Image Classification

Multi-label image classification with quality assessment for computer vision datasets.

🖼️

image annotation

Configuration Fileconfig.yaml

task_name: "Image Classification"

# Server configuration
server:
  port: 8000

# Image display settings
image:
  enabled: true
  max_width: 800
  max_height: 600
  zoom_enabled: true
  preserve_aspect_ratio: true

# Data configuration
data_files:
  - path: data/images.json
    image_field: image_path

# Annotation schemes
annotation_schemes:
  # Primary category (single choice)
  - annotation_type: radio
    name: primary_category
    description: "What is the main subject of this image?"
    labels:
      - Person/People
      - Animal
      - Vehicle
      - Building/Architecture
      - Nature/Landscape
      - Food
      - Object/Product
      - Text/Document
      - Other
    keyboard_shortcuts:
      "Person/People": "1"
      "Animal": "2"
      "Vehicle": "3"
      "Building/Architecture": "4"
      "Nature/Landscape": "5"
      "Food": "6"
      "Object/Product": "7"
      "Text/Document": "8"
      "Other": "9"

  # Additional tags (multi-label)
  - annotation_type: multiselect
    name: tags
    description: "Select all applicable tags"
    labels:
      - Indoor
      - Outdoor
      - Daytime
      - Nighttime
      - Close-up
      - Wide shot
      - Multiple subjects
      - Text visible
      - Watermark present

  # Scene type
  - annotation_type: radio
    name: scene_type
    description: "What type of scene is this?"
    labels:
      - Natural
      - Urban
      - Studio/Staged
      - Screenshot
      - Artwork/Illustration
      - Mixed/Unclear

  # Image quality assessment
  - annotation_type: likert
    name: image_quality
    description: "Rate the overall image quality"
    size: 5
    min_label: "Very poor"
    max_label: "Excellent"

  # Usability for training
  - annotation_type: radio
    name: usable_for_training
    description: "Is this image suitable for model training?"
    labels:
      - Yes - High quality
      - Yes - Acceptable
      - No - Quality issues
      - No - Content issues
      - Unsure

  # Quality issues (if any)
  - annotation_type: multiselect
    name: quality_issues
    description: "Select any quality issues (if applicable)"
    labels:
      - Blurry
      - Too dark
      - Too bright
      - Low resolution
      - Cropped badly
      - Watermark obstructs content
      - NSFW content
      - Duplicate
      - No issues

# User settings
allow_all_users: true
instances_per_annotator: 200
annotation_per_instance: 2

# Output
output:
  path: annotations/
  format: json

Get This Design

This design is available in our showcase. Copy the configuration below to get started.

Quick start:

# Create your project folder
mkdir image-classification
cd image-classification
# Copy config.yaml from above
potato start config.yaml

Details

Annotation Types

multiselectradiolikert

Domain

Computer Vision

Use Cases

classificationimage labelingdataset creation

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