---
license: mit
task_categories:
  - object-detection
tags:
  - yolo
  - yolo26
  - pose-estimation
  - keypoint-detection
  - synthetic
  - catan
  - settlers-of-catan
pretty_name: CatanSynth Meridian v1 (50k)
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: images/train/**
      - split: validation
        path: images/valid/**
      - split: test
        path: images/test/**
---

# 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`](https://github.com) 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)`. Visibility `0` = 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:

1. **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
   ```
2. **`metadata.jsonl`** (one per split, next to `images/<split>/`) — for the Hugging Face `imagefolder` loader / Dataset Viewer, so each row's `bbox` and `keypoints` show up as structured columns rather than a plain image gallery:
   ```json
   {"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

```python
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