README.md

---
license: cc-by-nc-4.0
tags:
- music
- documents
- end-to-end
- full-page
- system-level
annotations_creators:
- manually expert-generated
pretty_name: Sheet Music Benchmark
size_categories:
- 1K<n<10K
task_categories:
- image-to-text
- image-segmentation
- text-retrieval
subtasks:
- document-retrieval
extra_gated_fields:
  Affiliation: text
---


# ⚠️ Work in Progress! SMB: A Multi-Texture Sheet Music Recognition Benchmark ⚠️

## Overview

SMB (Sheet Music Benchmark) is a dataset of printed Common Western Modern Notation scores developed at the University of Alicante at the Pattern Recognition and Artificial Intelligence Group.

## Dataset Details
- **Image Format**: PNG
- **Encoding Formats**: RAW Humdrum **kern, **ekern (standarized **kern version)
- **Annotations**:
  - *Segmentation:* Bounding boxes
  - *Music encoding (system-level and full-page):* Humdrum **kern
- **Use Cases**:
  - Optical Music Recognition (OMR): system-level, full-page
  - Image Segmentation: music regions

## SMB usage 📖
SMB is publicly available at [HuggingFace](https://huggingface.co/datasets/PRAIG/SMB).

To download from HuggingFace:

  1.  Gain access to the dataset and get your HF access token from: [https://huggingface.co/settings/tokens](https://huggingface.co/settings/tokens).
  2.  Install dependencies and login HF:
       - Install Python
       - Run `pip install pillow datasets huggingface_hub[cli]`
       - Login by `huggingface-cli login` and paste the HF access token. Check [here](https://huggingface.co/docs/huggingface_hub/guides/cli#huggingface-cli-login) for details.
  3.  Use the following code to load SMB and extract the regions:


  ```python
  from datasets import load_dataset
  from PIL import Image, ImageDraw
  
  ds = load_dataset("PRAIG/SMB")

  # First image of the train split
  data = ds["train"][0]
  image = data["image"]
  
  # Create a drawing context
  draw = ImageDraw.Draw(image)
  
  for reg in data["regions"]:
      value = reg["bbox"]
  
      # Calculate positions and dimensions
      box_x = value["x"] / 100 * data["original_width"]
      box_y = value["y"] / 100 * data["original_height"]
      box_width = value["width"] / 100 * data["original_width"]
      box_height = value["height"] / 100 * data["original_height"]
  
      # Calculate the corners of the box
      top_left = (box_x, box_y)
      top_right = (box_x + box_width, box_y)
      bottom_left = (box_x, box_y + box_height)
      bottom_right = (box_x + box_width, box_y + box_height)
  
      # Draw the box
      draw.rectangle([top_left, bottom_right], width=3)
  
  # Save the image with boxes
  image.save("image.png")
  ```

## Citation

If you use our work, please cite us:

```bibtex
@preprint{MartinezSevillaPRAIG24,
  author = {Juan C. Martinez{-}Sevilla and
            Noelia Luna{-}Barahona and
            Joan Cerveto{-}Serrano and
            Antonio Rios{-}Vila and
            David Rizo and
            Jorge Calvo{-}Zaragoza},
  title = {A Multi{-}Texture Sheet Music Recognition Benchmark},
  year = {2024}
}
```