# APTOS 2019 Blindness Detection — Data

来源：https://www.kaggle.com/competitions/aptos2019-blindness-detection/data

本地 Meta Kaggle 快照；不是本次实时页面。

# Dataset Description

You are provided with a large set of retina images taken using [fundus photography](https://en.wikipedia.org/wiki/Fundus_photography) under a variety of imaging conditions.

A clinician has rated each image for the severity of [diabetic retinopathy](https://nei.nih.gov/health/diabetic/retinopathy) on a scale of 0 to 4:
> 0 - No DR

> 1 - Mild

> 2 - Moderate

> 3 - Severe

> 4 - Proliferative DR

Like any real-world data set, you will encounter noise in both the images and labels. Images may contain artifacts, be out of focus, underexposed, or overexposed. The images were gathered from multiple clinics using a variety of cameras over an extended period of time, which will introduce further variation.

## Files
In a synchronous Kernels-only competition, the files you can observe and download will be different than the private test set and sample submission. The files may have different ids, may be a different size, and may vary in other ways, depending on the problem. You should structure your code so that it returns predictions for the public test set images in the format specified by the public sample_submission.csv, but does not hard code aspects like the id or number of rows. When Kaggle runs your Kernel privately, it substitutes the private test set and sample submission in place of the public ones. 
You can plan on the private test set consisting of 20GB of data across 13,000 images (approximately). 

![Public vs Private](https://storage.googleapis.com/kaggle-media/competitions/general/public_vs_private.png)

*   **train.csv** - the training labels
*   **test.csv** - the test set (you must predict the `diagnosis` value for these variables)
*   **sample_submission.csv** - a sample submission file in the correct format
*   **train.zip** - the training set images
*   **test.zip** - the public test set images