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---
license: cc-by-4.0
language:
- en
pretty_name: Puzzle Perception (Segmentation + pVQA)
size_categories:
- 10K<n<100K
task_categories:
- image-segmentation
- visual-question-answering
tags:
- puzzle-perception
- segmentation
- pvqa
- chess
- maze
- tower-of-hanoi
- nqueens
- synthetic
configs:
- config_name: default
data_files:
- split: train
path: data/train-*.parquet
- split: val
path: data/val-*.parquet
- split: test
path: data/test-*.parquet
---
# Puzzle Perception — Segmentation + pVQA
A single table over two tasks on synthetic puzzle images:
- **Segmentation** — per-pixel masks over chess, maze and tower-of-hanoi under one
unified 30-class label space.
- **pVQA** — multiple-choice perception probes over chess and N-Queens boards.
Every row carries the same 11 columns; the `type` column says which task it
belongs to, and columns that do not apply are `null`.
```python
from datasets import load_dataset
ds = load_dataset("PuzzleBench/Puzzle_Perception", split="test")
pvqa = ds.filter(lambda r: r["type"] == "pvqa")
seg = ds.filter(lambda r: r["type"] == "segmentation")
row = seg[0]
row["image"] # PIL.Image, 512x512 RGB
row["segmentation_labels"] # e.g. ['black_square', 'white_queen', ...]
```
## Columns
| Column | Type | Segmentation rows | pVQA rows |
|---|---|---|---|
| `id` | `int64` | unique, contiguous from 0 | unique, contiguous from 0 |
| `image` | `Image` | 512×512 RGB | 512×512 RGB |
| `mask` | `string` | path to a mask PNG in this repo | `null` |
| `puzzle` | `string` | `chess` / `maze` / `hanoi` | `chess` / `nqueens` |
| `image_path` | `string` | source-tree path (provenance) | source-tree path (provenance) |
| `type` | `string` | `segmentation` | `pvqa` |
| `segmentation_labels` | `list<string>` | class names present in the mask | `null` |
| `question` | `string` | `null` | probe question text |
| `answer` | `string` | `null` | correct option, as text |
| `question_id` | `string` | `null` | `q1``q8`, joins `pvqa_questions.yaml` |
| `options` | `list<string>` | `null` | the full answer space |
`image` is embedded in the Parquet as PNG bytes, so it renders in the viewer and
`load_dataset` hands back a `PIL.Image`. `mask` is instead a **path** to a real
file shipped under `masks/`, which keeps label maps — whose pixel values are
`0..29` and therefore look almost black — out of the preview:
```python
from huggingface_hub import hf_hub_download
import numpy as np
from PIL import Image
p = hf_hub_download("PuzzleBench/Puzzle_Perception", row["mask"], repo_type="dataset")
mask = np.array(Image.open(p)) # (512, 512) uint8, values in [0, 29]
```
## Splits
| Split | Total | Segmentation | pVQA | Per-puzzle |
|---|---:|---:|---:|---|
| `train` | 6000 | 6000 | 0 | seg: chess 2000, hanoi 2000, maze 2000 |
| `val` | 1500 | 1500 | 0 | seg: chess 500, hanoi 500, maze 500 |
| `test` | 2700 | 1500 | 1200 | seg: chess 500, hanoi 500, maze 500 · pVQA: chess 800, nqueens 400 |
| **total** | **10200** | **9000** | **1200** | |
pVQA appears **only** in `test`, and its rows are written before the
segmentation rows of that split.
## Segmentation labels
Masks are 8-bit PNGs whose pixel values **are** these class ids — no remapping
at load time.
| Puzzle | Class ids | Count |
|---|---|---:|
| maze | 0–7 | 8 |
| chess | 8–22 | 15 |
| hanoi | 23–29 | 7 |
| Class id | Name | Puzzle |
|---:|---|---|
| 0 | `wall` | maze |
| 1 | `path` | maze |
| 2 | `start` | maze |
| 3 | `dest_a` | maze |
| 4 | `dest_b` | maze |
| 5 | `dest_c` | maze |
| 6 | `dest_d` | maze |
| 7 | `dest_e` | maze |
| 8 | `background` | chess |
| 9 | `white_square` | chess |
| 10 | `black_square` | chess |
| 11 | `white_pawn` | chess |
| 12 | `white_knight` | chess |
| 13 | `white_bishop` | chess |
| 14 | `white_rook` | chess |
| 15 | `white_queen` | chess |
| 16 | `white_king` | chess |
| 17 | `black_pawn` | chess |
| 18 | `black_knight` | chess |
| 19 | `black_bishop` | chess |
| 20 | `black_rook` | chess |
| 21 | `black_queen` | chess |
| 22 | `black_king` | chess |
| 23 | `background` | hanoi |
| 24 | `peg` | hanoi |
| 25 | `disk_1` | hanoi |
| 26 | `disk_2` | hanoi |
| 27 | `disk_3` | hanoi |
| 28 | `disk_4` | hanoi |
| 29 | `disk_5` | hanoi |
## pVQA answer spaces
| Puzzle | `question_id` | Question | `options` | Rows |
|---|---|---|---|---:|
| chess | `q1` | Which quarter of the board is the white queen in? A=top-left B=top-right C=bottom-left D=bottom-right. | `[A, B, C, D]` | 100 |
| chess | `q2` | Which quarter of the board is the white bishop in? A=top-left B=top-right C=bottom-left D=bottom-right. | `[A, B, C, D]` | 100 |
| chess | `q3` | Is the leftmost white pawn in the top half or the bottom half of the board? | `[top, bottom]` | 100 |
| chess | `q4` | Is the white king above or below the black knight? | `[above, below]` | 100 |
| chess | `q5` | Are the white rook and the black king in the same row or the same column? | `[Yes, No]` | 100 |
| chess | `q6` | Are the white king and the black king on adjacent (touching) squares? | `[Yes, No]` | 100 |
| chess | `q7` | Is the white queen on the same rank, file, or diagonal as the black king? | `[Yes, No]` | 100 |
| chess | `q8` | Is there a white queen on the board? | `[Yes, No]` | 100 |
| nqueens | `q1` | Is the leftmost queen in the top half or the bottom half of the board? | `[top, bottom]` | 100 |
| nqueens | `q2` | Is the topmost queen in the left half or the right half of the board? | `[left, right]` | 100 |
| nqueens | `q3` | Is the leftmost queen above or below the rightmost queen? | `[above, below]` | 100 |
| nqueens | `q4` | Is the leftmost queen in the top, middle, or bottom third of the board? | `[top, middle, bottom]` | 100 |
## Files
| Path | Contents |
|---|---|
| `data/*.parquet` | the table — 10200 rows |
| `masks/{train,val,test}/*.png` | 9000 label maps, 8-bit, values `0..29` |
| `classes.yaml` | 30-class map + per-class loss weights |
| `manifest.csv` | `unified_id,source_task,source_id,split` |
| `pvqa_questions.yaml` | published question specs, nested by task |
## License
**CC BY 4.0.** If you use this dataset, please cite the PuzzleBench project.