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README.md

@@ -1,39 +1,76 @@
 ---
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: lmx
-    dtype: string
-  - name: musicxml
-    dtype: string
-  - name: score_id
-    dtype: string
-  - name: page_system
-    dtype: string
-  - name: source
-    dtype: string
-  - name: split
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 1647853186
-    num_examples: 15014
-  - name: dev
-    num_bytes: 316417090
-    num_examples: 2876
-  - name: test
-    num_bytes: 334323314
-    num_examples: 2986
-  download_size: 1715698697
-  dataset_size: 2298593590
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: dev
-    path: data/dev-*
-  - split: test
-    path: data/test-*
+license: cc-by-sa-4.0
+task_categories:
+  - image-to-text
+language:
+  - en
+tags:
+  - music
+  - optical-music-recognition
+  - omr
+  - sheet-music
+  - musicxml
+  - piano
+size_categories:
+  - 10K<n<100K
 ---
+
+# OLiMPiC — OpenScore Lieder Linearized MusicXML Piano Corpus
+
+A HuggingFace-formatted mirror of the [OLiMPiC dataset](https://github.com/ufal/olimpic-icdar24)
+for end-to-end optical music recognition of pianoform music.
+
+## Dataset description
+
+OLiMPiC provides system-level (one staff row) crops of piano scores paired with
+ground-truth annotations in Linearized MusicXML (LMX) format. Each sample is one
+system — the smallest unit that makes musical sense for training sequence models.
+
+- **Synthetic variant**: 17,945 rendered systems (train/dev/test)
+- **Scanned variant**: ~2,900 real IMSLP scans (dev/test only)
+- **Source**: OpenScore Lieder corpus (1,356 manually verified scores)
+
+## Format
+
+```python
+{
+    "image":       PIL.Image,   # system-level crop (one row of grand staff)
+    "lmx":         str,         # Linearized MusicXML token sequence
+    "musicxml":    str,         # full MusicXML for this system
+    "score_id":    str,         # OpenScore score identifier
+    "page_system": str,         # e.g. "p2-s3" (page 2, system 3)
+    "source":      str,         # "synthetic" or "scanned"
+    "split":       str,         # "train", "dev", or "test"
+}
+```
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("zzsi/olimpic")
+example = ds["train"][0]
+print(example["lmx"][:200])
+example["image"].show()
+```
+
+## License
+
+[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
+
+## Attribution
+
+Please cite the original work:
+
+```bibtex
+@inproceedings{OLiMPiC,
+  title     = {Practical End-to-End Optical Music Recognition for Pianoform Music},
+  author    = {Fier, Jiří and Hajič, Jan},
+  booktitle = {International Conference on Document Analysis and Recognition (ICDAR)},
+  year      = {2024}
+}
+```
+
+Original dataset: <https://github.com/ufal/olimpic-icdar24>
+Original authors: Jiří Fier, Jan Hajič (UFAL, Charles University)