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Multi Label Classification On Pascal Voc 2007

評価指標

mAP

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

Paper Title
Q2L-CvT(ImageNet-21K pretrained, resolution 384)97.3Query2Label: A Simple Transformer Way to Multi-Label Classification
Q2L-TResL(ImageNet-21K pretrained, resolution 448)96.9Query2Label: A Simple Transformer Way to Multi-Label Classification
GKGNet96.8GKGNet: Group K-Nearest Neighbor based Graph Convolutional Network for Multi-Label Image Recognition
MLD-TResNetL-AAM (resolution 448, pretrain from OpenImages V6)96.70Combining Metric Learning and Attention Heads For Accurate and Efficient Multilabel Image Classification
M3TR(448×448)96.5M3TR: Multi-modal Multi-label Recognition with Transformer
Q2L-TResL(resolution 448)96.1Query2Label: A Simple Transformer Way to Multi-Label Classification
MSRN(pretrain from MS-COCO)96.0Multi-layered Semantic Representation Network for Multi-label Image Classification
TResNet-L (resolution 448, pretrain from MS-COCO)95.8Asymmetric Loss For Multi-Label Classification
TDRG-R101(448×448)95.0Transformer-based Dual Relation Graph for Multi-label Image Recognition
SSGRL (pretrain from MS-COCO)95.0Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition
MCAR (ResNet101, 448x448)94.8Learning to Discover Multi-Class Attentional Regions for Multi-Label Image Recognition
TResNet-L (resolution 448, pretrain from ImageNet)94.6Asymmetric Loss For Multi-Label Classification
ML-GCN (pretrain from ImageNet)94.0Multi-Label Image Recognition with Graph Convolutional Networks
SSGRL (pretrain from ImageNet)93.4Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition
Ours PF-DLDL93.4Deep Label Distribution Learning with Label Ambiguity
ViT-B-16 (ImageNet-21K pretrained)93.1ImageNet-21K Pretraining for the Masses
FeV+LV (pretrain from ImageNet)92.0Exploit Bounding Box Annotations for Multi-label Object Recognition
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Multi Label Classification On Pascal Voc 2007 | SOTA | HyperAI超神経