| --- |
| dataset_info: |
| features: |
| - name: id |
| dtype: string |
| - name: task_type |
| dtype: string |
| - name: image |
| dtype: binary |
| - name: question |
| dtype: string |
| - name: answer |
| dtype: string |
| - name: choices |
| dtype: string |
| - name: metadata |
| dtype: string |
| splits: |
| - name: test |
| num_bytes: 740966 |
| num_examples: 500 |
| download_size: 740966 |
| dataset_size: 740966 |
| configs: |
| - config_name: default |
| data_files: |
| - split: test |
| path: data/vlbenchy.parquet |
| --- |
| |
| ## Dataset Summary |
|
|
| VLbenchy is a 500-sample procedural vision-language benchmark for evaluating the fundamental visual understanding capabilities of VL models across 10 distinct task types. |
|
|
| ## Dataset Structure |
|
|
| Each sample contains a procedurally generated 336×336 PNG image paired with a natural-language question, a ground-truth answer, and multiple-choice options. All images are unique — generated with randomised shapes, colors, sizes, and layouts. |
|
|
| ## Data Fields |
|
|
| id (string): Unique sample identifier (e.g. "vlb_0042") |
| |
| task_type (string): One of 10 task categories — color_recognition, shape_counting, ocr, spatial_reasoning, size_comparison, object_presence, color_counting, grid_pattern, odd_one_out, background_color |
|
|
| image (binary): Raw PNG image bytes (336×336 px) |
|
|
| question (string): Natural-language question about the image |
|
|
| answer (string): Ground-truth answer string (exact-match scoreable) |
|
|
| choices (string): JSON-encoded list of 2–4 multiple-choice options |
|
|
| metadata (string): JSON-encoded dict with generation parameters (shapes, colors, counts, etc.) |
|
|
| ## Task Types |
|
|
| | Task | Samples | Description | |
| |------|---------|-------------| |
| | color_recognition | 50 | Identify the color of a shape | |
| | shape_counting | 50 | Count how many of a given shape are visible | |
| | ocr | 50 | Read a number rendered in the image | |
| | spatial_reasoning | 50 | Determine positional relationship between two shapes | |
| | size_comparison | 50 | Identify which of two shapes is larger | |
| | object_presence | 50 | Detect whether a specific shape is present | |
| | color_counting | 50 | Count the number of distinct colors among shapes | |
| | grid_pattern | 50 | Count rows or columns in a grid of shapes | |
| | odd_one_out | 50 | Identify the shape that differs from the others | |
| | background_color | 50 | Identify the background color of the image | |
|
|
