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MS COCO Object Detection & Categorization

Object detection annotation following the MS COCO format (Lin et al., ECCV 2014). Select all object categories present in each image and mark object locations with bounding boxes, drawn from COCO's 80 common object categories.

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
# MS COCO Object Detection & Categorization Configuration
# Based on Lin et al., ECCV 2014

annotation_task_name: "MS COCO Object Detection & Categorization"
task_dir: "."

data_files:
  - "sample-data.json"

item_properties:
  id_key: "id"
  text_key: "image_url"
  context_key: "context"

user_config:
  allow_all_users: true

annotation_schemes:
  - annotation_type: "multiselect"
    name: "object_categories"
    description: "Select all object categories visible in the image"
    labels:
      - name: "person"
        tooltip: "Human figures, any age"
      - name: "bicycle"
        tooltip: "Bicycles, including parts"
      - name: "car"
        tooltip: "Cars, sedans, coupes"
      - name: "motorcycle"
        tooltip: "Motorcycles, scooters"
      - name: "airplane"
        tooltip: "Aircraft of any type"
      - name: "bus"
        tooltip: "Buses, shuttles"
      - name: "train"
        tooltip: "Trains, trams, metros"
      - name: "truck"
        tooltip: "Trucks, vans, pickups"
      - name: "boat"
        tooltip: "Boats, ships, watercraft"
      - name: "dog"
        tooltip: "Dogs of any breed"
      - name: "cat"
        tooltip: "Cats, domestic felines"
      - name: "horse"
        tooltip: "Horses, ponies"
      - name: "chair"
        tooltip: "Chairs, seats"
      - name: "couch"
        tooltip: "Sofas, couches"
      - name: "dining_table"
        tooltip: "Tables for dining"
      - name: "tv"
        tooltip: "Television sets, monitors"
      - name: "laptop"
        tooltip: "Laptop computers"
      - name: "cell_phone"
        tooltip: "Mobile phones"
      - name: "bottle"
        tooltip: "Bottles of any kind"
      - name: "cup"
        tooltip: "Cups, mugs, glasses"

  - annotation_type: "text"
    name: "bounding_boxes"
    description: "Draw bounding boxes around each object (format: category,x,y,width,height per line)"

  - annotation_type: "radio"
    name: "image_quality"
    description: "Rate the overall image quality for annotation"
    labels:
      - name: "excellent"
        tooltip: "Clear, well-lit, easy to annotate"
      - name: "good"
        tooltip: "Minor issues but annotatable"
      - name: "poor"
        tooltip: "Difficult to annotate due to quality"
      - name: "unusable"
        tooltip: "Cannot be reliably annotated"

interface_config:
  item_display_format: "<img src='{{text}}' style='max-width:100%; max-height:500px;'/><br/><small>{{context}}</small>"

output_annotation_format: "json"
output_annotation_dir: "annotations"

Sample Datasample-data.json

json
[
  {
    "id": "coco_001",
    "image_url": "https://images.cocodataset.org/val2017/000000397133.jpg",
    "context": "Street scene with various objects. Identify and mark all visible objects."
  },
  {
    "id": "coco_002",
    "image_url": "https://images.cocodataset.org/val2017/000000037777.jpg",
    "context": "Indoor scene. Identify all objects and their locations."
  }
]

// ... and 1 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/image/classification/ms-coco
potato start config.yaml

Dataset & paper

Lin et al., ECCV 2014

Official dataset ↗Read the paper ↗

Citation (BibTeX)

bibtex
@inproceedings{lin2014microsoft,
    title = "{Microsoft COCO}: Common Objects in Context",
    author = "Lin, Tsung-Yi  and Maire, Michael  and Belongie, Serge  and Hays, James  and Perona, Pietro  and Ramanan, Deva  and Doll{\'a}r, Piotr  and Zitnick, C. Lawrence",
    booktitle = "European Conference on Computer Vision (ECCV)",
    pages = "740--755",
    year = "2014",
    publisher = "Springer",
    url = "https://arxiv.org/abs/1405.0312"
}

Details

Annotation Types

multiselectradiotext

Domain

Computer VisionObject Detection

Use Cases

Object DetectionMulti-label ClassificationScene Understanding

Tags

cocoobject-detectionclassificationbounding-boxeccv2014

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