HyperAI초신경

Multi Label Classification On Pascal Voc 2007

평가 지표

mAP

평가 결과

이 벤치마크에서 각 모델의 성능 결과

		Paper Title
Q2L-CvT(ImageNet-21K pretrained, resolution 384)	97.3	Query2Label: A Simple Transformer Way to Multi-Label Classification
Q2L-TResL(ImageNet-21K pretrained, resolution 448)	96.9	Query2Label: A Simple Transformer Way to Multi-Label Classification
GKGNet	96.8	GKGNet: Group K-Nearest Neighbor based Graph Convolutional Network for Multi-Label Image Recognition
MLD-TResNetL-AAM (resolution 448, pretrain from OpenImages V6)	96.70	Combining Metric Learning and Attention Heads For Accurate and Efficient Multilabel Image Classification
M3TR(448×448)	96.5	M3TR: Multi-modal Multi-label Recognition with Transformer
Q2L-TResL(resolution 448)	96.1	Query2Label: A Simple Transformer Way to Multi-Label Classification
MSRN(pretrain from MS-COCO)	96.0	Multi-layered Semantic Representation Network for Multi-label Image Classification
TResNet-L (resolution 448, pretrain from MS-COCO)	95.8	Asymmetric Loss For Multi-Label Classification
TDRG-R101(448×448)	95.0	Transformer-based Dual Relation Graph for Multi-label Image Recognition
SSGRL (pretrain from MS-COCO)	95.0	Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition
MCAR (ResNet101, 448x448)	94.8	Learning to Discover Multi-Class Attentional Regions for Multi-Label Image Recognition
TResNet-L (resolution 448, pretrain from ImageNet)	94.6	Asymmetric Loss For Multi-Label Classification
ML-GCN (pretrain from ImageNet)	94.0	Multi-Label Image Recognition with Graph Convolutional Networks
SSGRL (pretrain from ImageNet)	93.4	Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition
Ours PF-DLDL	93.4	Deep Label Distribution Learning with Label Ambiguity
ViT-B-16 (ImageNet-21K pretrained)	93.1	ImageNet-21K Pretraining for the Masses
FeV+LV (pretrain from ImageNet)	92.0	Exploit Bounding Box Annotations for Multi-label Object Recognition

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