Datasets:
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---
task_categories:
- object-detection
dataset_info:
features:
- name: image_id
dtype: string
- name: image
dtype: image
- name: label_bbox
list:
- name: bbox
sequence: int64
- name: bbox_id
dtype: string
- name: label
dtype: string
- name: issues
list:
- name: confidence
dtype: float64
- name: description
dtype: string
- name: issue_type
dtype: string
splits:
- name: train
num_bytes: 13436697177.0
num_examples: 82081
- name: validation
num_bytes: 6606403140.0
num_examples: 40137
- name: test
num_bytes: 6653024122.0
num_examples: 40775
download_size: 26617129269
dataset_size: 26696124439.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
[![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
<div style="text-align: center; margin-top:50px;">
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</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> |