NJU-HARD

---
license: unknown
language:
- en
task_categories:
- visual-question-answering
tags:
- vqa
- remote-sensing
- uav
- multi-image
- parquet
- deduplicated
size_categories:
- 1K<n<10K
configs:
- config_name: preview
  data_files:
  - split: sample
    path: preview/vqa_preview.parquet
- config_name: questions
  default: true
  data_files:
  - split: unsplit
    path: questions/*.parquet
- config_name: images
  data_files:
  - split: unsplit
    path: images/*.parquet
---
# NJU-HARD

<table align="center" role="presentation" style="margin:0 auto; border:0; background:transparent;">
  <tr style="border:0; background:transparent;">
    <td align="center" style="border:0; background:transparent; padding:0 8px;">
      <img src="./assets/rl-mind-logo-v2.webp" alt="RL-MIND research group logo" width="160" height="160" loading="eager" fetchpriority="high" decoding="async" style="width:160px; height:160px; object-fit:contain;" />
    </td>
    <td align="center" style="border:0; background:transparent; padding:0 8px;">
      <img src="./assets/dataset-logo-v2.webp" alt="NJU-HARD dataset logo" width="160" height="160" loading="eager" fetchpriority="high" decoding="async" style="width:160px; height:160px; object-fit:contain;" />
    </td>
  </tr>
</table>
<p align="center">
  <a href="https://huggingface.co/datasets/RL-MIND/NJU-HARD">🤗 Hugging Face</a> ·
  <a href="https://www.modelscope.cn/datasets/KAIWANG/NJU-HARD">🟣 ModelScope</a> ·
  <a href="https://huggingface.co/datasets/RL-MIND/NJU-HARD/blob/main/statistics.json">📊 Statistics</a>
</p>

<p align="center"><b>English</b> | 中文：<a href="https://huggingface.co/datasets/RL-MIND/NJU-HARD/blob/main/README_ZH.md">Hugging Face</a> · <a href="https://www.modelscope.cn/datasets/KAIWANG/NJU-HARD/file/view/master/README_ZH.md?status=1">ModelScope</a></p>

## 📚 Introduction

**NJU-HARD** is the deduplicated, full-resolution release of the HARD visual question answering data. It contains **1,563 valid VQA records** across **8 task types**, using **854 original aerial images**. Only images referenced by these valid questions are included.

Every original JPEG is embedded **once** in a native Hugging Face Parquet image table. Questions refer to images by their full SHA-256 IDs, in the original Image 1 / Image 2 / Image 3 order. The release supports single-image and multi-image VQA without storing the same original repeatedly for different questions.

This release packages the question table and original-image table together for distribution on Hugging Face and ModelScope. It is separate from the per-question embedded-image `HARD-VQA` version.

## 📊 Dataset at a Glance

| Item | Value |
|:---|:---|
| Valid questions | 1,563 |
| Unique original images | 854 distinct paths and SHA-256 hashes |
| Ordered image references | 2,571 |
| Images per question | 1, 2, or 3 |
| Original resolution | 12,768 × 9,564 pixels |
| Original JPEG bytes | 87,637,348,481 bytes, approximately 87.64 GB |
| Original-image Parquet shards | 180 |
| All Parquet files | 87,639,068,405 bytes, including questions and thumbnail preview |
| Split | `unsplit`; no new train/validation/test split is introduced |
| Preview | 8 examples, one per task, with derived thumbnails |

Original images are **not resized, cropped, or re-encoded**. Thumbnail images are separate browsing aids and are not additional original training or evaluation images.

| Source task label | Questions |
|:---|---:|
| `classification` | 250 |
| `counting` | 200 |
| `property` | 154 |
| `spatial_relation` | 100 |
| `task1_specialdetect` | 66 |
| `task3_region_single` | 151 |
| `task4_findit_nearbox` | 342 |
| `task_2_relocate` | 300 |
| **Total** | **1,563** |

## 📦 Data Organization

| Configuration | Split | Contents |
|:---|:---|:---|
| `questions` **(default)** | `unsplit` | Question text, options, answers, ordered `image_ids`, and source annotations |
| `images` | `unsplit` | One original image per row, keyed by `image_id`; JPEG bytes embedded as `Image()` |
| `preview` | `sample` | Eight VQA examples with thumbnails for browsing |

```text
questions/   # 1,563 question records
images/      # 854 originals in 180 Parquet shards
preview/     # 8 thumbnail examples
provenance/  # source/checksum manifests, image index, and validation reports
statistics.json
```

The question table preserves `question_id`, `question`, `options`, `answer`, `answer_text`, `task_type`, `num_images`, `source_image_paths`, `reference_bbox`, `scene`, `sequence`, and `quality_flags`. The `options` struct has A/B/C/D fields; C/D are null for binary-choice questions. Ordered `image_ids` correspond one-to-one with `source_image_paths`.

