Add MMB counterfactual dataset with viewer

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text

.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+env/
+venv/
+ENV/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+Thumbs.db

MMB_dataset.py

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+"""
+Hugging Face datasets loading script for the MMB Counterfactual Dataset.
+
+This dataset contains counterfactual visual question answering examples with:
+- Original images and counterfactual variants
+- Questions for each image variant
+- Answer matrices showing how each image answers each question
+"""
+
+from __future__ import annotations
+
+import csv
+from pathlib import Path
+from typing import Iterator
+
+import datasets
+from datasets import Image, Value
+
+
+_DESCRIPTION = """
+MMB-style counterfactual visual question answering dataset.
+
+This dataset contains scenes with:
+- Original images and counterfactual variants
+- Questions for each image variant
+- Answer matrices showing how each image answers each question
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/scholo/MMB_dataset"
+
+_LICENSE = "mit"
+
+_CITATION = """
+@misc{mmb_counterfactual_dataset,
+  title={MMB Counterfactual Dataset},
+  author={MMB-Dataset authors},
+  year={2026}
+}
+"""
+
+
+class MMB_dataset(datasets.GeneratorBasedBuilder):
+    """MMB counterfactual dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self) -> datasets.DatasetInfo:
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    # Simple scene identifier derived from the original image filename
+                    "scene_id": Value("string"),
+                    # Image triplet (paths resolved relative to dataset_1k_720p_2/images/)
+                    "original_image": Image(decode=True),
+                    "counterfactual1_image": Image(decode=True),
+                    "counterfactual2_image": Image(decode=True),
+                    # Questions
+                    "original_question": Value("string"),
+                    "counterfactual1_question": Value("string"),
+                    "counterfactual2_question": Value("string"),
+                    # Difficulties
+                    "original_question_difficulty": Value("string"),
+                    "counterfactual1_question_difficulty": Value("string"),
+                    "counterfactual2_question_difficulty": Value("string"),
+                    # Answer matrix (9 entries, image × question)
+                    "original_image_answer_to_original_question": Value("string"),
+                    "original_image_answer_to_cf1_question": Value("string"),
+                    "original_image_answer_to_cf2_question": Value("string"),
+                    "cf1_image_answer_to_original_question": Value("string"),
+                    "cf1_image_answer_to_cf1_question": Value("string"),
+                    "cf1_image_answer_to_cf2_question": Value("string"),
+                    "cf2_image_answer_to_original_question": Value("string"),
+                    "cf2_image_answer_to_cf1_question": Value("string"),
+                    "cf2_image_answer_to_cf2_question": Value("string"),
+                }
+            ),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(
+        self, dl_manager: datasets.DownloadManager
+    ) -> list[datasets.SplitGenerator]:
+        """We expose a single train split backed by dataset_1k_720p_2/image_mapping_with_questions.csv."""
+        data_dir = Path(__file__).parent.resolve()
+        csv_path = data_dir / "dataset_1k_720p_2" / "image_mapping_with_questions.csv"
+        images_dir = data_dir / "dataset_1k_720p_2" / "images"
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "csv_path": str(csv_path),
+                    "images_dir": str(images_dir),
+                },
+            )
+        ]
+
+    def _generate_examples(
+        self, csv_path: str, images_dir: str
+    ) -> Iterator[tuple[int, dict]]:
+        """Yield (key, example) tuples from the wide-format CSV."""
+        csv_path = Path(csv_path)
+        images_root = Path(images_dir)
+
+        if not csv_path.exists():
+            raise FileNotFoundError(f"CSV file not found: {csv_path}")
+
+        with csv_path.open("r", encoding="utf-8", newline="") as f:
+            reader = csv.DictReader(f)
+            for idx, row in enumerate(reader):
+                # Resolve scene_id from original_image filename, e.g. scene_0000_original.png -> scene_0000
+                original_image_name = row.get("original_image", "") or ""
+                scene_id = original_image_name.split("_original", 1)[0]
+
+                def _image_field(col_name: str) -> dict | None:
+                    filename = (row.get(col_name) or "").strip()
+                    if not filename:
+                        return None
+                    path = images_root / filename
+                    if not path.exists():
+                        return None
+                    return {"path": str(path)}
+
+                example = {
+                    "scene_id": scene_id,
+                    "original_image": _image_field("original_image"),
+                    "counterfactual1_image": _image_field("counterfactual1_image"),
+                    "counterfactual2_image": _image_field("counterfactual2_image"),
+                    "original_question": row.get("original_question", ""),
+                    "counterfactual1_question": row.get("counterfactual1_question", ""),
+                    "counterfactual2_question": row.get("counterfactual2_question", ""),
+                    "original_question_difficulty": row.get(
+                        "original_question_difficulty", ""
+                    ),
+                    "counterfactual1_question_difficulty": row.get(
+                        "counterfactual1_question_difficulty", ""
+                    ),
+                    "counterfactual2_question_difficulty": row.get(
+                        "counterfactual2_question_difficulty", ""
+                    ),
+                    "original_image_answer_to_original_question": row.get(
+                        "original_image_answer_to_original_question", ""
+                    ),
+                    "original_image_answer_to_cf1_question": row.get(
+                        "original_image_answer_to_cf1_question", ""
+                    ),
+                    "original_image_answer_to_cf2_question": row.get(
+                        "original_image_answer_to_cf2_question", ""
+                    ),
+                    "cf1_image_answer_to_original_question": row.get(
+                        "cf1_image_answer_to_original_question", ""
+                    ),
+                    "cf1_image_answer_to_cf1_question": row.get(
+                        "cf1_image_answer_to_cf1_question", ""
+                    ),
+                    "cf1_image_answer_to_cf2_question": row.get(
+                        "cf1_image_answer_to_cf2_question", ""
+                    ),
+                    "cf2_image_answer_to_original_question": row.get(
+                        "cf2_image_answer_to_original_question", ""
+                    ),
+                    "cf2_image_answer_to_cf1_question": row.get(
+                        "cf2_image_answer_to_cf1_question", ""
+                    ),
+                    "cf2_image_answer_to_cf2_question": row.get(
+                        "cf2_image_answer_to_cf2_question", ""
+                    ),
+                }
+
+                yield idx, example

