| --- |
| configs: |
| - config_name: default |
| data_files: |
| - split: test |
| path: data/test-*.parquet |
| - config_name: 2d_single_loop |
| data_files: |
| - split: test |
| path: data/2d_single_loop/test-*.parquet |
| - config_name: 2d_maze_loop |
| data_files: |
| - split: test |
| path: data/2d_maze_loop/test-*.parquet |
| --- |
| |
| # OrdinalBench |
|
|
| **OrdinalBench** is a synthetic benchmark for evaluating ordinal reasoning capabilities of vision-language models (VLMs). It tests whether models can correctly identify the N-th element along structured visual paths — circular loops and maze traversals — in 2D top-down scenes. |
|
|
| 🌐 **Project page:** [ordinalbench.github.io](https://ordinalbench.github.io) |
| 🔧 **Evaluation toolkit:** [github.com/ordinalbench/ordinalbench-public](https://github.com/ordinalbench/ordinalbench-public) |
| 📦 **Dataset:** [huggingface.co/datasets/u-lec16/ordinalbench-dataset](https://huggingface.co/datasets/u-lec16/ordinalbench-dataset) |
|
|
| ## Dataset Summary |
|
|
| | Variant | Modality | Images | QA Pairs | Description | |
| |---|---|---|---|---| |
| | `2d_single_loop` | 2D top-down | 1,000 | 15,000 | Count along circular arrangements of labeled objects | |
| | `2d_maze_loop` | 2D top-down | 1,000 | 15,000 | Traverse maze corridors following turn-preference rules | |
| | **Total** | — | **2,000** | **30,000** | — | |
|
|
| ### Difficulty Axes |
|
|
| - **Ordinal Level (N magnitude):** Within Objects (N ≤ total objects), Exceed Objects (N > total objects, ≤ 99), Large Scale (100 ≤ N ≤ 300) |
| - **Object Count / Grid Size:** Few (5 / 7×7), Medium (10 / 11×11), Many (20 / 21×21) |
| - **Stride:** 1 (every step), 2 (skip-1), 3 (skip-2) |
|
|
| ## Usage |
|
|
| ### With 🤗 Datasets |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load all variants |
| ds = load_dataset("u-lec16/ordinalbench-dataset") |
| |
| # Load a specific variant |
| ds_maze = load_dataset("u-lec16/ordinalbench-dataset", "2d_maze_loop") |
| |
| # Inspect a sample |
| sample = ds_maze["test"][0] |
| print(sample["question"]) |
| print(sample["answer"]) |
| sample["image"].show() # PIL Image |
| ``` |
|
|
| ### With the Evaluation Toolkit |
|
|
| ```bash |
| pip install ordinalbench # or: uv sync |
| ob run --model openai --input <annotation.jsonl> --images-dir <images/> --output predictions.jsonl |
| ob score --preds predictions.jsonl --truth <annotation.jsonl> |
| ``` |
|
|
| ## Annotation Schema |
|
|
| Each record contains: |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | `question_id` | str | Unique question identifier | |
| | `image` | Image | The input image | |
| | `question` | str | Natural language instruction | |
| | `answer` | str | Ground-truth identifier | |
| | `N` | int | Target ordinal position (1-indexed, up to 300) | |
| | `ordinal_level` | str | `Within Objects` / `Exceed Objects` / `Large Scale` | |
| | `stride` | int | Counting stride (1, 2, or 3) | |
| | `modality` | str | `2d` | |
| | `loop_type` | str | `single` or `maze` | |
| | `trace` | list | Step-by-step traversal trace | |
|
|
| ## Data Splits |
|
|
| Currently provides a single `test` split designed for zero-shot evaluation. Training splits can be generated using the toolkit's data generation pipelines. |
|
|
| ## Limitations |
|
|
| - Synthetic data — limited real-world visual noise and lighting variation |
| - Questions are in English only |
| - Large Scale (N up to 300) may exceed token limits of some models |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{ordinalbench2025, |
| title={OrdinalBench: A Benchmark for Ordinal Reasoning in Vision-Language Models}, |
| author={Tozaki, Yusuke}, |
| year={2025} |
| } |
| ``` |
|
|
| ## License |
|
|
| MIT |
|
|
