---
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.

![Dataset statistics](./assets/dataset-overview.png)

## 🎯 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.

![Matched examples across the five view families](./assets/view-examples.png)

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.