PanoVQA

PanoVQA provides English visual question-answering annotations for panoramic scenes from NuScenes, DeepAccident, and BlendPASS. Questions cover topics such as scene descriptions, object attributes, spatial relationships, visibility, and traffic situations.

The full dataset contains 653,914 question-answer records across 40,910 images. A smaller version, PanoVQA_mini, contains 13,427 records across 4,415 images for smaller experiments and pipeline development.

Each record contains one image reference, one user question, and one assistant answer. Multiple records can reference the same image.

Dataset splits

Counts below refer to question-answer records and distinct image paths within each split.

Source	Split	Full records	Full images	Mini records	Mini images
NuScenes	train	419,336	28,130	3,616	242
NuScenes	val	89,659	6,019	2,418	162
DeepAccident	train	119,299	5,551	5,073	3,251
DeepAccident	val	24,328	1,112	1,028	662
BlendPASS	val	1,292	98	1,292	98
Total		653,914	40,910	13,427	4,415

BlendPASS includes only a validation split in this release. No test split is provided. The mini *_qwenvl.json files are alternate representations of the corresponding mini annotations, so they are not counted again in this table.

Download and use the full dataset

The following shell commands target Linux or macOS and require Python, cat, and unzip.

1. Install the download tool

python -m pip install -U huggingface_hub

2. Download annotations and image archive parts

Run this command from the directory where you want to store the dataset:

hf download wakinghours/PanoVQA \
  --repo-type dataset \
  --local-dir ./PanoVQA-data \
  --include "PanoVQA/**" \
  --include "Panorama.zip.part-*" \
  --include "README.md" \
  --max-workers 2

cd PanoVQA-data

The full image archive is distributed as three binary parts:

File	Size (decimal GB, approximate)
Panorama.zip.part-000	21.47
Panorama.zip.part-001	21.47
Panorama.zip.part-002	9.96

Download all three parts. They are consecutive pieces of a single ZIP file and cannot be extracted individually. The parts total approximately 52.9 GB. Allow about 160 GB of free disk space if you retain the parts, the reassembled ZIP, and the extracted images at the same time.

3. Reassemble and extract the images

Run these commands inside PanoVQA-data:

cat Panorama.zip.part-000 Panorama.zip.part-001 Panorama.zip.part-002 > Panorama.zip
unzip Panorama.zip

For an optional integrity check, the SHA-256 of the reassembled Panorama.zip is:

8a2247a1772e858c9858d2de74a7bfb0669331d95c856d76a7a6d0a33e62214a

On Linux, compute it with sha256sum Panorama.zip; on macOS, use shasum -a 256 Panorama.zip.

After extraction, your directory should look like this:

PanoVQA-data/
├── README.md
├── PanoVQA/
│   ├── NuScenes/
│   │   ├── train.json
│   │   └── val.json
│   ├── DeepAccident/
│   │   ├── train.json
│   │   └── val.json
│   └── BlendPASS/
│       └── val.json
└── Panorama/
    ├── NuScenes/
    ├── DeepAccident/
    └── BlendPASS/

Keep PanoVQA/ and Panorama/ next to each other. Once extraction succeeds, you may remove downloaded archive files to reclaim disk space; keep the extracted images and annotations.

Annotation format

Each annotation file is a JSON array, not JSON Lines. A record has these fields:

Field	Meaning
id	Identifier supplied for the question-answer record.
image	Image path relative to the directory containing the JSON file.
conversations	Two messages: a user question and an assistant answer. Each message has role and content.

For example, the first record in PanoVQA/NuScenes/train.json is:

{
  "id": "N1_train_1531883530449377_000000",
  "image": "../../Panorama/NuScenes/1531883530449377.jpg",
  "conversations": [
    {
      "role": "user",
      "content": "<image>\nSummarize the panoramic scene."
    },
    {
      "role": "assistant",
      "content": "Several traffic cones, three trucks, two cars, and two construction workers are visible, mostly in the front left and front directions; distances range roughly from 10 to 60 meters and all annotated objects are fully visible."
    }
  ]
}

The question begins with an <image> marker followed by a newline. JSON encodes that newline as \n; json.load decodes it automatically.

Load a record in Python

Run the following code from PanoVQA-data after extraction:

import json
from pathlib import Path

annotation_file = Path("PanoVQA/NuScenes/train.json")
with annotation_file.open(encoding="utf-8") as f:
    records = json.load(f)

sample = records[0]
image_path = (annotation_file.parent / sample["image"]).resolve()
assert image_path.is_file(), f"Missing image: {image_path}"

question = sample["conversations"][0]["content"]
answer = sample["conversations"][1]["content"]

print("ID:", sample["id"])
print("Image:", image_path)
print("Question:", question)
print("Answer:", answer)

To open the image, install Pillow (python -m pip install Pillow) and use:

from PIL import Image

with Image.open(image_path) as image:
    image = image.convert("RGB")
    print(image.size)
    # Pass the image and question to your model's processor.

Resolve image against annotation_file.parent, rather than the current working directory. JSON files contain paths, not embedded image data. Model-specific image preprocessing and chat templates must be applied by your training or evaluation code.

For training, use the supplied train.json files. For evaluation, use val.json, pass the image and user question to the model, and keep the assistant answer as the reference rather than including it in the input prompt. If combining sources, load their splits separately so each image path is resolved relative to its own annotation file.

Use the mini dataset

For a smaller download, use a separate directory:

hf download wakinghours/PanoVQA \
  --repo-type dataset \
  --local-dir ./PanoVQA-mini-data \
  --include "PanoVQA_mini/**" \
  --include "Panorama_mini.zip" \
  --include "README.md" \
  --max-workers 2

cd PanoVQA-mini-data
unzip Panorama_mini.zip
ln -s Panorama_mini Panorama

The mini ZIP is approximately 5.5 GB; allow about 12 GB for the ZIP and extracted images together.

The mini annotations also reference ../../Panorama/.... The archive extracts into Panorama_mini/, so the symbolic link above makes those existing paths resolve without modifying the annotations. Create it only in a directory where Panorama does not already exist. As an alternative, rename the extracted Panorama_mini directory to Panorama.

If you already extracted the full Panorama/ image set, download just PanoVQA_mini/** into the same dataset root. The mini annotations can use the full image set directly; neither the mini ZIP nor a symbolic link is needed.

Use the Python example above with, for example:

annotation_file = Path("PanoVQA_mini/NuScenes/train.json")

The mini directory also contains train_qwenvl.json and val_qwenvl.json where the corresponding split exists. These files use from / value message fields and human / gpt roles instead of role / content and user / assistant. The top-level id, image, and conversations fields are unchanged. Choose one representation per split; the alternate files do not add new examples. Adapt the representation to the input schema required by your training framework.

Troubleshooting

• Missing images: Check that extraction finished and resolve paths relative to the JSON file's directory. For mini-only downloads, check the Panorama symbolic link or directory name.
• ZIP extraction fails: Verify that all three full-archive parts are present, concatenate them in numeric order, and compare the resulting SHA-256 with the value above.
• Interrupted download: Rerun the same hf download command with the same --local-dir so completed files can be reused.
• Rate-limit errors: Wait for the server's indicated reset time before retrying. The examples use two download workers to limit concurrent requests.

License

This dataset repository declares the Apache License 2.0.