README.md

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+---
+license: mit
+pretty_name: MMB Counterfactual Dataset
+task_categories:
+  - visual-question-answering
+  - multiple-choice
+language:
+  - en
+tags:
+  - vision
+  - language
+  - multimodal
+  - counterfactual
+  - question-answering
+  - synthetic
+size_categories:
+  - 1K<n<10K
+---
+
+# MMB Counterfactual Dataset
+
+A multimodal benchmark dataset featuring counterfactual visual question answering examples.
+
+## Dataset Structure
+
+This repository contains counterfactual visual question answering data with:
+
+- **Original images** and **counterfactual variants** (modifications to test reasoning)
+- **Questions** for each image variant
+- **Answer matrices** showing how each image answers each question (9 values per scene: 3 images × 3 questions)
+
+### Current Run: `dataset_1k_720p_2`
+
+The dataset currently includes:
+- **1,000 scenes**
+- **3 images per scene**: original, counterfactual 1, counterfactual 2
+- **3 questions per scene**: one for each image
+- **Answer matrix**: the answer of every image to every question
+
+Data is stored in `dataset_1k_720p_2/image_mapping_with_questions.csv` with images in `dataset_1k_720p_2/images/`.
+
+## Hugging Face Dataset Viewer
+
+This repository is configured as a Hugging Face dataset. The **Preview** tab will automatically:
+
+1. Use `MMB_Dataset.py` to load the CSV from `dataset_1k_720p_2/`
+2. Resolve image paths to display thumbnails
+3. Show all fields including:
+   - Scene IDs
+   - Three images per example (original + 2 counterfactuals)
+   - Questions and difficulties
+   - Complete answer matrix (9 fields)
+
+### Loading from Python
+
+After pushing this repository to the Hub, load it with:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("scholo/MMB_dataset", split="train", trust_remote_code=True)
+print(ds[0])
+```
+
+## Directory Structure
+
+```
+MMB-Dataset/
+├── MMB_dataset.py              # Hugging Face dataset loading script
+├── README.md                   # This file
+├── .gitattributes              # Git LFS configuration for images
+├── dataset_1k_720p_2/          # Current dataset run
+│   ├── images/                 # All PNG images
+│   ├── scenes/                 # JSON scene descriptions (reference)
+│   ├── image_mapping_with_questions.csv  # Main data file
+│   ├── checkpoint.json         # Run metadata
+│   └── run_metadata.json       # Run metadata
+```
+
+## License
+
+MIT

dataset_1k_720p_2/checkpoint.json

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+{"completed_scenes": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999]}