DATASET.md

How to install datasets

We suggest putting all datasets under the same folder (say $DATA) to ease management and following the instructions below to organize datasets to avoid modifying the source code. The file structure looks like

$DATA/
|–– imagenet/
|–– caltech-101/
|–– oxford_pets/
|–– stanford_cars/

If you have some datasets already installed somewhere else, you can create symbolic links in $DATA/dataset_name that point to the original data to avoid duplicate download.

Datasets list:

• ImageNet
• Caltech101
• OxfordPets
• StanfordCars
• Flowers102
• Food101
• FGVCAircraft
• SUN397
• DTD
• EuroSAT
• UCF101

The instructions to prepare each dataset are detailed below. To ensure reproducibility and fair comparison for future work, we utilize CoOp-style train/val/test splits for all datasets except ImageNet where the validation set is used as test set.

ImageNet

• Create a folder named imagenet/ under $DATA.
• Create images/ under imagenet/.
• Download the dataset from the official website and extract the training and validation sets to $DATA/imagenet/images. The directory structure should look like

imagenet/
|–– images/
|   |–– train/ # contains 1,000 folders like n01440764, n01443537, etc.
|   |–– val/

• If you had downloaded the ImageNet dataset before, you can create symbolic links to map the training and validation sets to $DATA/imagenet/images.
• Download the classnames.txt to $DATA/imagenet/ from this link. The class names are copied from CLIP.

Caltech101

• Create a folder named caltech-101/ under $DATA.
• Download 101_ObjectCategories.tar.gz from http://www.vision.caltech.edu/Image_Datasets/Caltech101/101_ObjectCategories.tar.gz and extract the file under $DATA/caltech-101.
• Download split_zhou_Caltech101.json from this link and put it under $DATA/caltech-101.

The directory structure should look like

caltech-101/
|–– 101_ObjectCategories/
|–– split_zhou_Caltech101.json

OxfordPets

• Create a folder named oxford_pets/ under $DATA.
• Download the images from https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz.
• Download the annotations from https://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz.
• Download split_zhou_OxfordPets.json from this link.

The directory structure should look like

oxford_pets/
|–– images/
|–– annotations/
|–– split_zhou_OxfordPets.json

StanfordCars

• Create a folder named stanford_cars/ under $DATA.
• Download the train images http://ai.stanford.edu/~jkrause/car196/cars_train.tgz.
• Download the test images http://ai.stanford.edu/~jkrause/car196/cars_test.tgz.
• Download the train labels https://ai.stanford.edu/~jkrause/cars/car_devkit.tgz.
• Download the test labels http://ai.stanford.edu/~jkrause/car196/cars_test_annos_withlabels.mat.
• Download split_zhou_StanfordCars.json from this link.

The directory structure should look like

stanford_cars/
|–– cars_test\
|–– cars_test_annos_withlabels.mat
|–– cars_train\
|–– devkit\
|–– split_zhou_StanfordCars.json

Flowers102

• Create a folder named oxford_flowers/ under $DATA.
• Download the images and labels from https://www.robots.ox.ac.uk/~vgg/data/flowers/102/102flowers.tgz and https://www.robots.ox.ac.uk/~vgg/data/flowers/102/imagelabels.mat respectively.
• Download cat_to_name.json from here.
• Download split_zhou_OxfordFlowers.json from here.

The directory structure should look like

oxford_flowers/
|–– cat_to_name.json
|–– imagelabels.mat
|–– jpg/
|–– split_zhou_OxfordFlowers.json

Food101

• Download the dataset from https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/ and extract the file food-101.tar.gz under $DATA, resulting in a folder named $DATA/food-101/.
• Download split_zhou_Food101.json from here.

The directory structure should look like

food-101/
|–– images/
|–– license_agreement.txt
|–– meta/
|–– README.txt
|–– split_zhou_Food101.json

FGVCAircraft

• Download the data from https://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/archives/fgvc-aircraft-2013b.tar.gz.
• Extract fgvc-aircraft-2013b.tar.gz and keep only data/.
• Move data/ to $DATA and rename the folder to fgvc_aircraft/.

The directory structure should look like

fgvc_aircraft/
|–– images/
|–– ... # a bunch of .txt files

SUN397

• Create a folder named sun397/ under $DATA.
• Download the images http://vision.princeton.edu/projects/2010/SUN/SUN397.tar.gz.
• Download the partitions https://vision.princeton.edu/projects/2010/SUN/download/Partitions.zip.
• Extract these files under $DATA/sun397/.
• Download split_zhou_SUN397.json from this link.

The directory structure should look like

sun397/
|–– SUN397/
|–– split_zhou_SUN397.json
|–– ... # a bunch of .txt files

DTD

• Download the dataset from https://www.robots.ox.ac.uk/~vgg/data/dtd/download/dtd-r1.0.1.tar.gz and extract it to $DATA. This should lead to $DATA/dtd/.
• Download split_zhou_DescribableTextures.json from this link.

The directory structure should look like

dtd/
|–– images/
|–– imdb/
|–– labels/
|–– split_zhou_DescribableTextures.json

EuroSAT

• Create a folder named eurosat/ under $DATA.
• Download the dataset from http://madm.dfki.de/files/sentinel/EuroSAT.zip and extract it to $DATA/eurosat/.
• Download split_zhou_EuroSAT.json from here.

The directory structure should look like

eurosat/
|–– 2750/
|–– split_zhou_EuroSAT.json

UCF101

• Create a folder named ucf101/ under $DATA.
• Download the zip file UCF-101-midframes.zip from here and extract it to $DATA/ucf101/. This zip file contains the extracted middle video frames.
• Download split_zhou_UCF101.json from this link.

The directory structure should look like

ucf101/
|–– UCF-101-midframes/
|–– split_zhou_UCF101.json