Datasets:
metadata
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.
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:
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.