| --- |
| pretty_name: SUM |
| license: other |
| license_name: source-specific-terms |
| license_link: https://huggingface.co/datasets/RL-MIND/SUM/blob/main/THIRD_PARTY_NOTICES.md |
| task_categories: |
| - visual-question-answering |
| language: |
| - en |
| size_categories: |
| - n<1K |
| tags: |
| - benchmark |
| - high-resolution |
| - fine-grained-perception |
| - multimodal |
| - vqa |
| - image |
| - parquet |
| configs: |
| - config_name: original |
| default: true |
| data_files: |
| - split: test |
| path: parquet/original/test-*.parquet |
| - config_name: crop_1x |
| data_files: |
| - split: test |
| path: parquet/crop_1x/test-*.parquet |
| - config_name: crop_1_5x |
| data_files: |
| - split: test |
| path: parquet/crop_1_5x/test-*.parquet |
| - config_name: crop_3x |
| data_files: |
| - split: test |
| path: parquet/crop_3x/test-*.parquet |
| - config_name: crop_6x |
| data_files: |
| - split: test |
| path: parquet/crop_6x/test-*.parquet |
| - config_name: crop_12x |
| data_files: |
| - split: test |
| path: parquet/crop_12x/test-*.parquet |
| - config_name: crop_18x |
| data_files: |
| - split: test |
| path: parquet/crop_18x/test-*.parquet |
| - config_name: whiteground_1x |
| data_files: |
| - split: test |
| path: parquet/whiteground_1x/test-*.parquet |
| - config_name: whiteground_1_5x |
| data_files: |
| - split: test |
| path: parquet/whiteground_1_5x/test-*.parquet |
| - config_name: whiteground_3x |
| data_files: |
| - split: test |
| path: parquet/whiteground_3x/test-*.parquet |
| - config_name: whiteground_6x |
| data_files: |
| - split: test |
| path: parquet/whiteground_6x/test-*.parquet |
| - config_name: whiteground_12x |
| data_files: |
| - split: test |
| path: parquet/whiteground_12x/test-*.parquet |
| - config_name: whiteground_18x |
| data_files: |
| - split: test |
| path: parquet/whiteground_18x/test-*.parquet |
| - config_name: downsample_0_9375 |
| data_files: |
| - split: test |
| path: parquet/downsample_0_9375/test-*.parquet |
| - config_name: downsample_0_875 |
| data_files: |
| - split: test |
| path: parquet/downsample_0_875/test-*.parquet |
| - config_name: downsample_0_8125 |
| data_files: |
| - split: test |
| path: parquet/downsample_0_8125/test-*.parquet |
| - config_name: downsample_0_75 |
| data_files: |
| - split: test |
| path: parquet/downsample_0_75/test-*.parquet |
| - config_name: downsample_0_6875 |
| data_files: |
| - split: test |
| path: parquet/downsample_0_6875/test-*.parquet |
| - config_name: downsample_0_625 |
| data_files: |
| - split: test |
| path: parquet/downsample_0_625/test-*.parquet |
| - config_name: downsample_0_5625 |
| data_files: |
| - split: test |
| path: parquet/downsample_0_5625/test-*.parquet |
| - config_name: downsample_0_5 |
| data_files: |
| - split: test |
| path: parquet/downsample_0_5/test-*.parquet |
| - config_name: downsample_0_25 |
| data_files: |
| - split: test |
| path: parquet/downsample_0_25/test-*.parquet |
| - config_name: downsample_0_125 |
| data_files: |
| - split: test |
| path: parquet/downsample_0_125/test-*.parquet |
| - config_name: downsample_0_0625 |
| data_files: |
| - split: test |
| path: parquet/downsample_0_0625/test-*.parquet |
| - config_name: target_only_1x |
| data_files: |
| - split: test |
| path: parquet/target_only_1x/test-*.parquet |
| - config_name: target_only_3x |
| data_files: |
| - split: test |
| path: parquet/target_only_3x/test-*.parquet |
| - config_name: target_only_6x |
| data_files: |
| - split: test |
| path: parquet/target_only_6x/test-*.parquet |
| - config_name: target_only_18x |
| data_files: |
| - split: test |
| path: parquet/target_only_18x/test-*.parquet |
| - config_name: target_only_original |
| data_files: |
| - split: test |
| path: parquet/target_only_original/test-*.parquet |
| --- |
| |
| <table align="center" role="presentation" style="margin-left: auto; margin-right: auto;"> |
| <tr> |
| <td align="center"><img src="./assets/rl-mind-logo.webp" alt="RL-MIND research group logo" width="160" /></td> |
| <td align="center"><img src="./assets/sum-logo.png" alt="SUM dataset logo" width="160" /></td> |
| </tr> |
| </table> |
| <h1 align="center">SUM</h1> |
| <p align="center"><b>High-Resolution Fine-Grained Perception Across Controlled Image Views</b></p> |
| <p align="center"> |
| <a href="https://huggingface.co/datasets/RL-MIND/SUM">🤗 Hugging Face</a> · |
| <a href="https://modelscope.cn/datasets/KAIWANG/SUM">🤖 ModelScope</a> · |
| <a href="./README.md">English</a> · <a href="./README_ZH.md">中文</a> |
| </p> |
| <p align="center">700 questions · 29 aligned views · 5 task categories</p> |
| |
| ## 🔎 Introduction |
|
|
| **SUM** is an evaluation collection for studying fine-grained visual perception under controlled changes to image context, resolution, and background. Each of its **700 multiple-choice questions** is paired with **29 aligned image views**, so the same question can be evaluated on the full image, a region crop, a white canvas, a downsampled image, or a view retaining only the target bounding-box pixels. |