The image table contains `image_id`, `image`, `source_image_paths`, `sha256`, `width`, `height`, and `byte_count`. `image_id` equals the SHA-256 of the complete original JPEG. Source paths are provenance only: loading does not require the source server or an external image directory.

## 🚀 Quick Start

Load the small question table first, or inspect the thumbnail preview:

```python
from datasets import load_dataset
REPO = "RL-MIND/NJU-HARD"
questions = load_dataset(REPO, "questions", split="unsplit")
preview = load_dataset(REPO, "preview", split="sample")
print(questions[0]["question"], questions[0]["image_ids"])
```

To reconstruct a VQA example with original images:

```python
from datasets import Image, load_dataset
images = load_dataset(REPO, "images", split="unsplit")
images = images.cast_column("image", Image(decode=False))
# Store only IDs and row numbers in the lookup, not all image bytes.
id_to_row = {image_id: i for i, image_id in enumerate(images["image_id"])}
question = questions[0]
for image_number, image_id in enumerate(question["image_ids"], start=1):
    jpeg_bytes = images[id_to_row[image_id]]["image"]["bytes"]
    # Process this original; iteration preserves the question's image order.
    print(image_number, image_id, len(jpeg_bytes))
```

Loading `questions` alone does not download the originals. A full `images` load downloads approximately 87 GB and builds a local cache; allow additional cache space. Datasets **3.6.0 and 5.0.0** have been checked with this format.

After downloading a complete repository snapshot from **either platform**, local Parquet loading is also supported:

```python
from pathlib import Path
from datasets import load_dataset
root = Path("/path/to/NJU-HARD")
questions = load_dataset("parquet", data_files={
    "unsplit": str(root / "questions" / "*.parquet")
}, split="unsplit")
images = load_dataset("parquet", data_files={
    "unsplit": str(root / "images" / "*.parquet")
}, split="unsplit")
```

Sequential streaming avoids downloading the entire image table up front:

```python
from datasets import Image, load_dataset
stream = load_dataset(REPO, "images", split="unsplit", streaming=True)
stream = stream.cast_column("image", Image(decode=False))
first = next(iter(stream))
print(first["image_id"], len(first["image"]["bytes"]))
```

Streaming is sequential and does not provide random lookup by image ID. For repeated question-to-image joins, use the cached dataset and row-number index above.

## 🔍 Validation and Annotation Notes

- The source has 1,565 records. IDs **476 and 496** have null questions, options, and answers and are excluded. Images used only by excluded records are not included.
- ID **597** is preserved with the flag `duplicate_option_text`: A and B both contain `white`, while the source answer is B.
- Question text, options, answers, ordered image references, and bounding boxes preserve source values. Structural validation does not establish that every answer is semantically correct.
- Every original JPEG is checked by SHA-256 before embedding and after Parquet readback. Independent validation checks all question fields, image metadata, reference closure, and image-index rows, plus sampled original bytes through both Datasets versions. Reports are in `provenance/`.
- Preview images have a 1,024-pixel longest edge and `preview_only=true`. The ordered `images` list and `image_1`/`image_2`/`image_3` columns support browsing; missing slots are null. Preview questions and boxes still use original-image coordinates. Use the originals for evaluation.
- The source provides no train/validation/test assignment, so all records remain `unsplit`. If you create a split, account for shared images and adjacent frames across partitions.

## 📜 License and Provenance

The source data does not include an explicit license. The dataset card therefore retains **`license: unknown`**. Packaging the data does not grant a new data-use license; consult the data owner for applicable terms.

Source-image checksums, excluded records, the image-to-shard index, and validation evidence are provided in `provenance/`. The paired header uses the supplied RL-MIND group mark and a newly generated NJU-HARD dataset logo.

