README.md

metadata

dataset_info:
  - config_name: real
    features:
      - name: question_index
        dtype: int64
      - name: image_id
        dtype: string
      - name: question
        dtype: string
      - name: answer
        dtype: string
      - name: question_category
        dtype: string
      - name: question_type
        dtype: string
      - name: target_object
        dtype: string
      - name: objects
        sequence: string
      - name: id
        dtype: string
      - name: image
        dtype: image
    splits:
      - name: test
        num_bytes: 1383586783.875
        num_examples: 5439
    download_size: 559763548
    dataset_size: 1383586783.875
  - config_name: sim
    features:
      - name: question_index
        dtype: int64
      - name: image_id
        dtype: string
      - name: question
        dtype: string
      - name: answer
        dtype: string
      - name: question_category
        dtype: string
      - name: question_type
        dtype: string
      - name: target_object
        dtype: string
      - name: objects
        sequence: string
      - name: id
        dtype: string
      - name: image
        dtype: image
    splits:
      - name: test
        num_bytes: 10278779977.424
        num_examples: 22644
    download_size: 2894555522
    dataset_size: 10278779977.424
configs:
  - config_name: real
    data_files:
      - split: test
        path: real/test-*
  - config_name: sim
    data_files:
      - split: test
        path: sim/test-*

PercepTax — Benchmarking Physical Intelligence through Cross-Property Reasoning in Vision-Language Models

PercepTax is an open-ended visual-question-answering benchmark that tests whether vision-language models can reason across physical object properties — material, shape, function, affordance — together with spatial relations and compositional / counterfactual reasoning. In each question the object of interest is marked by a colored box, and the model must answer in free form (no multiple-choice options in the prompt).

Subsets

Config	Source	Questions	Images
real	Real photos (OpenImages) with 3D annotation	5,439	2,516
sim	Rendered simulated indoor scenes	22,644	14,499

Each subset has a single test split. Question categories: taxonomy_reasoning · taxonomy_description · spatial_relation (fine types in question_type, e.g. compositional_set_subtraction_container, repurposing_shield_concept, spatial_above_below).

Fields

Field	Type	Description
image	Image	scene image; the queried object is marked by a colored box
question	string	open-ended question
answer	string	ground-truth answer (object name, or a spatial direction)
question_category	string	coarse category
question_type	string	fine-grained type
target_object	string	object the question is about
objects	list[string]	objects referenced in the scene
image_id	string	source image / scene id
id / question_index	string / int	identifiers

Quick start

from datasets import load_dataset

ds = load_dataset("RyanWW/PercepTaxBench", "real", split="test")   # or "sim"
ex = ds[0]
ex["image"]                      # PIL.Image (object marked by a colored box)
print(ex["question"], "->", ex["answer"], f"({ex['question_category']})")

Running a model (inference + scoring)

Open-ended answers are scored by normalized exact match with an optional LLM judge for paraphrases. Minimal, model-agnostic loop:

from datasets import load_dataset

ds = load_dataset("RyanWW/PercepTaxBench", "real", split="test")

def predict(image, question):
    # plug in any VLM (HF transformers, or an API such as GPT-4o / Gemini / Claude)
    ...

def norm(s): return " ".join(s.lower().split())

correct = 0
for ex in ds:
    pred = predict(ex["image"], ex["question"])
    correct += norm(pred) == norm(ex["answer"])      # swap in an LLM judge for paraphrase credit
print("accuracy:", correct / len(ds))

For a full, reproducible evaluation harness (per-category accuracy, LLM-judge scoring across many VLMs), use the VLMEvalKit integration in the code repo: 👉 https://github.com/XingruiWang/PercepTaxBench#inference--evaluation

License & citation

CC-BY-4.0.

@inproceedings{lee2026perceptax,
  title     = {PercepTax: Benchmarking Physical Intelligence through Cross-Property Reasoning in Vision-Language Models},
  author    = {Lee, Jonathan and Wang, Xingrui and Peng, Jiawei and Ye, Luoxin and Zheng, Zehan and Zhang, Tiezheng and Wang, Tao and Ma, Wufei and Chen, Siyi and Chou, Yu-Cheng and Kaushik, Prakhar and Yuille, Alan},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year      = {2026}
}