metadata

dataset_info:
  features:
    - name: image_id
      dtype: string
    - name: image
      dtype: image
    - name: objects
      struct:
        - name: bbox
          sequence:
            sequence: float32
        - name: categories
          sequence:
            class_label:
              names:
                '0': person
                '1': bicycle
                '2': car
                '3': motorcycle
                '4': airplane
                '5': bus
                '6': train
                '7': truck
                '8': boat
                '9': traffic light
                '10': fire hydrant
                '11': stop sign
                '12': parking meter
                '13': bench
                '14': bird
                '15': cat
                '16': dog
                '17': horse
                '18': sheep
                '19': cow
                '20': elephant
                '21': bear
                '22': zebra
                '23': giraffe
                '24': backpack
                '25': umbrella
                '26': handbag
                '27': tie
                '28': suitcase
                '29': frisbee
                '30': skis
                '31': snowboard
                '32': sports ball
                '33': kite
                '34': baseball bat
                '35': baseball glove
                '36': skateboard
                '37': surfboard
                '38': tennis racket
                '39': bottle
                '40': wine glass
                '41': cup
                '42': fork
                '43': knife
                '44': spoon
                '45': bowl
                '46': banana
                '47': apple
                '48': sandwich
                '49': orange
                '50': broccoli
                '51': carrot
                '52': hot dog
                '53': pizza
                '54': donut
                '55': cake
                '56': chair
                '57': couch
                '58': potted plant
                '59': bed
                '60': dining table
                '61': toilet
                '62': tv
                '63': laptop
                '64': mouse
                '65': remote
                '66': keyboard
                '67': cell phone
                '68': microwave
                '69': oven
                '70': toaster
                '71': sink
                '72': refrigerator
                '73': book
                '74': clock
                '75': vase
                '76': scissors
                '77': teddy bear
                '78': hair drier
                '79': toothbrush
        - name: area
          sequence: float32
        - name: iscrowd
          sequence: bool
    - name: issues
      list:
        - name: confidence
          dtype: float64
        - name: description
          dtype: 'null'
        - name: issue_type
          dtype: string
  splits:
    - name: train
      num_bytes: 13410501369
      num_examples: 82081
    - name: validation
      num_bytes: 6593725253
      num_examples: 40137
    - name: test
      num_bytes: 6653522091
      num_examples: 40775
  download_size: 26604054770
  dataset_size: 26657748713
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
task_categories:
  - object-detection

COCO-2014-VL-Enriched

An enriched version of the COCO 2014 dataset with label issues! The label issues helps to curate a cleaner and leaner dataset.

Description

The dataset consists of 6 columns:

image_id: The original image filename from the COCO dataset.
image: Image data in the form of PIL Image.
label_bbox: Bounding box annotations from the COCO dataset. Consists of bounding box coordinates, confidence scores, and labels for the bounding box generated using object detection models.
issues: Quality issues found such as duplicate, mislabeled, dark, blurry, bright, and outlier images.

Usage

This dataset can be used with the Hugging Face Datasets library.:

import datasets
ds = datasets.load_dataset("visual-layer/coco-2014-vl-enriched")

Interactive Visualization

Visual Layer provides a platform to interactively visualize a dataset and highlight quality issues such as duplicates, mislabels, outliers, etc. Check it out here. No sign-up required.

License & Disclaimer

We provide no warranty on the dataset, and the user takes full responsibility for the usage of the dataset. By using the dataset, you agree to the terms of the ImageNet-1K dataset license.

About Visual Layer