nithinmanoj10/CataNET-Meridian-v1.0-Nano
Keypoint Detection • Updated • 28 • 1
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[
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End of preview. Expand in Data Studio
CatanSynth Meridian v1 (50k)
50,000 procedurally-generated synthetic images of Settlers of Catan boards, labeled for YOLO pose/keypoint estimation of the hexagonal terrain tiles. Generated by the catan-in-silico synthetic data engine, which composites terrain/token/road/settlement/city/robber assets over randomized table backgrounds with camera-style augmentations (lighting, perspective, blur, noise).
Task
Single-class keypoint detection: every hexagonal terrain tile on the board is one object instance.
- Class (
nc=1):terrain_hex - Keypoints (
kpt_shape: [6, 3]): 6 keypoints per hex (the tile's 6 corners), each as(x, y, visibility). Visibility0= not labeled (occluded/out of frame, coordinates zeroed),2= labeled and visible. - Bounding box: standard YOLO
(cx, cy, w, h), normalized 0–1.
A board typically contains multiple terrain_hex instances (up to 19 for a full board), so each image has multiple label rows / multiple entries in the objects list.
Splits
| Split | Images | Directory |
|---|---|---|
| train | 40,000 | images/train/ |
| validation | 5,000 | images/valid/ |
| test | 5,000 | images/test/ |
Each split's images (and matching YOLO labels/*.txt) are chunked into chunk_000/, chunk_001/, … subfolders of ≤8,000 files each, to stay under Hugging Face's recommended ~10k-files-per-folder limit. An image and its label always share the same chunk index, e.g. images/train/chunk_002/catan_board_00001.jpg ↔ labels/train/chunk_002/catan_board_00001.txt.
Format
Two parallel representations are included:
- Raw YOLO pose format (
labels/<split>/chunk_*/*.txt) — for training directly with Ultralytics:class cx cy w h x1 y1 v1 x2 y2 v2 x3 y3 v3 x4 y4 v4 x5 y5 v5 x6 y6 v6 metadata.jsonl(one per split, next toimages/<split>/) — for the Hugging Faceimagefolderloader / Dataset Viewer, so each row'sbboxandkeypointsshow up as structured columns rather than a plain image gallery:{"file_name": "chunk_002/catan_board_00001.jpg", "objects": [{"category": "terrain_hex", "bbox": [0.46, 0.44, 0.36, 0.37], "keypoints": [[0.50, 0.25, 2.0], ...]}, ...]}
Note on the Viewer: the auto-generated preview shows image thumbnails plus the parsed bbox/keypoints values as JSON columns — it does not draw keypoint overlays on the image itself (Hugging Face's stock viewer has no built-in renderer for arbitrary keypoint tasks).
Loading
from datasets import load_dataset
ds = load_dataset("nithinmanoj10/CatanSynth-Meridian-v1-50K")
ds["train"][0] # {"image": <PIL.Image>, "objects": [...]}
To train with Ultralytics directly, download the repo and point a dataset.yaml at the images//labels/ folders (kpt_shape: [6, 3], nc: 1, names: ['terrain_hex']).
License
MIT
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