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--- |
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dataset_info: |
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features: |
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- name: image_id |
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dtype: string |
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- name: image |
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dtype: image |
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- name: objects |
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struct: |
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- name: bbox |
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sequence: |
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sequence: float32 |
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- name: categories |
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sequence: |
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class_label: |
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names: |
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'0': person |
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'1': bicycle |
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'2': car |
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'3': motorcycle |
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'4': airplane |
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'5': bus |
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'6': train |
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'7': truck |
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'8': boat |
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'9': traffic light |
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'10': fire hydrant |
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'11': stop sign |
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'12': parking meter |
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'13': bench |
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'14': bird |
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'15': cat |
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'16': dog |
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'17': horse |
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'18': sheep |
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'19': cow |
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'20': elephant |
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'21': bear |
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'22': zebra |
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'23': giraffe |
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'24': backpack |
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'25': umbrella |
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'26': handbag |
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'27': tie |
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'28': suitcase |
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'29': frisbee |
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'30': skis |
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'31': snowboard |
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'32': sports ball |
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'33': kite |
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'34': baseball bat |
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'35': baseball glove |
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'36': skateboard |
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'37': surfboard |
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'38': tennis racket |
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'39': bottle |
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'40': wine glass |
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'41': cup |
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'42': fork |
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'43': knife |
|
'44': spoon |
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'45': bowl |
|
'46': banana |
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'47': apple |
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'48': sandwich |
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'49': orange |
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'50': broccoli |
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'51': carrot |
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'52': hot dog |
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'53': pizza |
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'54': donut |
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'55': cake |
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'56': chair |
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'57': couch |
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'58': potted plant |
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'59': bed |
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'60': dining table |
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'61': toilet |
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'62': tv |
|
'63': laptop |
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'64': mouse |
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'65': remote |
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'66': keyboard |
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'67': cell phone |
|
'68': microwave |
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'69': oven |
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'70': toaster |
|
'71': sink |
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'72': refrigerator |
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'73': book |
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'74': clock |
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'75': vase |
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'76': scissors |
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'77': teddy bear |
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'78': hair drier |
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'79': toothbrush |
|
- name: area |
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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) |
|
|
|
<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. |
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+ `image`: Image data in the form of PIL Image. |
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+ `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. |
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+ `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 |
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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 |
|
|
|
<div style="text-align: center; margin-top:50px;"> |
|
<a href="https://visual-layer.com/" style="padding:10px; display: inline-block;"> |
|
<img alt="site" src="https://vl-blog.s3.us-east-2.amazonaws.com/imgs/web.png" width="50"></a> |
|
<a href="https://medium.com/visual-layer" style="padding:10px; display: inline-block;"> |
|
<img alt="blog" src="https://vl-blog.s3.us-east-2.amazonaws.com/imgs/forum.png" width="50"></a> |
|
<a href="https://github.com/visual-layer/fastdup" style="padding:10px; display: inline-block;"> |
|
<img alt="github" src="https://vl-blog.s3.us-east-2.amazonaws.com/imgs/github.png" width="50"></a> |
|
<a href="https://discord.com/invite/Dqw458EG/" style="padding:10px; display: inline-block;"> |
|
<img alt="slack" src="https://vl-blog.s3.us-east-2.amazonaws.com/imgs/discord.png" width="50"></a> |
|
<a href="https://www.linkedin.com/company/visual-layer/" style="padding:10px; display: inline-block;"> |
|
<img alt="linkedin" src="https://vl-blog.s3.us-east-2.amazonaws.com/imgs/linkedin.png" width="50"></a> |
|
<a href="https://www.youtube.com/@visual-layer" style="padding:10px; display: inline-block;"> |
|
<img alt="youtube" src="https://vl-blog.s3.us-east-2.amazonaws.com/imgs/youtube.png" width="50"></a> |
|
<a href="https://twitter.com/visual_layer" style="padding:10px; display: inline-block;"> |
|
<img alt="twitter" src="https://vl-blog.s3.us-east-2.amazonaws.com/imgs/x.png" width="50"></a> |
|
</div> |
|
<div style="text-align: center;"> |
|
<img style="width:200px; display: block; margin: 0 auto;" alt="logo" src="https://d2iycffepdu1yp.cloudfront.net/design-assets/VL_horizontal_logo.png"> |
|
<div style="margin-top:20px;">Copyright © 2024 Visual Layer. All rights reserved.</div> |
|
</div> |
