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README.md

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----
-license: mit
----
+---
+pretty_name: MMNeedle
+language:
+- en
+license: cc-by-4.0
+homepage: https://mmneedle.github.io/
+paper: https://arxiv.org/abs/2406.11230
+tagline: Benchmarking long-context multimodal retrieval under extreme visual context lengths.
+tags:
+- multimodal
+- visual-question-answering
+- long-context
+- evaluation
+size_categories:
+- 100K<n<1M
+---
+
+# MMNeedle
+
+MMNeedle is a stress test for long-context multimodal reasoning. Each example
+contains a sequence of haystack images created by stitching MS COCO sub-images
+into 1×1, 2×2, 4×4, or 8×8 grids. Given textual needle descriptions (derived
+from MS COCO captions), models must predict which haystack image and which
+sub-image cell matches the caption—or report that the needle is absent.
+
+This dataset card accompanies the official Hugging Face release so researchers no
+longer need to download from Google Drive or regenerate the benchmark from MS
+COCO.
+
+## Dataset structure
+
+- **Sequences (`sequence_length`)**: either a single stitched image or a set of 10 stitched images.
+- **Grid sizes (`grid_rows`, `grid_cols`)**: {1, 2, 4, 8} with square layouts.
+- **Needles per query (`needles_per_query`)**: {1, 2, 5}. Each query provides that many captions.
+- **Examples per configuration**: 10,000. Half contain the needle(s); half are negatives.
+- **Total examples**: 210,000 (21 configurations × 10k samples).
+
+Every example stores the full list of haystack image paths, the ground-truth
+needle locations (`image_index`, `row`, `col`), the MS COCO image IDs for the
+needles, the natural-language captions, and a `has_needle` boolean.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("Wang-ML-Lab/MMNeedle", split="test")
+example = ds[0]
+print(example.keys())
+# dict_keys(['id', 'sequence_length', 'grid_rows', 'grid_cols', 'needles_per_query',
+#            'haystack_images', 'needle_locations', 'needle_image_ids',
+#            'needle_captions', 'has_needle'])
+```
+
+Each entry in `haystack_images` is a PIL-compatible image object. `needle_captions`
+contains one string per requested needle (even for negative examples, where the
+corresponding location is `(-1, -1, -1)`).
+
+## Data fields
+
+| Field | Type | Description |
+| --- | --- | --- |
+| `id` | string | Unique identifier combining configuration and sample id. |
+| `sequence_length` | int | Number of stitched haystack images shown to the model. |
+| `grid_rows`, `grid_cols` | int | Dimensions of the stitched grid (each cell is 256×256 px). |
+| `needles_per_query` | int | Number of captions provided for the sample (1, 2, or 5). |
+| `haystack_images` | list of `Image` | Ordered haystack images for the sequence. |
+| `needle_locations` | list of dict | One dict per caption with `image_index`, `row`, and `col` (−1 when absent). |
+| `needle_image_ids` | list of string | MS COCO filenames that generated each caption. |
+| `needle_captions` | list of string | MS COCO captions used as the needle descriptions. |
+| `has_needle` | bool | True if at least one caption corresponds to a haystack cell. |
+
+## Recommended evaluation protocol
+
+1. Feed the ordered haystack images (preserving grid layout) plus the instruction
+   template from the MMNeedle paper to your multimodal model.
+2. Parse the model output into `(image_index, row, col)` triples.
+3. Compare against `needle_locations` to compute accuracy for positives and the
+   false-positive rate for negatives.
+
+See the repository’s `needle.py` for a reference implementation.
+
+## Source data
+
+- **Images & Captions**: MS COCO 2014 validation split (CC BY 4.0).
+- **Needle Metadata**: Automatically generated by the MMNeedle authors; included
+  here as JSON files.
+
+## Licensing
+
+All stitched haystack images inherit the [Creative Commons Attribution 4.0
+License](https://creativecommons.org/licenses/by/4.0/) from MS COCO. Attribution
+at minimum should cite both MMNeedle and MS COCO.
+
+## Citations
+
+```
+@article{wang2024mmneedle,
+  title={Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models},
+  author={Wang, Hengyi and Shi, Haizhou and Tan, Shiwei and Qin, Weiyi and Wang, Wenyuan and Zhang, Tunyu and Nambi, Akshay and Ganu, Tanuja and Wang, Hao},
+  journal={arXiv preprint arXiv:2406.11230},
+  year={2024}
+}
+
+@article{lin2014microsoft,
+  title={Microsoft COCO: Common Objects in Context},
+  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C. Lawrence},
+  journal={ECCV},
+  year={2014}
+}
+```