NJU-HARD

🤗 Hugging Face · 🟣 ModelScope · 📊 Statistics

English | 中文：Hugging Face · ModelScope

📚 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

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:

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:

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:

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:

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.