الرئيسية الأخبار أحدث الأوراق البحثية الدروس مجموعات البيانات الموسوعة SOTA نماذج LLM لوحة الأداء GPU الفعاليات

العربية

Image Clustering On Imagenet

المقاييس

ARI

Accuracy

NMI

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

اسم النموذج	ARI	Accuracy	NMI	Paper Title	Repository
MIM-Refiner (D2V2-ViT-H/14)	42.2	67.3	87.2	MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations	-
TEMI DINO (ViT-B)	45.9	58.0	81.4	Exploring the Limits of Deep Image Clustering using Pretrained Models	-
TURTLE (CLIP + DINOv2)	62.5	72.9	88.2	Let Go of Your Labels with Unsupervised Transfer	-
MIM-Refiner (MAE-ViT-H/14)	45.5	64.6	85.3	MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations	-
TEMI MSN (ViT-L)	48.4	61.6	82.5	Exploring the Limits of Deep Image Clustering using Pretrained Models	-
MAE-CT (ViT-H/16 best)	-	58.0	81.8	Contrastive Tuning: A Little Help to Make Masked Autoencoders Forget	-
SCAN	-	39.9	72.0	SCAN: Learning to Classify Images without Labels	-
SeLa	-	-	66.4	Self-labelling via simultaneous clustering and representation learning	-
SeCu	41.9	53.5	79.4	Stable Cluster Discrimination for Deep Clustering	-
CoKe	35.6	47.6	76.2	Stable Cluster Discrimination for Deep Clustering	-
PRO-DSC	-	65.0	83.4	Exploring a Principled Framework For Deep Subspace Clustering
MAE-CT (ViT-H/16 mean)	-	57.1	81.7	Contrastive Tuning: A Little Help to Make Masked Autoencoders Forget	-

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Image Clustering On Imagenet | SOTA | HyperAI