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
MNIST-full
SPC
USPS
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
ImageNet-100
ImageNet-200
TEMI CLIP ViT-L (openai)
ImageNet-50
Stanford Cars
FineGAN
CMU-PIE
CUB Birds
FineGAN
Stanford Dogs
FineGAN
UMist
J-DSSC (Scattered)
YouTube Faces DB
JULE-RC
coil-40
A-DSSC (Scattered)
FRGC
DEPICT
HAR
N2D (UMAP)
MNIST
DTD
TURTLE (CLIP + DINOv2)
EMNIST-Balanced
AE+SNNL
LetterA-J
DDC-DA
pendigits
N2D (UMAP)
UCF101
ARL Polarimetric Thermal Face Dataset
Birdsnap
Caltech-101
CARS196
CIFAR-20
CLEVR Counts
Country211
CUB-200-2011
EuroSAT
FER2013
FGVC Aircraft
Flowers-102
Food-101
GTSRB
Hateful Memes
imagenet-1k
TAC
Kinetics-700
KITTI
Oxford-IIIT Pets
PCam
Rendered SST2
TURTLE (CLIP + DINOv2)
RESISC45
SUN397