	---
	license: unknown
	language:
	- en
	task_categories:
	- visual-question-answering
	tags:
	- vqa
	- remote-sensing
	- uav
	- multi-image
	- parquet
	- deduplicated
	size_categories:
	- 1K<n<10K
	configs:
	- config_name: preview
	data_files:
	- split: sample
	path: preview/vqa_preview.parquet
	- config_name: questions
	default: true
	data_files:
	- split: unsplit
	path: questions/*.parquet
	- config_name: images
	data_files:
	- split: unsplit
	path: images/*.parquet
	---

	# NJU-HARD

	<table align="center" role="presentation" style="margin:0 auto; border:0; background:transparent;">
	<tr style="border:0; background:transparent;">
	<td align="center" style="border:0; background:transparent; padding:0 8px;">
	<img src="./assets/rl-mind-logo-v2.webp" alt="RL-MIND research group logo" width="160" height="160" loading="eager" fetchpriority="high" decoding="async" style="width:160px; height:160px; object-fit:contain;" />
	</td>
	<td align="center" style="border:0; background:transparent; padding:0 8px;">
	<img src="./assets/dataset-logo-v2.webp" alt="NJU-HARD dataset logo" width="160" height="160" loading="eager" fetchpriority="high" decoding="async" style="width:160px; height:160px; object-fit:contain;" />
	</td>
	</tr>
	</table>

	<p align="center">
	<a href="https://huggingface.co/datasets/RL-MIND/NJU-HARD">🤗 Hugging Face</a> ·
	<a href="https://www.modelscope.cn/datasets/KAIWANG/NJU-HARD">🟣 ModelScope</a> ·
	<a href="https://huggingface.co/datasets/RL-MIND/NJU-HARD/blob/main/statistics.json">📊 Statistics</a>
	</p>

	<p align="center"><b>English</b> \| 中文：<a href="https://huggingface.co/datasets/RL-MIND/NJU-HARD/blob/main/README_ZH.md">Hugging Face</a> · <a href="https://www.modelscope.cn/datasets/KAIWANG/NJU-HARD/file/view/master/README_ZH.md?status=1">ModelScope</a></p>

	## 📚 Introduction

	NJU-HARD is the deduplicated, full-resolution release of the HARD visual question answering data. It contains 1,563 valid VQA records across 8 task types, using 854 original aerial images. Only images referenced by these valid questions are included.

	Every original JPEG is embedded once in a native Hugging Face Parquet image table. Questions refer to images by their full SHA-256 IDs, in the original Image 1 / Image 2 / Image 3 order. The release supports single-image and multi-image VQA without storing the same original repeatedly for different questions.

	This release packages the question table and original-image table together for distribution on Hugging Face and ModelScope. It is separate from the per-question embedded-image `HARD-VQA` version.

	## 📊 Dataset at a Glance

	\| Item \| Value \|
	\|:---\|:---\|
	\| Valid questions \| 1,563 \|
	\| Unique original images \| 854 distinct paths and SHA-256 hashes \|
	\| Ordered image references \| 2,571 \|
	\| Images per question \| 1, 2, or 3 \|
	\| Original resolution \| 12,768 × 9,564 pixels \|
	\| Original JPEG bytes \| 87,637,348,481 bytes, approximately 87.64 GB \|
	\| Original-image Parquet shards \| 180 \|
	\| All Parquet files \| 87,639,068,405 bytes, including questions and thumbnail preview \|
	\| Split \| `unsplit`; no new train/validation/test split is introduced \|
	\| Preview \| 8 examples, one per task, with derived thumbnails \|

	Original images are not resized, cropped, or re-encoded. Thumbnail images are separate browsing aids and are not additional original training or evaluation images.

	\| Source task label \| Questions \|
	\|:---\|---:\|
	\| `classification` \| 250 \|
	\| `counting` \| 200 \|
	\| `property` \| 154 \|
	\| `spatial_relation` \| 100 \|
	\| `task1_specialdetect` \| 66 \|
	\| `task3_region_single` \| 151 \|
	\| `task4_findit_nearbox` \| 342 \|
	\| `task_2_relocate` \| 300 \|
	\| Total \| 1,563 \|

	## 📦 Data Organization

	\| Configuration \| Split \| Contents \|
	\|:---\|:---\|:---\|
	\| `questions` (default) \| `unsplit` \| Question text, options, answers, ordered `image_ids`, and source annotations \|
	\| `images` \| `unsplit` \| One original image per row, keyed by `image_id`; JPEG bytes embedded as `Image()` \|
	\| `preview` \| `sample` \| Eight VQA examples with thumbnails for browsing \|

	```text
	questions/ # 1,563 question records
	images/ # 854 originals in 180 Parquet shards
	preview/ # 8 thumbnail examples
	provenance/ # source/checksum manifests, image index, and validation reports
	statistics.json
	```

	The question table preserves `question_id`, `question`, `options`, `answer`, `answer_text`, `task_type`, `num_images`, `source_image_paths`, `reference_bbox`, `scene`, `sequence`, and `quality_flags`. The `options` struct has A/B/C/D fields; C/D are null for binary-choice questions. Ordered `image_ids` correspond one-to-one with `source_image_paths`.

