Startseite Neuigkeiten Neueste Forschungsarbeiten Tutorials Datensätze Wiki SOTA LLM-Modelle GPU-Rangliste Veranstaltungen

Deutsch

Long Tail Learning On Coco Mlt

Metriken

Average mAP

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	Average mAP	Paper Title	Repository
DB Focal(ResNet-50)	53.55	Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets	-
PG Loss(ResNet-50)	54.43	Probability Guided Loss for Long-Tailed Multi-Label Image Classification	-
LDAM(ResNet-50)	40.53	Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss	-
RS(ResNet-50)	46.97	Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks	-
ML-GCN(ResNet-50)	44.24	Multi-Label Image Recognition with Graph Convolutional Networks	-
Focal Loss(ResNet-50)	49.46	Focal Loss for Dense Object Detection	-
LMPT(ResNet-50)	58.97	LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-tailed Multi-Label Visual Recognition	-
OLTR(ResNet-50)	45.83	Large-Scale Long-Tailed Recognition in an Open World	-
LTML(ResNet-50)	56.90	Long-Tailed Multi-Label Visual Recognition by Collaborative Training on Uniform and Re-Balanced Samplings	-
CB Loss(ResNet-50)	49.06	Class-Balanced Loss Based on Effective Number of Samples	-
CLIP(ViT-B/16)	60.17	Learning Transferable Visual Models From Natural Language Supervision	-
CLIP(ResNet-50)	56.19	Learning Transferable Visual Models From Natural Language Supervision	-
LMPT(ViT-B/16)	66.19	LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-tailed Multi-Label Visual Recognition	-

0 of 13 row(s) selected.

Long Tail Learning On Coco Mlt | SOTA | HyperAI