VisFactor

Overview

VisFactor evaluates foundational visual cognition in multimodal large language models using 20 vision-centric subtests adapted from the Factor-Referenced Cognitive Test (FRCT). It isolates abilities that support higher-level visual reasoning instead of measuring performance on a single downstream task.

Task Description

• Task Type: Visual cognition assessment with binary and short free-form questions

• Input: One to four images interleaved with a task-specific instruction

• Output: A JSON object containing a boolean, word, number, coordinate pair, or letter answer

• Domain: Visualization and spatial processing, perceptual closure, visual memory, and reasoning

Key Features

• Contains 3,046 rows representing 808 test items across 20 FRCT subtests

• Uses rule-based variants and grouped consistency checks to reduce average chance performance to approximately 2.9%

• Preserves the official zero-shot prompts and their image ordering from the VLMEvalKit implementation

• Covers hidden figures, gestalt completion, visual memory, mental rotation, path finding, paper folding, and related abilities

Evaluation Notes

• Uses the test split from the ModelScope mirror of the official VisFactor.tsv

• Extracts the last {"answer": ...} object and applies the official category-specific normalization rules

• A logical test item may contain multiple rows and receives credit only when every row is correct

• Reports each subtest’s item-level accuracy; the primary score is the unweighted macro-average over represented subtests

• Scoring is deterministic and does not require an LLM judge

Properties

Property	Value
Benchmark Name	visfactor
Dataset ID	lmms-lab-encoder/visfactor
Paper	Paper
Tags	MultiModal, QA, Reasoning
Metrics	accuracy
Default Shots	0-shot
Evaluation Split	test

Data Statistics

Metric	Value
Total Samples	3,046
Prompt Length (Mean)	463.45 chars
Prompt Length (Min/Max)	188 / 932 chars

Image Statistics:

Metric	Value
Total Images	6,048
Images per Sample	min: 1, max: 4, mean: 1.99
Resolution Range	100x100 - 668x911
Formats	jpeg

Sample Example

Subset: default

{
  "input": [
    {
      "id": "1a4f53fe",
      "content": [
        {
          "text": "Look at the two images:\n\nBelow is the first image, one simple shape:"
        },
        {
          "image": "[BASE64_IMAGE: jpeg, ~2.6KB]"
        },
        {
          "text": "Below is the second image, a larger, complex pattern:"
        },
        {
          "image": "[BASE64_IMAGE: jpeg, ~8.5KB]"
        },
        {
          "text": "Task: Decide whether the shape in the first image is hidden anywhere inside the second image. The shape will never be rotated, flipped, or resized. The shape will always be right-side-up and exactly the same size as in the first image.\n\nOutput: Respond with only one word: “TRUE” if it is present, “FALSE” if it is not, in JSON format as follows: {\"answer\": YOUR_ANSWER_HERE}."
        }
      ]
    }
  ],
  "target": "T",
  "id": 0,
  "group_id": 0,
  "metadata": {
    "index": 0,
    "category_id": "CF1",
    "category_name": "Hidden Figures Test",
    "eval_index": 0,
    "additional": ""
  }
}

Prompt Template

No prompt template defined.

Usage

Using CLI

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets visfactor \
    --limit 10  # Remove this line for formal evaluation

Using Python

from evalscope import run_task
from evalscope.config import TaskConfig

task_cfg = TaskConfig(
    model='YOUR_MODEL',
    api_url='OPENAI_API_COMPAT_URL',
    api_key='EMPTY_TOKEN',
    datasets=['visfactor'],
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)