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README.md

@@ -1,80 +1,87 @@
 ---
+dataset_info:
+  features:
+  - name: images
+    sequence: image
+  - name: question
+    dtype: string
+  - name: label
+    dtype: string
+  - name: task
+    dtype: string
+  - name: num_images
+    dtype: int64
+  - name: image_names
+    sequence: string
+  - name: injected
+    dtype: bool
+  - name: object_counts
+    dtype: string
+  configs:
+  - config_name: count
+    data_files:
+    - split: train
+      path: data/count.parquet
+  - config_name: existence_adversarial
+    data_files:
+    - split: train
+      path: data/existence_adversarial.parquet
+  - config_name: existence_popular
+    data_files:
+    - split: train
+      path: data/existence_popular.parquet
+  - config_name: existence_random
+    data_files:
+    - split: train
+      path: data/existence_random.parquet
 license: cc-by-4.0
 task_categories:
-  - visual-question-answering
+- visual-question-answering
 language:
-  - en
+- en
 tags:
-  - multi-image
-  - hallucination
-  - benchmark
-  - vision-language-model
-  - multimodal
+- multi-image
+- hallucination
+- benchmark
+- vision-language-model
+- multimodal
 size_categories:
-  - 1K<n<10K
+- 1K<n<10K
 ---
 
-# MIHBench: Multi-Image Hallucination Benchmark
+# MIHBench
 
-**Paper**: [MIHBench: Can Multi-modal Large Language Models Understand Multi-Image Inputs?](https://arxiv.org/abs/2505.xxxxx) | ACM Multimedia 2025
+Multi-Image Hallucination Benchmark for evaluating multi-image understanding in MLLMs. 3,200 samples across 4 tasks (800 each), with each sample containing 2-4 images from COCO.
 
-## Overview
+## Fields
 
-MIHBench is a comprehensive benchmark for evaluating multi-image understanding and hallucination in Multi-modal Large Language Models (MLLMs). It contains **3,200 samples** across **4 tasks** (800 samples each), with each sample containing 2-4 images.
+| Field | Description |
+|-------|-------------|
+| images | 2-4 images (list) |
+| question | Natural language question about the images |
+| label | Ground truth: "yes" or "no" |
+| task | Task identifier |
+| num_images | Number of images in the sample |
+| image_names | Source image filenames |
+
+Additional fields for **count** task: `injected` (bool), `object_counts` (JSON string).
 
 ## Tasks
 
 | Task | # Images | Description |
 |------|----------|-------------|
-| **Count** | 2 | Determine whether the same number of a target object appears in both images. 400 samples include injected distracting objects. |
-| **Existence (Adversarial)** | 3 | Determine whether a target object exists in all images, with adversarially selected objects (rare, confusing). |
-| **Existence (Popular)** | 3 | Determine whether a target object exists in all images, using commonly known objects. |
-| **Existence (Random)** | 3 | Determine whether a target object exists in all images, using randomly selected objects. |
-
-> **Note**: A 5th task (ID Consistency) will be added in a future update.
-
-## Dataset Schema
-
-### Common columns (all tasks)
-
-| Column | Type | Description |
-|--------|------|-------------|
-| `images` | `list[image]` | 2-4 images (PIL Image objects) |
-| `question` | `str` | Natural language question about the images |
-| `label` | `str` | Ground truth answer (`"yes"` or `"no"`) |
-| `task` | `str` | Task identifier |
-| `num_images` | `int` | Number of images in the sample |
-| `image_names` | `list[str]` | Source image filenames |
-
-### Additional columns (Count task only)
-
-| Column | Type | Description |
-|--------|------|-------------|
-| `injected` | `bool` | Whether distracting objects were injected into the question |
-| `object_counts` | `str` | JSON string mapping image identifiers to object counts (e.g., `'{"A": 1, "B": 1}'`) |
+| count | 2 | Same number of target object in both images? |
+| existence_adversarial | 3 | Target object exists in all images? (rare/confusing objects) |
+| existence_popular | 3 | Target object exists in all images? (common objects) |
+| existence_random | 3 | Target object exists in all images? (random objects) |
 
-## Data Splits
+## Evaluation
 
-Each task is a separate configuration/split with 800 samples (400 `"yes"`, 400 `"no"`).
-
-## Image Sources
-
-- **Tasks 1-4**: COCO (Common Objects in Context) dataset
-- **Task 5** (ID Consistency, coming soon): CO3D dataset
-
-## Citation
-
-If you use MIHBench in your research, please cite:
-
-```bibtex
-@inproceedings{mihbench2025,
-  title={MIHBench: Can Multi-modal Large Language Models Understand Multi-Image Inputs?},
-  author={},
-  booktitle={Proceedings of the ACM Multimedia 2025},
-  year={2025}
-}
+```
+metrics: Accuracy, Precision, Recall, F1
+parser: yes/no binary
 ```
 
-## License
+## Source
 
-This dataset is released under the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license. The underlying images are from COCO and CO3D, which have their own licenses.
+Original data from [MIHBench](https://arxiv.org/abs/2505.xxxxx) (ACM Multimedia 2025).