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MMBench Multimodal Evaluation

Multimodal evaluation benchmark combining image understanding with multiple-choice questions, based on MMBench (Liu et al., ECCV 2024). Annotators answer image-based questions, provide explanations, and tag the required perception or reasoning skills.

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Fichier de configurationconfig.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

Données d'exemplesample-data.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

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View on GitHub

Clone or download from the repository

Démarrage rapide :

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

Détails

Types d'annotation

radiotextmultiselect

Domaine

MultimodalComputer Vision

Cas d'utilisation

Multimodal EvaluationVisual Question AnsweringVLM Benchmarking

Étiquettes

mmbenchmultimodalvisual-qamultiple-choiceeccv2024

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