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---
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
---

[![Visualize Dataset on Visual Layer](https://img.shields.io/badge/Visualize%20on-%20Visual%20Layer-purple?style=for-the-badge&logo=numpy)](https://app.visual-layer.com/dataset/acd886ce-2b9f-11ef-bb10-e605d78f584b/data?p=1&page=1)

<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/6195f404c07573b03c61702c/jQPvVpNJBB6M_9Mcun5eb.mp4"></video>

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

More in this [notebook](usage.ipynb).

## 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](https://app.visual-layer.com/dataset/acd886ce-2b9f-11ef-bb10-e605d78f584b/data?p=1&page=1). No sign-up required.

<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/6195f404c07573b03c61702c/jQPvVpNJBB6M_9Mcun5eb.mp4"></video>

<div style="text-align: center;">
  <a href="https://app.visual-layer.com/dataset/acd886ce-2b9f-11ef-bb10-e605d78f584b/data?p=1&page=1">
    <img src="https://img.shields.io/badge/Visualize%20on-%20Visual%20Layer-purple?style=for-the-badge&logo=numpy" alt="Visualize Dataset on Visual Layer">
  </a>
</div>


## 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

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    <img style="width:200px; display: block; margin: 0 auto;" alt="logo" src="https://d2iycffepdu1yp.cloudfront.net/design-assets/VL_horizontal_logo.png">
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