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Image Clustering
Image clustering is an important task in the field of computer vision, aiming to divide a dataset of images into semantically meaningful clusters without accessing ground truth labels. This task automatically discovers the inherent structure and patterns within images through unsupervised learning methods, thereby enabling effective organization and management of unlabeled images. Image clustering holds significant value in applications such as image retrieval, data mining, and content analysis.
CIFAR-10
SCAN
CIFAR-100
HUME
STL-10
RUC
Imagenet-dog-15
MAE-CT (best)
ImageNet-10
DCCM
USPS
SPC
MNIST-full
SPC
Tiny-ImageNet
PRO-DSC
Fashion-MNIST
N2D (UMAP)
ImageNet
TURTLE (CLIP + DINOv2)
MNIST-test
DynAE
coil-100
JULE-RC
Extended Yale-B
DMSC
Coil-20
JULE-RC
Stanford Cars
FineGAN
ImageNet-200
TEMI CLIP ViT-L (openai)
ImageNet-100
ImageNet-50
Stanford Dogs
FineGAN
CMU-PIE
CUB Birds
FineGAN
UMist
J-DSSC (Scattered)
YouTube Faces DB
JULE-RC
MNIST
HAR
N2D (UMAP)
coil-40
A-DSSC (Scattered)
FRGC
DEPICT
pendigits
N2D (UMAP)
EMNIST-Balanced
AE+SNNL
UCF101
LetterA-J
DDC-DA
DTD
TURTLE (CLIP + DINOv2)
RESISC45
SUN397
Country211
FGVC Aircraft
CARS196
ARL Polarimetric Thermal Face Dataset
Oxford-IIIT Pets
EuroSAT
Rendered SST2
TURTLE (CLIP + DINOv2)
Flowers-102
CIFAR-20
imagenet-1k
TAC
PCam
CUB-200-2011
Kinetics-700
FER2013
Hateful Memes
GTSRB
KITTI
Caltech-101
CLEVR Counts
Food-101
Birdsnap