|
|
| The collection draws on selected samples from **VStarBench (115)**, **ZoomBench (485)**, and **HR-Bench (100)**. The release contains **20,300 view images**, representing 700 questions and **664 distinct original-image file hashes**. These are different units: the transformed views are not additional independent questions. |
|
|
|  |
|
|
| ## 🎯 What can be compared? |
|
|
| | View family | Variants | Images | What changes | |
| | --- | --- | ---: | --- | |
| | Original | Full image | 700 | Reference scene and resolution | |
| | Crop | 1, 1.5, 3, 6, 12, 18 × bbox area | 4,200 | Amount of context around the target | |
| | Whiteground | The same 6 crop settings | 4,200 | Crop pasted at its original position on a full-size white canvas | |
| | Downsample | 11 linear scales from 0.9375 to 0.0625 | 7,700 | Full-scene spatial resolution | |
| | Target-only | 1, 3, 6, 18 × crop canvas, plus original canvas | 3,500 | Only target-bbox pixels retained; all other pixels replaced with white | |
|
|
| Crop multipliers describe **area**, while downsample factors describe **width and height**. These quantities should not be compared as if they were the same scale. The target-only view preserves pixels inside a bounding box; it is not an instance-segmentation mask. Whiteground preserves all content inside the selected crop, including its surrounding context. |
|
|
|  |
|
|
| Thumbnails are resized for display. Pixel dimensions below each thumbnail describe the stored image. Released benchmark images retain their original encoding and resolution. |
|
|
| ## 📊 Dataset composition |
|
|
| | Task | Questions | |
| | --- | ---: | |
| | Color Attributes | 347 | |
| | OCR | 118 | |
| | Structural Attributes | 94 | |
| | Object Identification | 84 | |
| | Material Attributes | 57 | |
| | **Total** | **700** | |
|
|
| There are **679 four-option** and **21 two-option** questions. Questions are primarily in English; some OCR targets and a small number of source options contain Chinese or Japanese text. VStarBench samples come from `direct_attributes`; HR-Bench samples come from the local `FSP_8k_100` selection. The category mapping used by this release is preserved in the legacy metadata, including the historical merge of Visual Prompting and Fine-Grained Counting into Object Identification. |
|
|
| ## 📦 Files and configurations |
|
|
| ### Native Parquet |
|
|
| The default loader reads **Apache Parquet**: **29 configurations × 700 rows**, packaged in **187 shards (44.10 GB)**. Each row contains the question, answer, view metadata and an `image` column recognized by Hugging Face Datasets. Image bytes retain their original encoding and resolution. No separate image download is needed when loading a Parquet configuration. |
|
|
| Shards target 256 MiB, and every row group is below 100 MiB. All annotations and available image SHA-256 hashes were checked after conversion. [The Parquet manifest](./metadata/parquet_manifest.json) records the schema, row counts, shard sizes and checksums. |
|
|
| ```text |
| README.md / README_ZH.md Dataset cards |
| parquet/<view>/test-*.parquet Native Parquet shards (default loader) |
| metadata/parquet_manifest.json Parquet schema, row counts and checksums |
| data/<view>/test/metadata.jsonl 700 image-linked rows per view |
| data/<view>/test/images/ Original encoded image files |
| dataset.json 700 canonical questions; original-view image paths |
| metadata/image_manifest.json Per-image SHA-256, byte size and dimensions |
| metadata/validation_summary.json Full release validation summary |
| metadata/errata.json Known issues and historical corrections |
| metadata/legacy/ Legacy metadata with portable paths |
| metadata/path_mapping.json Mapping from the previous directory layout |
| scripts/prompts.py Normalized and legacy prompt constructors |
| scripts/evaluate.py Exact option-letter accuracy |
| assets/ Logos and data-derived figures |
| THIRD_PARTY_NOTICES.md Source attribution and terms |
| ``` |
|
|
| The default configuration is **`original`**. Every configuration has a single **`test`** split with 700 rows. Examples include `crop_3x`, `whiteground_3x`, `downsample_0_25`, and `target_only_original`. The complete configuration list appears in the dataset selector and `metadata/validation_summary.json`. |
|
|
| | Field | Meaning | |
| | --- | --- | |
| | `id`, `base_sample_id` | Stable question ID shared across views | |
| | `image` | Image decoded from the Parquet image bytes | |
| | `image_path`, `image_sha256` | Repository-relative source image path and expected SHA-256 | |
| | `image_available` | Whether this row contains image bytes | |
| | `image_unavailable_reason` | Null for available images; otherwise the recorded availability reason | |
| | `file_name` | Image path relative to the corresponding metadata file, in raw JSONL | |
| | `image_group_id` | SHA-256 of the original image file; identical-image questions share this ID | |
