| --- |
| configs: |
| - config_name: exp1_counting |
| data_files: |
| - split: train |
| path: exp1_counting/train-*.parquet |
| - config_name: exp2_discrimination |
| data_files: |
| - split: train |
| path: exp2_discrimination/train-*.parquet |
| - config_name: exp3_controlled |
| data_files: |
| - split: train |
| path: exp3_controlled/train-*.parquet |
| - config_name: exp4_realworld_discrimination |
| data_files: |
| - split: train |
| path: exp4_realworld_discrimination/train-* |
| - config_name: exp4_realworld_naming |
| data_files: |
| - split: train |
| path: exp4_realworld_naming/train-*.parquet |
| - config_name: exp5_synthetic_matched |
| data_files: |
| - split: train |
| path: exp5_synthetic_matched/train-* |
| dataset_info: |
| - config_name: exp4_realworld_discrimination |
| features: |
| - name: image |
| dtype: image |
| - name: pair_num |
| dtype: string |
| - name: condition |
| dtype: string |
| - name: left_category |
| dtype: string |
| - name: right_category |
| dtype: string |
| - name: left_count |
| dtype: string |
| - name: right_count |
| dtype: string |
| - name: num_ratio |
| dtype: string |
| - name: answer |
| dtype: string |
| - name: prompt |
| dtype: string |
| - name: task |
| dtype: string |
| - name: tag |
| dtype: string |
| - name: eval_id |
| dtype: int64 |
| - name: correct_answer |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 325752264 |
| num_examples: 584 |
| download_size: 325629874 |
| dataset_size: 325752264 |
| - config_name: exp5_synthetic_matched |
| features: |
| - name: image |
| dtype: image |
| - name: pair_num |
| dtype: string |
| - name: condition |
| dtype: string |
| - name: shape_type |
| dtype: string |
| - name: left_category |
| dtype: string |
| - name: right_category |
| dtype: string |
| - name: left_count |
| dtype: string |
| - name: right_count |
| dtype: string |
| - name: num_ratio |
| dtype: string |
| - name: answer |
| dtype: string |
| - name: prompt |
| dtype: string |
| - name: task |
| dtype: string |
| - name: tag |
| dtype: string |
| - name: eval_id |
| dtype: int64 |
| - name: correct_answer |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 41105917 |
| num_examples: 1134 |
| download_size: 39706387 |
| dataset_size: 41105917 |
| --- |
| |
| # ANS Benchmark |
|
|
| A cognitively-grounded benchmark for **numerical cognition in vision-language models**, |
| covering **exact counting** and **approximate numerosity discrimination (ANS)**. |
|
|
| Each row is a self-contained trial: the stimulus image is embedded inline, and `prompt` |
| holds the exact question text sent to the model during evaluation. |
|
|
| ## Configs (6) |
|
|
| | Config | Task | Rows | Stimuli | |
| |---|---|---|---| |
| | `exp1_counting` | Count items in one image | 1,994 | Abstract shapes + real-world (FSC-133), small-n & large-n | |
| | `exp2_discrimination` | Which side has more? (abstract, random sizes) | 480 | Pre-stitched pairs, 6 ratio conditions | |
| | `exp3_controlled` | Which side has more? (numerosity decorrelated from area/spacing) | 2,500 | Pre-stitched pairs, 3 shapes × 4 colors | |
| | `exp4_realworld_naming` | Name the object category | 864 | Single real-world panels | |
| | `exp4_realworld_discrimination` | Which side has more? (real-world) | 600 | Pre-stitched same/diff-category pairs | |
| | `exp5_synthetic_matched` | Which side has more? (abstract matched to real-world) | 1,164 | Pre-stitched pairs, counts/coords matched to Exp4 | |
|
|
| Every paired discrimination image is `left | 10 px white divider | right`. |
|
|
| ## Common columns (all configs) |
|
|
| | Column | Type | Description | |
| |---|---|---| |
| | `image` | `Image` | Stimulus image (embedded bytes, loads as PIL) | |
| | `prompt` | `string` | Exact question text used in evaluation | |
| | `task` | `string` | Task type — see values below | |
| | `tag` | `string` | Analysis tag (`ans`, `ans2`, `ans3`, `ans4`, `ans5`) | |
| | `eval_id` | `int` | Global sequential trial index within each tag | |
