of bigger objects, compared to the other two datasets with distinct clusters.