Skip to content

Latest commit

 

History

History
71 lines (56 loc) · 3.14 KB

README.md

File metadata and controls

71 lines (56 loc) · 3.14 KB
Raw

How to Download the Datasets

In this document, we introduce how to prepare the datasets used in this study.
When you use the pre-computed features to evaluate FID and IS, note that you do not need to download the datasets below.
After you download each dataset, please use the directory path, which includes the datasets below, as root for the Dataset classes in ../rqvae/img_datasets and ../rqvae/txtimg_datasets.
If you have already downloaded a dataset, you can use its path as root for the Dataset classes.

FFHQ

Before you download the FFHQ dataset, you can refer to the details in the official repository.
You can download the zip file for FFHQ images 1024x1024 at this link.
After downloading the zip file, please unzip it into the root directory for class FFHQ in ../rqvae/img_datasets/ffhq.py.

LSUN-{Church, Bedroom}

Before you download LSUN-{Church, Bedroom}, you can refer to the details in the official repository.
After cloning the official LSUN repository, you can easily download the two datasets using the scripts below.

git clone https://github.com/fyu/lsun.git
cd lsun
python3 download.py -c church_outdoor -o $CHURCH_DIR_FOR_ROOT # your root directory
python3 download.py -c bedroom -o $BEDROOM_DIR_FOR_ROOT # your root directory

LSUN-Cat

To download the LSUN-Cat dataset, you can refer to the official LSUN homepage.
Otherwise, use the codes below to download cat.zip and unzip it.

mkdir $CAT_DIR_FOR_ROOT # your root directory
cd $CAT_DIR_FOR_ROOT
wget http://dl.yf.io/lsun/objects/cat.zip
unzip cat.zip

If $CAT_DIR_FOR_ROOT does not exist, make $CAT_DIR_FOR_ROOT first.

ImageNet

For ImageNet, we use torchvision.datasets.ImageNet in this repository.
Since the ImageNet dataset is no longer publicly accessible, please download the train/val datasets.
Then, move the train/val datasets into a directory, which is used for root for torchvision.datasets.ImageNet.

Conceptual Captions (CC-3M)

For the CC-3M dataset, only Image URLs are provided instead of the image file.
To download the images and prepare (image_path, text) pairs, please refer to ./cc3m/README.md.

MS-COCO

You have to make a $COCO_ROOT_DIR directory. Then, make $COCO_ROOT_DIR/ìmages and $COCO_ROOT_DIR/annotations for downloading images and annotations, respectively.

mkdir $COCO_ROOT_DIR # your root directory
cd $COCO_ROOT_DIR
mkdir images
mkdir annotations

You can download the images and annotations at the official homepage. Of course, you can use the scripts below.

  • To download MS-COCO images
cd $COCO_ROOT_DIR/images
wget http://images.cocodataset.org/zips/val2014.zip
unzip val2014zip
  • To download MS-COCO annotations
cd $COCO_ROOT_DIR/annotations
wget https://twg.kakaocdn.net/brainrepo/etc/RQVAE/54599b4b2286fdc2252d927aa3fd55eb/captions_val2014_30K_samples.json