# DataDownload

**Repository Path**: wang-tf/DataDownload

## Basic Information

- **Project Name**: DataDownload
- **Description**: https://github.com/Computer-Vision-in-the-Wild/DataDownload.git
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-12-31
- **Last Updated**: 2024-12-31

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Data Download
This repo contains scripts for downloading datasets and knowledge files of all tasks in [ELEVATER](https://computer-vision-in-the-wild.github.io/ELEVATER/). In this repo, we provide a central hub to all the datasets along with our collected knowledge files to facilitate future end-to-end training on ELEVATER benchmark. Note that:

- Our software toolkit supports automatic dataset downloading, and knowledge files are a part of toolkit.
- Please refer to the original license of the each dataset, and this benchmark is for academic research purpose.


## Image Classification

Note:  The data is on Azure Storage Blob, a SAS with Read permission is provided. Please append the following SAS at the end of each link to download: 
```bash
?sp=r&st=2023-08-28T01:41:20Z&se=3023-08-28T09:41:20Z&sv=2022-11-02&sr=c&sig=Msoq5dIl%2Fve6F01edGr8jgcZUt7rtsuJ896xvstSNfM%3D
```

Install

```Shell
pip install vision-datasets==0.2.17
cd classification
```

Download all datasets

```Shell
python download_datasets.py --ds all
```

Download a specific dataset

```Shell
python download_datasets.py --ds `DATASET_NAME`

# `DATASET_NAME` should be one of ['caltech-101', 'cifar-10', 'cifar-100', 'country211', 'dtd', 'eurosat_clip', 'fer-2013', 'fgvc-aircraft-2013b-variants102', 'food-101', 'gtsrb', 'hateful-memes', 'kitti-distance', 'mnist', 'oxford-flower-102', 'oxford-iiit-pets', 'patch-camelyon', 'ping-attack-on-titan-plus', 'ping-whiskey-plus', 'rendered-sst2', 'resisc45_clip', 'stanford-cars', 'voc-2007-classification']
```

Download knowledge files

```Shell
python download_knowledge.py
```

## Object Detection
```Shell
cd detection
```

Download all datasets
```
python download.py
```

Download a specific dataset
```
python download.py --dataset_names `DATASET_NAME`
```

Knowledge files are already included in the repo
```
detection/*yaml
```