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Long Tail Learning On Coco Mlt

Metrics

Average mAP

Results

Performance results of various models on this benchmark

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