---
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
---
[](https://app.visual-layer.com/dataset/acd886ce-2b9f-11ef-bb10-e605d78f584b/data?p=1&page=1)
# 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.
## 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