| | `answer` / `correct_answer` | `string` | Ground-truth label (`left`/`right`/`tie` or integer count or category name) | |
|
|
| **`task` values:** |
|
|
| | Value | Description | |
| |---|---| |
| | `counting` | Exact count of objects | |
| | `discrimination_abstract` | Which side has more? — abstract shapes, unconstrained | |
| | `discrimination_controlled` | Which side has more? — numerosity decorrelated from surface area | |
| | `discrimination_realworld` | Which side has more? — real-world object images | |
| | `discrimination_synthetic` | Which side has more? — synthetic shapes matched to real-world stimuli | |
| | `naming` | Identify the object category | |
|
|
| ## Config-specific columns |
|
|
| **exp1_counting** — `subset`, `stimulus_type` {abstract, realworld}, `n_range` {small_n, large_n}, `shape`, `category`, `count` |
| |
| **exp2_discrimination** — `pair_id`, `shape` {dots, rectangles}, `anchor_n`, `ratio_condition`, `ratio_value`, `left_count`, `right_count`, `correct_answer` |
|
|
| **exp3_controlled** — `pair_num`, `pair_shape` {dots, ellipse, rectangle}, `pair_color`, `pair_ratio`, `left_numerosity`, `right_numerosity`, `left_TSA`, `right_TSA`, `left_FA`, `right_FA`, `correct_answer` |
| (TSA = total surface area; FA = field area / convex hull) |
| |
| **exp4_realworld_naming** — `category`, `count`, `original_image_name` |
|
|
| **exp4_realworld_discrimination** — `pair_num`, `condition` {same_cat, diff_cat}, `left_category`, `right_category`, `left_count`, `right_count`, `num_ratio`, `answer` |
|
|
| **exp5_synthetic_matched** — `pair_num`, `condition`, `shape_type` {dots, rectangles}, `left_category`, `right_category`, `left_count`, `right_count`, `num_ratio`, `answer` |
|
|
| ## Prompt templates |
|
|
| **5 semantically-equivalent templates per task**, assigned **one per stimulus** deterministically |
| (md5 hash of stimulus id). Each image is tested exactly once — prompts rotate across stimuli, |
| not within. |
|
|
| - **Counting:** `"How many {item} are there in the image? Please answer with an integer directly."` (+ 4 variants) |
| - **Discrimination abstract:** `"Two images of {desc} appear side by side. Which image contains more shapes? Respond with 'left' or 'right'."` (+ 4 variants) |
| - **Discrimination same-category:** `"Look at these two images. Which image has more {cat}? … Answer with 'left' or 'right'."` (+ 2 variants) |
| - **Discrimination diff-category:** `"Left image contains {lcat}, right image contains {rcat}. Which image has more items? … Answer with 'left' or 'right'."` (+ 2 variants) |
| - **Naming:** `"What is(are) the object(s) in the image called? Please answer with one object category name directly."` (+ 4 variants) |
|
|
| ## Quick start |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load one config |
| ds = load_dataset("87cc2s/ans-benchmark", "exp1_counting") |
| row = ds["train"][0] |
| row["image"] # PIL Image |
| row["prompt"] # "How many dots are there in the image? ..." |
| row["answer"] # "1" |
| row["task"] # "counting" |
| |
| # Load discrimination |
| ds2 = load_dataset("87cc2s/ans-benchmark", "exp2_discrimination") |
| row2 = ds2["train"][0] |
| row2["prompt"] # "Two images of white dots appear side by side ..." |
| row2["correct_answer"] # "right" |
| row2["task"] # "discrimination_abstract" |
| ``` |
|
|
| ## Provenance & notes |
|
|
| - Derived from original Experiment1–Experiment4, Experiment1_1, Experiment2_1, Experiment3_1 stimulus sets. |
| - **Excluded:** Exp1 `random_largen`-in-small-n bucket, real-world `FSC_sampled`/`FSC_sampled_largen`, |
| Exp3 `ellipses_white`/`rectangles_white` (rendered incorrectly — replaced by Exp3_1 re-renders). |
| 36 synthetic pairs with missing source images and 1 exact-duplicate counting row were dropped. |
| - **License:** Real-world images derive from FSC-133 (Ranjan et al., 2021) / FSC-147. Abstract stimuli |
| are procedurally generated and freely usable. Respect upstream FSC licensing. |
|
|
