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MMBench: Multimodal Vision-Language Benchmark

MMBench is a multiple-choice benchmark of about 2,974 questions testing vision-language models across 20 ability dimensions (Liu et al., ECCV 2024). This Potato config reproduces image-question answering with skill tagging.

About this dataset

MMBench is a multimodal benchmark for evaluating vision-language models, built by Yuan Liu, Haodong Duan, and collaborators at Shanghai AI Laboratory and partner labs. It was published at ECCV 2024 (originally released in 2023) to test whether a model is an all-around player across many perception and reasoning skills.

The benchmark contains about 2,974 single-choice questions organized into a three-level ability taxonomy. The two L-1 dimensions are Perception and Reasoning, which split into 6 L-2 dimensions (coarse perception, fine-grained single-instance perception, fine-grained cross-instance perception, attribute reasoning, relation reasoning, and logic reasoning) and 20 L-3 leaf abilities.

Each item pairs an image with a question and multiple choices. MMBench scores models with CircularEval: a question with N choices is presented N times with the choices circularly shifted, and the model is counted correct only if it answers correctly on every pass. This penalizes guessing and position bias and gives a stricter accuracy than a single pass.

The Potato config below reproduces the MMBench task. Annotators view an image, pick the answer from a multiple-choice radio scheme, write a short explanation, and tag the perception or reasoning skills the question requires.

Questions
~2,974 single-choice
L-1 dimensions
2 (Perception, Reasoning)
L-2 dimensions
6
L-3 leaf abilities
20
Scoring
CircularEval (N passes per N-choice item)
Venue
ECCV 2024
Labels:outdoornatureurbanpeopleanimal+

Configuration Fileconfig.yaml

This Potato config reproduces the annotation task. Save it as config.yaml and run potato start config.yaml to try it.

yaml
# MMBench Multimodal Evaluation
# Based on Liu et al., ECCV 2024
# Paper: https://arxiv.org/abs/2307.06281
# Dataset: https://github.com/open-compass/MMBench
#
# Multimodal evaluation benchmark combining image understanding with
# multiple-choice questions. Tests a variety of visual perception and
# reasoning abilities. Annotators view an image, answer a multiple-choice
# question, explain their reasoning, and tag which skills are required.
#
# Answer Options:
# - A, B, C, D: Four possible answers; exactly one is correct
#
# Skill Tags (select all that apply):
# - Visual Perception: Identifying objects, colors, shapes
# - Spatial Reasoning: Understanding spatial relationships and layouts
# - OCR: Reading text in images
# - Object Recognition: Identifying specific objects or entities
# - Scene Understanding: Comprehending the overall scene or context
# - Knowledge: Requiring external knowledge beyond what's visible
#
# Annotation Guidelines:
# 1. Examine the image carefully
# 2. Read the question and all four options
# 3. Select the correct answer
# 4. Explain your reasoning
# 5. Tag which visual/reasoning skills are needed

annotation_task_name: "MMBench Multimodal Evaluation"
task_dir: "."

data_files:
  - sample-data.json

item_properties:
  id_key: "id"
  text_key: "text"

output_annotation_dir: "annotation_output/"
output_annotation_format: "json"

port: 8000
server_name: localhost

annotation_schemes:
  # Step 1: Select the correct answer
  - annotation_type: radio
    name: answer
    description: "Based on the image, select the correct answer."
    labels:
      - "A"
      - "B"
      - "C"
      - "D"
    keyboard_shortcuts:
      "A": "1"
      "B": "2"
      "C": "3"
      "D": "4"
    tooltips:
      "A": "Select option A"
      "B": "Select option B"
      "C": "Select option C"
      "D": "Select option D"

  # Step 2: Explanation
  - annotation_type: text
    name: explanation
    description: "Briefly explain your reasoning for the selected answer."
    textarea: true
    required: false
    placeholder: "Why did you choose this answer?"

