ProjectNeura/Varest_Dataset

E2 Benchmark

An efficiency benchmark of 180 VQA cases in two sweeps. Each sweep varies one workload dimension while holding the others fixed, so latency can be measured against that axis. Token counts use LLaVA-1.5's tokenizer and prompt format. Images are unmodified, so visual tokens are constant only for fixed-resolution models.

category	varied axis (level)	levels	cases	held fixed
input_text_tokens	prompt tokens	16 (57 to 74)	80	answer 1 to 11 tokens
reference_answer_tokens	gold answer tokens	20 (1 to 21)	100	prompt 59 to 63 tokens

Each level has 5 distinct cases.

Sources

dataset	split	cases
AI2D	test	8
ChartQA	test (human)	37
DocVQA	val	65
GQA	testdev balanced	23
TextVQA	val	47

AI2D questions that refer to diagram letters use the ABC-labelled version of the diagram (images/AI2D_abc/). AI2D answers are given as option text, not letters.

Layout

varest_benchmark.json    # all cases
images/<dataset>/    # source images

Fields

• question_id: unique case id
• image: image path, relative to this folder
• text: question
• answer, answers: primary gold answer, all gold answers
• category, level: sweep and its value for this case
• evaluator: scoring method (ANLS for DocVQA, relaxed accuracy for ChartQA, VQA soft accuracy for TextVQA, exact match for GQA, option match for AI2D)
• source: original dataset, split, question id and file paths within the original dataset release
• workload: visual, prompt and answer token counts, and image size