	The image table contains `image_id`, `image`, `source_image_paths`, `sha256`, `width`, `height`, and `byte_count`. `image_id` equals the SHA-256 of the complete original JPEG. Source paths are provenance only: loading does not require the source server or an external image directory.

	## 🚀 Quick Start

	Load the small question table first, or inspect the thumbnail preview:

	```python
	from datasets import load_dataset

	REPO = "RL-MIND/NJU-HARD"
	questions = load_dataset(REPO, "questions", split="unsplit")
	preview = load_dataset(REPO, "preview", split="sample")
	print(questions[0]["question"], questions[0]["image_ids"])
	```

	To reconstruct a VQA example with original images:

	```python
	from datasets import Image, load_dataset

	images = load_dataset(REPO, "images", split="unsplit")
	images = images.cast_column("image", Image(decode=False))

	# Store only IDs and row numbers in the lookup, not all image bytes.
	id_to_row = {image_id: i for i, image_id in enumerate(images["image_id"])}
	question = questions[0]
	for image_number, image_id in enumerate(question["image_ids"], start=1):
	jpeg_bytes = images[id_to_row[image_id]]["image"]["bytes"]
	# Process this original; iteration preserves the question's image order.
	print(image_number, image_id, len(jpeg_bytes))
	```

	Loading `questions` alone does not download the originals. A full `images` load downloads approximately 87 GB and builds a local cache; allow additional cache space. Datasets 3.6.0 and 5.0.0 have been checked with this format.

	After downloading a complete repository snapshot from either platform, local Parquet loading is also supported:

	```python
	from pathlib import Path
	from datasets import load_dataset

	root = Path("/path/to/NJU-HARD")
	questions = load_dataset("parquet", data_files={
	"unsplit": str(root / "questions" / "*.parquet")
	}, split="unsplit")
	images = load_dataset("parquet", data_files={
	"unsplit": str(root / "images" / "*.parquet")
	}, split="unsplit")
	```

	Sequential streaming avoids downloading the entire image table up front:

	```python
	from datasets import Image, load_dataset

	stream = load_dataset(REPO, "images", split="unsplit", streaming=True)
	stream = stream.cast_column("image", Image(decode=False))
	first = next(iter(stream))
	print(first["image_id"], len(first["image"]["bytes"]))
	```

	Streaming is sequential and does not provide random lookup by image ID. For repeated question-to-image joins, use the cached dataset and row-number index above.

	## 🔍 Validation and Annotation Notes

	- The source has 1,565 records. IDs 476 and 496 have null questions, options, and answers and are excluded. Images used only by excluded records are not included.
	- ID 597 is preserved with the flag `duplicate_option_text`: A and B both contain `white`, while the source answer is B.
	- Question text, options, answers, ordered image references, and bounding boxes preserve source values. Structural validation does not establish that every answer is semantically correct.
	- Every original JPEG is checked by SHA-256 before embedding and after Parquet readback. Independent validation checks all question fields, image metadata, reference closure, and image-index rows, plus sampled original bytes through both Datasets versions. Reports are in `provenance/`.
	- Preview images have a 1,024-pixel longest edge and `preview_only=true`. The ordered `images` list and `image_1`/`image_2`/`image_3` columns support browsing; missing slots are null. Preview questions and boxes still use original-image coordinates. Use the originals for evaluation.
	- The source provides no train/validation/test assignment, so all records remain `unsplit`. If you create a split, account for shared images and adjacent frames across partitions.

	## 📜 License and Provenance

	The source data does not include an explicit license. The dataset card therefore retains `license: unknown`. Packaging the data does not grant a new data-use license; consult the data owner for applicable terms.

	Source-image checksums, excluded records, the image-to-shard index, and validation evidence are provided in `provenance/`. The paired header uses the supplied RL-MIND group mark and a newly generated NJU-HARD dataset logo.