README.md

metadata

pretty_name: SciFormaBench-2K Full-Reference Backup
license: other
task_categories:
  - text-to-image
language:
  - en
size_categories:
  - 1K<n<10K
tags:
  - scientific-diagrams
  - benchmark
  - evaluation
  - methodology-figures
configs:
  - config_name: simple
    data_files:
      - split: test
        path: data/simple.parquet
  - config_name: medium
    data_files:
      - split: test
        path: data/medium.parquet
  - config_name: hard
    data_files:
      - split: test
        path: data/hard.parquet

SciFormaBench-2K: Full-Reference Benchmark Backup

     

This repository is the complete-reference backup of SciFormaBench-2K, the official evaluation benchmark for SciForma. It contains all 2,000 reference images, prompts, target resolutions, provenance fields, and Component/Arrow/Text structural rubrics.

Features

• 🖼️ Complete reference set. All 2,000 ground-truth scientific methodology diagrams are embedded in the Parquet files.
• 🧩 Official benchmark splits. Simple/Medium/Hard contain 500/900/600 samples with the original benchmark indices and ordering.
• 🎯 Axis-specific evaluation. Each sample includes independently verified Component, Arrow, and Text inventories.
• 🔗 Source-linked records. Each row retains its arXiv ID, year, source image path, prompt, and target dimensions.
• ✅ Complete text rubrics. rubric_text is populated from the official stage1_abstraction.text_labels; 1,873 rows contain non-empty text-label inventories.

Usage

import json
from datasets import load_dataset

dataset = load_dataset(
    "LoYuXrqw/SciFormaBench-2K",
    "hard",                    # simple / medium / hard
    split="test",
    token=True,
)

sample = dataset[0]
sample["gt_image"].show()

components = json.loads(sample["rubric_components"])
arrows = json.loads(sample["rubric_arrows"])
text_labels = json.loads(sample["rubric_text"])
print(sample["index"], len(components), len(arrows), len(text_labels))

Set HF_TOKEN or log in with hf auth login before loading this repository.

Evaluation

Use the official SciForma evaluation pipeline:

python eval/eval_benchmark.py \
  --gen_dir ./results/my_model \
  --output_dir ./eval_results/my_model \
  --hf_benchmark LoYuXrqw/SciFormaBench-2K \
  --levels hard \
  --num_retest 2 \
  --deployment_name gpt-5.4

Use --levels simple medium hard for the complete benchmark. The paper uses GPT-5.4 as the judge; GPT-4o is the public alternative. The official protocol uses axis-specific calls with split_dims=True and averages two evaluation rounds with num_retest=2.

License

This full-reference repository is maintained for internal preservation and evaluation. It does not provide a new redistribution license for the embedded figures. Each image remains governed by its original source-paper license.