| | `source_dataset`, `source_question_id`, `source_image_id` | Source provenance | |
| | `question`, `question_raw` | Normalized question stem and original source question text | |
| | `choices`, `answer`, `answer_text` | Option list, correct letter and corresponding text | |
| | `task_type` | One of the five released task categories | |
| | `view_id`, `view_family` | Exact transformation setting and family | |
| | `width`, `height` | Dimensions of the current view | |
| | `bbox_original`, `bbox_clipped`, `bbox_view` | Source, in-bounds original-image, and current-view coordinates | |
| | `crop_box_original` | Crop or paste region in original-image coordinates | |
| | `crop_area_multiplier`, `downsample_linear_scale` | Separate parameters for the two scale definitions | |
|
|
| Coordinates are pixel-space `xyxy`. `bbox_view` for downsampling uses the actual output width and height, including rounding. Legacy metadata retains the crop boxes, effective area multipliers and pixel rules used to generate the released images. Paths beginning with `upstream://` inside legacy metadata are provenance identifiers, not additional bundled files. |
|
|
| ## 🚀 Quick start |
|
|
| ### Hugging Face: load one view |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("RL-MIND/SUM", "original", split="test", streaming=True) |
| sample = next(iter(dataset)) |
| print(sample["question"], sample["choices"], sample["answer"]) |
| image = sample["image"] |
| |
| # Use the same question IDs for a paired comparison. |
| crops = load_dataset("RL-MIND/SUM", "crop_3x", split="test", streaming=True) |
| ``` |
|
|
| ### Download an individual sample |
|
|
| ```python |
| import json |
| from huggingface_hub import hf_hub_download |
| |
| annotations = hf_hub_download("RL-MIND/SUM", "dataset.json", repo_type="dataset") |
| with open(annotations, encoding="utf-8") as f: |
| sample = json.load(f)[0] |
| image_path = hf_hub_download("RL-MIND/SUM", sample["image"], repo_type="dataset") |
| ``` |
|
|
| ### ModelScope mirror |
|
|
| ```python |
| from pathlib import Path |
| from datasets import load_dataset |
| from modelscope_hub import HubApi |
| |
| root = HubApi().download_repo( |
| "KAIWANG/SUM", "dataset", |
| allow_patterns=["parquet/original/*.parquet"], |
| ) |
| dataset = load_dataset( |
| "parquet", |
| data_files={"test": str(Path(root) / "parquet/original/*.parquet")}, |
| split="test", |
| ) |
| sample = dataset[0] |
| image = sample["image"] |
| ``` |
|
|
| Install `datasets`, `huggingface_hub`, `Pillow`, or `modelscope-hub` as needed. The Parquet version totals approximately 44.10 GB. The original image folders and JSONL annotations remain available (approximately 44.4 GB); choose the format and views you need to avoid downloading both copies. |
|
|
| ## ✅ Evaluation and release notes |
|
|
| - Evaluate the same IDs under each view and report accuracy by source, task and view. `scripts/evaluate.py` scores explicit option letters; absent or invalid answers count as incorrect. |
| - The 485 ZoomBench source question strings already include choices. `question` separates their stem from the choices; `question_raw` preserves the old text. `scripts/prompts.py` offers both the normalized prompt and the legacy prompt that repeats those choices. Prompt changes can affect results, so record which version was used. |
| - Thirty source bounding boxes extend slightly beyond image boundaries. The source coordinates are retained alongside clipped coordinates; source image bytes and existing transformed views are preserved. |
| - Sample `000669` contains the duplicate distractors `red` and `Red`; its recorded answer is `C` / `Brown`. The source options are retained and the issue is flagged in `quality_notes` and the errata. No replacement distractor was invented. |
| - Repeated original images can support different questions. Keep the same `image_group_id` and all of its views together if creating any downstream split. Do not treat the 29 paired views as independent data points. |
| - Every released image was fully decoded, checked against its expected dimensions and SHA-256 hashed during packaging. This structural validation is not a new human review of every semantic answer or a rerun of all transformation pixel-equivalence checks. |
|
|
| ## 📜 Sources, terms and citation |
|
|
| Please credit [V*Bench](https://github.com/penghao-wu/vstar), [ZoomBench](https://huggingface.co/datasets/inclusionAI/ZoomBench), and [HR-Bench](https://huggingface.co/datasets/DreamMr/HR-Bench), as applicable. SUM provides a selected, normalized and transformed evaluation collection; it does not claim that the source questions or imagery were newly authored for this release. |
|
|
| Source-specific terms and unresolved upstream license details are documented in [THIRD_PARTY_NOTICES.md](./THIRD_PARTY_NOTICES.md). The metadata deliberately uses `license: other`; this is not a blanket permissive license for all underlying images. |
|
|
