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Long Tail Learning On Voc Mlt

评估指标

Average mAP

评测结果

各个模型在此基准测试上的表现结果

模型名称
Average mAP
Paper TitleRepository
OLTR(ResNet-50)71.02Large-Scale Long-Tailed Recognition in an Open World-
DB Focal(ResNet-50)78.94Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets-
Focal Loss(ResNet-50)73.88Focal Loss for Dense Object Detection-
PG Loss(ResNet-50)80.37Probability Guided Loss for Long-Tailed Multi-Label Image Classification-
RS(ResNet-50)75.38Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks-
LTML(ResNet-50)81.44Long-Tailed Multi-Label Visual Recognition by Collaborative Training on Uniform and Re-Balanced Samplings-
CLIP(ViT-B/16)85.77Learning Transferable Visual Models From Natural Language Supervision-
LMPT(ResNet-50)85.44LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-tailed Multi-Label Visual Recognition-
LMPT(ViT-B/16)87.88LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-tailed Multi-Label Visual Recognition-
CB Focal(ResNet-50)75.24Class-Balanced Loss Based on Effective Number of Samples-
LDAM(ResNet-50)70.73Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss-
ML-GCN(ResNet-50)68.92Multi-Label Image Recognition with Graph Convolutional Networks-
CLIP(ResNet-50)84.30Learning Transferable Visual Models From Natural Language Supervision-
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