---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label_code
    dtype: int64
  - name: label
    dtype: string
  splits:
  - name: train
    num_bytes: 249970283.654
    num_examples: 3662
  download_size: 249981721
  dataset_size: 249970283.654
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: mit
---

# Dataset Card for Dataset Name

<!-- Provide a quick summary of the dataset. -->

This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).

## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->
**Asia Pacific Tele-Ophthalmology Society (APTOS)** dataset. The images consist of retina scan images to detect diabetic retinopathy. 
The original dataset is available at [APTOS 2019 Blindness Detection](https://www.kaggle.com/c/aptos2019-blindness-detection/data). 
These images are resized into 224x224 pixels so that they can be readily used with many pre-trained deep learning models.

- **Funded by [optional]:** Asia Pacific Tele-Ophthalmology Society (APTOS).
- **Shared by:** [Sovit Ranjan Rath](https://www.kaggle.com/sovitrath)
- **License:** MIT

### Dataset Sources [optional]

<!-- Provide the basic links for the dataset. -->

- **Repository:**
  - [kaggle dataset](https://www.kaggle.com/datasets/sovitrath/diabetic-retinopathy-224x224-2019-data/)
  - [Original location (full dataset)](https://www.kaggle.com/c/aptos2019-blindness-detection/data)

## Uses

<!-- Address questions around how the dataset is intended to be used. -->

### Direct Use

<!-- This section describes suitable use cases for the dataset. -->

Diabetic retinopathy classification (binary or multiclass).
Feature extraction (unsupervised or self supervised learning).

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->

[More Information Needed]

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

There are no predefined partitions in this dataset; it is up to the user to decide how to split the data.

## Dataset Creation

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->

*Resizing*: The images were resized to 224x224.

### Source Data

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

From the description of the dataset we know that Aravind technicians travelled to rural areas in India to capture the images. 

#### Who are the source data producers?

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->

Aravind Eye Hospital.


#### Annotation process

<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->

A clinician has rated each image for the severity of diabetic retinopathy on a scale of 0 to 4:

0 - No DR

1 - Mild

2 - Moderate

3 - Severe

4 - Proliferative DR

#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

This dataset **only contains a subset of the original dataset**, the training split. The images have been resized by [Sovit Ranjan Rath](https://www.kaggle.com/sovitrath).

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

Karthik, Maggie, and Sohier Dane. APTOS 2019 Blindness Detection. https://kaggle.com/competitions/aptos2019-blindness-detection, 2019. Kaggle.

## Glossary 

<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->

[More Information Needed]

## More Information 

[More Information Needed]

## Dataset Card Authors 

bumbledeep