  # Step 3: Required skills
  - annotation_type: multiselect
    name: required_skills
    description: "Which visual or reasoning skills are needed to answer this question? Select all that apply."
    labels:
      - "Visual Perception"
      - "Spatial Reasoning"
      - "OCR"
      - "Object Recognition"
      - "Scene Understanding"
      - "Knowledge"
    tooltips:
      "Visual Perception": "Identifying basic visual attributes like colors, shapes, sizes"
      "Spatial Reasoning": "Understanding spatial relationships, positions, and layouts"
      "OCR": "Reading or recognizing text visible in the image"
      "Object Recognition": "Identifying specific objects, animals, or entities"
      "Scene Understanding": "Comprehending the overall scene, context, or activity"
      "Knowledge": "Requiring external knowledge beyond what is visible in the image"

annotation_instructions: |
  You will evaluate multimodal questions from the MMBench benchmark.

  For each item:
  1. Examine the image carefully before reading the question.
  2. Read the question and all four answer options (A, B, C, D).
  3. Select the single correct answer based on the image.
  4. Briefly explain your reasoning.
  5. Tag which skills are required to answer this question.

  Tips:
  - Pay close attention to details in the image.
  - Some questions require reading text in the image (OCR).
  - Some questions require world knowledge beyond what's visible.

html_layout: |
  <div style="padding: 15px; max-width: 800px; margin: auto;">
    <div style="text-align: center; margin-bottom: 16px;">
      <img src="{{image_url}}" style="max-width: 100%; max-height: 500px; border: 1px solid #ddd; border-radius: 8px;" />
    </div>
    <div style="background: #f0f9ff; border: 1px solid #bae6fd; border-radius: 8px; padding: 16px; margin-bottom: 16px;">
      <strong style="color: #0369a1;">Question:</strong>
      <p style="font-size: 16px; line-height: 1.7; margin: 8px 0 0 0;">{{text}}</p>
    </div>
    <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 10px;">
      <div style="background: #f8fafc; border: 1px solid #e2e8f0; border-radius: 8px; padding: 12px;">
        <strong style="color: #475569;">A:</strong> {{option_a}}
      </div>
      <div style="background: #f8fafc; border: 1px solid #e2e8f0; border-radius: 8px; padding: 12px;">
        <strong style="color: #475569;">B:</strong> {{option_b}}
      </div>
      <div style="background: #f8fafc; border: 1px solid #e2e8f0; border-radius: 8px; padding: 12px;">
        <strong style="color: #475569;">C:</strong> {{option_c}}
      </div>
      <div style="background: #f8fafc; border: 1px solid #e2e8f0; border-radius: 8px; padding: 12px;">
        <strong style="color: #475569;">D:</strong> {{option_d}}
      </div>
    </div>
  </div>

allow_all_users: true
instances_per_annotator: 50
annotation_per_instance: 3
allow_skip: true
skip_reason_required: false

Sample Datasample-data.json

json
[
  {
    "id": "mmb_001",
    "text": "How many red apples are visible on the table?",
    "image_url": "https://example.com/mmbench/image_001.jpg",
    "option_a": "Two",
    "option_b": "Three",
    "option_c": "Four",
    "option_d": "Five"
  },
  {
    "id": "mmb_002",
    "text": "What is the person in the image doing?",
    "image_url": "https://example.com/mmbench/image_002.jpg",
    "option_a": "Reading a book",
    "option_b": "Cooking a meal",
    "option_c": "Playing a guitar",
    "option_d": "Writing on a whiteboard"
  }
]

// ... and 8 more items

Get This Design

View on GitHub

Clone or download from the repository

Quick start:

git clone https://github.com/davidjurgens/potato-showcase.git
cd potato-showcase/multimodal/mmbench-multimodal-eval
potato start config.yaml

Dataset & paper

Liu et al., ECCV 2024

Citation (BibTeX)

bibtex
@inproceedings{liu2024mmbench,
    title = "{MMB}ench: Is Your Multi-modal Model an All-around Player?",
    author = "Liu, Yuan and Duan, Haodong and Zhang, Yuanhan and Li, Bo and Zhang, Songyang and Zhao, Wangbo and Yuan, Yike and Wang, Jiaqi and He, Conghui and Liu, Ziwei and Chen, Kai and Lin, Dahua",
    booktitle = "European Conference on Computer Vision",
    year = "2024",
    url = "https://arxiv.org/abs/2307.06281"
}

Details

Annotation Types

radiotextmultiselect

Domain

MultimodalComputer Vision

Use Cases

Multimodal EvaluationVisual Question AnsweringVLM Benchmarking

Tags

mmbenchmultimodalvisual-qamultiple-choiceeccv2024

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