README.md

metadata

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

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
• image_url — stable CDN URL of the image (.../resolve/main/images/<file>), also downloadable as a file in the repo
• 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