الرئيسية الأخبار 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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