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Image Clustering On Imagenet 50 1
Métriques
ACCURACY
ARI
NMI
Résultats
Résultats de performance de divers modèles sur ce benchmark
| Paper Title | ||||
|---|---|---|---|---|
| TEMI CLIP ViT-L (openai) | 0.8827 | 0.8272 | 0.9232 | Exploring the Limits of Deep Image Clustering using Pretrained Models |
| TEMI MSN ViT-L | 0.8487 | 0.7646 | 0.8814 | Exploring the Limits of Deep Image Clustering using Pretrained Models |
| Single-Noun Prior | 0.827 | 0.744 | 0.847 | Dataset Summarization by K Principal Concepts |
| TEMI DINO ViT-B | 0.801 | 0.7093 | 0.8610 | Exploring the Limits of Deep Image Clustering using Pretrained Models |
| SCAN | 0.751 | 0.635 | 0.805 | SCAN: Learning to Classify Images without Labels |
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