Datasets:

May-apple
/

VBVR-Reorganized-Image

license: apache-2.0
task_categories:
  - text-to-image
  - image-to-image
language:
  - en
size_categories:
  - 100K<n<1M
tags:
  - reasoning
  - image-generation
  - benchmark
  - vbvr
  - image-mode
configs:
  - config_name: default
    data_files:
      - split: train
        path: parquet/train__*.parquet
      - split: train_samples
        path: parquet/train_samples.parquet
      - split: test_in_domain
        path: parquet/test_in_domain__*.parquet
      - split: test_out_of_domain
        path: parquet/test_out_of_domain__*.parquet

VBVR-Reorganized-Image

Image-mode derivative of VBVR-Reorganized. Each sample is a triple (first_frame.png, prompt.txt, final_frame.png): the model takes first_frame + prompt as input and should output an image that matches final_frame. No video in this version — purely single-image-input, single-image-output.

Layout

VBVR-Reorganized-Image/
├── train/
│   ├── Pure_Reasoning/         (48 generators, 480,000 samples)
│   └── Instruction_Following/  (48 generators, 480,000 samples)
└── test/
    ├── In-Domain_50/
    │   ├── Pure_Reasoning/         (31 generators, 155 samples)
    │   └── Instruction_Following/  (17 generators,  85 samples)
    └── Out-of-Domain_50/
        ├── Pure_Reasoning/         (11 generators,  55 samples)
        └── Instruction_Following/  (42 generators, 210 samples)

Each sample directory contains exactly three files:

first_frame.png — visual input
final_frame.png — image-mode ground truth (target output)
prompt.txt — text input (already cleaned for image-mode)

Counts

Split	Class	Generators	Samples
train	Pure_Reasoning	48	480,000
train	Instruction_Following	48	480,000
test/In-Domain_50	Pure_Reasoning	31	155
test/In-Domain_50	Instruction_Following	17	85
test/Out-of-Domain_50	Pure_Reasoning	11	55
test/Out-of-Domain_50	Instruction_Following	42	210
TOTAL		197	960,505

How this differs from the video-mode parent

No ground_truth.mp4 — image-mode tasks have a single static answer image instead of a video.
No metadata.json — task parameters not exposed at row level (still recoverable from the parent video repo if needed).
Only one prompt per sample (prompt.txt); prompt_original.txt is dropped to keep rows lean.
CLASS_3 tasks dropped — 10 task types (e.g. O-22_construction_stack, G-39_attention_shift_different, O-32_rolling_ball, O-44_rotation_puzzle, O-47_sliding_puzzle, O-52_traffic_light, O-62_gravity_physics, G-11_handle_object_reappearance, G-22_attention_shift_same, G-33_visual_jenga) are temporal-by-nature tasks whose single-image version carries no reasoning signal. They are excluded entirely.

Image-mode classes

The 197 task-split slots fall into two construction classes:

Class	Count	`final_frame.png` source	Prompt rewriter
CLASS_1	171	Copied verbatim from the video-mode last frame	Light cleanup of process language ("step by step", "render the X", etc.) via `prompt_rewriter.py` / `train_prompt_rules.py`
CLASS_2	26	Re-rendered from `metadata.json` by a per-task painter (orange path cells for grid/maze tasks, red trajectory polylines for bouncing balls, numbered labels on fallen dominoes, ...)	Original prompt + appended task-specific image-mode output instruction

CLASS_2 examples:

Grid/maze (G-12 to G-18, G-31, G-32, G-41, G-44 to G-47, O-39): orange path overlay
Physics (G-35, G-48, O-15): red trajectory polyline
Domino (O-23, O-24): numeric labels on fallen pieces
Occlusion (G-21, G-36): mask redefined to stop at object midline
Other: O-29, O-31, O-34

Pure_Reasoning prompt cleanup

For Pure_Reasoning tasks, prompts are stripped of reasoning leaks beyond the standard image-mode cleanup. The full leak-removal pipeline runs: rules.py (family-level + task-specific rules from the video-mode dataset)

rules_image.py (image-mode-specific paraphrase handlers).

