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Datasets:
ShawnLi02
/
JigShape-Train

Tasks:
Image-to-Text
Visual Question Answering
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
100K - 1M
Tags:
jigsaw-puzzle
spatial-reasoning
vlm-benchmark
geometric-reasoning
vision-language
fine-tuning
Libraries:
Datasets
License:
Dataset card Data Studio Files
xet
 Community
JigShape-Train
File size: 4,821 Bytes
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---
license: cc-by-4.0
task_categories:
  - image-to-text
  - visual-question-answering
language:
  - en
tags:
  - jigsaw-puzzle
  - spatial-reasoning
  - vlm-benchmark
  - geometric-reasoning
  - vision-language
  - fine-tuning
size_categories:
  - 10K<n<100K
pretty_name: "JigShape Train & Eval"
---

# JigShape: Train & Evaluation Splits

This repository contains the **training** and **evaluation** splits of the [JigShape benchmark](https://huggingface.co/datasets/ShawnLi02/JigShape) for fine-tuning and evaluating Vision-Language Models on geometric jigsaw puzzle solving.

> The held-out **test split** is available separately at [ShawnLi02/JigShape](https://huggingface.co/datasets/ShawnLi02/JigShape).

## Overview

JigShape is a benchmark that evaluates **joint visual-geometric reasoning** in VLMs. Unlike traditional jigsaw benchmarks that use rectangular cuts (which create ambiguous ground truth in repeated-texture regions), JigShape features **tab-and-blank interlocking pieces** where geometric constraints ensure every puzzle has a unique solution.

Models must predict the correct grid position for each labeled piece by reasoning about both visual content (texture, color, object boundaries) and geometric constraints (tab-blank edge compatibility).

## Dataset Statistics

| Split | 4x4 | 8x8 | 12x12 | 16x16 | Total |
|:------|----:|----:|------:|------:|------:|
| **Train** | 22,992 | 22,992 | 22,992 | 22,992 | **91,968** |
| **Eval** | 250 | 250 | 250 | 250 | **1,000** |

- **Source images**: 23,742 unique high-resolution images from DIV2K, DIV8K, and Unsplash
- **No overlap**: Train and eval splits are partitioned by source image; the same image never appears in both

## Directory Structure

```
train/
  grid_4x4/
    DIV2K_0001/
      layout.png          # Shuffled pieces displayed on a grid, labeled with piece IDs
      source.png          # Original image (ground truth reference)
      ground_truth.json   # Piece-to-position mapping and edge signatures
    DIV2K_0002/
    ...
  grid_8x8/
  grid_12x12/
  grid_16x16/

validation/
  grid_4x4/
  grid_8x8/
  grid_12x12/
  grid_16x16/

split_index.json          # Canonical list of image IDs per split
```

## Instance Format

Each instance directory contains three files:

### `layout.png`
The model input: all N x N pieces arranged in ID order (not solution order) on a display board. Each piece is labeled with its numeric ID and shows its tab/blank/flat edge shapes.

### `source.png`
The original uncut image, provided for reference and visualization.

### `ground_truth.json`
```json
{
  "instance_id": "DIV2K_0001",
  "grid_size": 4,
  "n_pieces": 16,
  "id_to_position": {
    "7": [0, 0],
    "9": [0, 1],
    "5": [0, 2],
    "...": "..."
  },
  "edge_signatures": {
    "7": {"top": "flat", "right": "tab", "bottom": "blank", "left": "flat"},
    "9": {"top": "flat", "right": "tab", "bottom": "tab", "left": "blank"},
    "...": "..."
  }
}
```

**Fields:**
- `id_to_position`: Maps each piece ID to its correct `[row, col]` position in the solved puzzle
- `edge_signatures`: Each piece's four edges typed as `tab` (convex), `blank` (concave), or `flat` (border)

## Edge Types & Compatibility Rules

| Edge Type | Description | Constraint |
|:----------|:------------|:-----------|
| **Tab** | Convex semicircular protrusion | Must pair with a **blank** on the adjacent piece |
| **Blank** | Concave semicircular indentation | Must pair with a **tab** on the adjacent piece |
| **Flat** | Straight edge | Only on puzzle borders (corners have 2, edges have 1, interior pieces have 0) |

## Evaluation Metrics

| Metric | Description |
|:-------|:------------|
| **Piece Accuracy (PA)** | Fraction of pieces placed in their correct position |
| **Exact Match (EM)** | Whether the entire puzzle is solved correctly (all pieces correct) |

## Quick Start

```python
from huggingface_hub import snapshot_download

# Download eval split only (~5 GB)
snapshot_download(
    repo_id="ShawnLi02/JigShape-Train",
    repo_type="dataset",
    allow_patterns="validation/**",
    local_dir="./JigShape"
)

# Download a specific grid size for training (~120 GB for 16x16)
snapshot_download(
    repo_id="ShawnLi02/JigShape-Train",
    repo_type="dataset",
    allow_patterns="train/grid_4x4/**",
    local_dir="./JigShape"
)
```

## Related Resources

- **Test split** (held-out for competition): [ShawnLi02/JigShape](https://huggingface.co/datasets/ShawnLi02/JigShape)
- **Paper**: *JigShape: Can Vision-Language Models Solve Jigsaw Puzzles?* (under review)

## Citation

```bibtex
@article{jigshape2025,
  title={JigShape: Can Vision-Language Models Solve Jigsaw Puzzles?},
  author={Anonymous},
  year={2025}
}
```

## License

This dataset is released under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.