Dataset instructions

DIKI is trained and evaluated on MTIL benchmark from ZSCL. However, for the better consistency, we apply the dataset splits from CoOp.

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/
|–– caltech-101/
|–– oxford_pets/
|–– stanford_cars/
|-- ...

The instructions to prepare each dataset are detailed below.

Caltech101

• Create a folder named caltech-101/ under $DATA.
• Download 101_ObjectCategories.tar.gz from https://data.caltech.edu/records/mzrjq-6wc02 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.
• The original download link has been broken, please refer to https://github.com/pytorch/vision/issues/7545#issuecomment-1631441616 for solution.
• 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

MNIST

• Create a folder named mnist/ under $DATA.
• Download four files from http://yann.lecun.com/exdb/mnist/ and put them into $DATA/mnist/.

The directory structure should look like

mnist/
|–– t10k-images-idx3-ubyte
|–– t10k-labels-idx1-ubyte
|–– train-images-idx3-ubyte
|–– train-labels-idx1-ubyte

CIFAR100

• Create a folder named cifar100/ under $DATA.
• Download the dataset from https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz and extract it to $DATA/cifar100/.

The directory structure should look like

cifar100/
|–– meta
|–– test
|–– train