File size: 2,478 Bytes
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pretty_name: 'Pix2Fact: When Vision Is Not Enough — Benchmarking Fine-Grained VQA
with Web Verification on High-Resolution Real-World Scenes'
dataset_info:
features:
- name: image
dtype: image
- name: question
dtype: string
- name: answer
dtype: string
- name: image_url
dtype: string
- name: index
dtype: string
- name: ItemID
dtype: string
- name: is_original_qa
dtype: string
- name: if_search_first
dtype: string
- name: search_query
dtype: string
- name: local_image_path
dtype: string
- name: image_description
dtype: string
- name: bounding_box
dtype: string
- name: evidence_1
dtype: string
- name: evidence_2
dtype: string
- name: evidence_3
dtype: string
- name: evidence_url_1
dtype: string
- name: evidence_url_2
dtype: string
- name: evidence_url_3
dtype: string
- name: caption
dtype: string
- name: category
dtype: string
- name: confidence
dtype: string
- name: rebalanced
dtype: string
- name: image_resolution
dtype: string
splits:
- name: train
num_bytes: 8240548814
num_examples: 1000
download_size: 8230119774
dataset_size: 8240548814
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Pix2Fact: When Vision Is Not Enough — Benchmarking Fine-Grained VQA with Web Verification on High-Resolution Real-World Scenes
🌐 **Website:** https://fanfan7589.github.io/pix2fact/
📄 **Paper:** https://arxiv.org/abs/2602.00593
Pix2Fact is a visual question-answering benchmark designed to assess expert-level visual
perception and knowledge search. It comprises **1,000 high-resolution (4K+) images** spanning
eight real-world scenarios, with question–answer pairs meticulously crafted by PhD-holding
annotators. Each question requires both **fine-grained visual grounding** and the integration
of **external (web) knowledge**.
## Usage
```python
from datasets import load_dataset
ds = load_dataset("pix2fact/Pix2FactBenchmark", split="train")
print(ds[0]["question"], ds[0]["answer"])
ds[0]["image"] # PIL.Image
```
## Fields
- `image` — the high-resolution scene image
- `question` / `answer` — the QA pair
- `category` — one of the eight scenario categories
- `search_query`, `evidence_*`, `evidence_url_*` — supporting search queries / evidence
- `image_description`, `caption`, `bounding_box`, `image_resolution`, and other metadata
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