Examples of stripped leaks:

O-23 (E_OUTCOME_NARRATIVE): drop the 4-sentence outcome narration ("trunk falls first, then splits into Branch A...")
O-12 / O-11 / O-13 / O-14 (C_ANALOGY): drop the explicit "first change its color, then change its size" enumeration
G-273 (D_PHYSICS): drop the answer-leaking "right container holds the higher-density liquid" + parenthetical pointer
O-15 (D_PHYSICS): drop "elastic collision physics (angle of incidence equals angle of reflection)"
O-75 (D_PHYSICS): drop the terminal-state spoiler "to a common equilibrium level across all tubes"
O-45 (B_PATTERN_SEQUENCE): drop "Observe the cyclic order... Identify the color cycle..." choreography for both color and arithmetic paraphrases

Constraint phrases that are kept (they specify the task, not the answer): "shortest path", "minimum number of steps", "additive color mixing", "subtractive color mixing", physics constants (refractive index, viscous damping coefficient).

Paired-variant generators (4 unique tasks)

The same image-mode pipeline carries the depth-flip and inverse variants created in the parent video-mode dataset:

Variant	Mechanic difference vs forward
`G-21B_multiple_occlusions_vertical_behind`	Mask passes behind (objects in front) — final_frame: mask gone, objects visible
`G-36B_multiple_occlusions_horizontal_behind`	Same depth flip, horizontal direction
`O-18B_glass_refraction_inverse`	Given in-glass ray, predict incidence ray
`O-19B_mirror_reflection_inverse`	Given reflected ray, predict incidence ray

These share the same first_frame.png as their forward counterpart but have a different final_frame.png and a prompt that distinguishes the direction. The pair tests whether the model is reading the prompt rather than memorising the visual.

Tier-2 extremum-flip variants (5 unique tasks, test-only)

Five additional *B variants live in test/Out-of-Domain_50/Instruction_Following, flipping the extremum criterion of their forward task:

Variant	Forward	Flip
`G-160B_circle_smallest_numerical_value`	`G-160_circle_largest_numerical_value`	largest → smallest
`G-167B_select_shortest_polygon_side`	`G-167_select_longest_polygon_side`	longest → shortest
`G-218B_identify_smallest_angle_in_triangle`	`G-218_identify_largest_angle_in_triangle`	largest → smallest
`G-219B_select_rightmost_shape`	`G-219_select_leftmost_shape`	leftmost → rightmost
`G-221B_outline_outermost_square`	`G-221_outline_innermost_square`	innermost → outermost

These are classified as Instruction_Following, not Pure_Reasoning — they're explicit-criterion mark-and-pick tasks (mechanical perception+comparison), so flipping the criterion only changes which shape gets marked, not the reasoning structure. Each has 5 samples in OOD (25 samples total). They are counted in the OOD IF total in the counts table.

How to use

from datasets import load_dataset

ds = load_dataset("May-apple/VBVR-Reorganized-Image", split="train")
# Each row: class, task, split, sample_id, prompt, first_frame, final_frame
# first_frame and final_frame are HF Image() — call as .convert("RGB") to
# get a PIL image, or pass directly to your model's preprocessor.

Three splits:

train — 960,000 samples
test_in_domain — 240 samples
test_out_of_domain — 265 samples

Provenance

This is a derivative of the parent video-mode dataset. The image-mode build pipeline lives in the source repo (scripts/vbvr_reorg/):

build_image_mode_full.py — flattens video samples into image-mode samples
build_parquet_shards_image.py — packs into HF parquet shards
rules_image.py — image-mode-specific PR leak rules
Renderers reused from VBVR-Bench-Image/regenerator/ and VBVR-Train-Image/regenerator/

Citation

@dataset{vbvr_reorganized_image_2026,
  title  = {VBVR-Reorganized-Image: Single-Image Reasoning Benchmark Derived from VBVR},
  author = {Video-Reason},
  year   = {2026},
  url    = {https://huggingface.co/datasets/May-apple/VBVR-Reorganized-Image},
}

License

Inherits the license of the underlying VBVR dataset (Apache-2.0).