Multi-Label Classification on PASCAL VOC 2007

Metric: mAP (higher is better)

LeaderboardDataset

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Results

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#	Model↕	mAP▼	Augmentations	Paper	Date↕	Code
1	Q2L-CvT(ImageNet-21K pretrained, resolution 384)	97.3	No	Query2Label: A Simple Transformer Way to Multi-L...	2021-07-22	Code
2	Q2L-TResL(ImageNet-21K pretrained, resolution 448)	96.9	No	Query2Label: A Simple Transformer Way to Multi-L...	2021-07-22	Code
3	GKGNet	96.8	No	GKGNet: Group K-Nearest Neighbor based Graph Con...	2023-08-28	Code
4	MLD-TResNetL-AAM (resolution 448, pretrain from OpenImages V6)	96.7	No	Combining Metric Learning and Attention Heads Fo...	2022-09-14	Code
5	M3TR(448×448)	96.5	No	-	-	Code
6	Q2L-TResL(resolution 448)	96.1	No	Query2Label: A Simple Transformer Way to Multi-L...	2021-07-22	Code
7	MSRN(pretrain from MS-COCO)	96	No	Multi-layered Semantic Representation Network fo...	2021-06-22	Code
8	TResNet-L (resolution 448, pretrain from MS-COCO)	95.8	No	Asymmetric Loss For Multi-Label Classification	2020-09-29	Code
9	SSGRL (pretrain from MS-COCO)	95	No	Learning Semantic-Specific Graph Representation ...	2019-08-20	Code
10	TDRG-R101(448×448)	95	No	Transformer-based Dual Relation Graph for Multi-...	2021-10-10	Code
11	MCAR (ResNet101, 448x448)	94.8	No	Learning to Discover Multi-Class Attentional Reg...	2020-07-03	Code
12	TResNet-L (resolution 448, pretrain from ImageNet)	94.6	No	Asymmetric Loss For Multi-Label Classification	2020-09-29	Code
13	ML-GCN (pretrain from ImageNet)	94	No	Multi-Label Image Recognition with Graph Convolu...	2019-04-07	Code
14	SSGRL (pretrain from ImageNet)	93.4	No	Learning Semantic-Specific Graph Representation ...	2019-08-20	Code
15	Ours PF-DLDL	93.4	No	Deep Label Distribution Learning with Label Ambi...	2016-11-06	Code
16	ViT-B-16 (ImageNet-21K pretrained)	93.1	No	ImageNet-21K Pretraining for the Masses	2021-04-22	Code
17	FeV+LV (pretrain from ImageNet)	92	No	Exploit Bounding Box Annotations for Multi-label...	2015-04-22	-

#1Q2L-CvT(ImageNet-21K pretrained, resolution 384)SOTA
97.3
mAP· 2021-07-22
Query2Label: A Simple Transformer Way to Multi-Label Classification Code
#2Q2L-TResL(ImageNet-21K pretrained, resolution 448)
96.9
mAP· 2021-07-22
Query2Label: A Simple Transformer Way to Multi-Label Classification Code
#3GKGNet
96.8
mAP· 2023-08-28
GKGNet: Group K-Nearest Neighbor based Graph Convolutional Network for Multi-Label Image Recognition Code
#4MLD-TResNetL-AAM (resolution 448, pretrain from OpenImages V6)
96.7
mAP· 2022-09-14
Combining Metric Learning and Attention Heads For Accurate and Efficient Multilabel Image Classification Code
#5M3TR(448×448)
96.5
mAP
No paperCode
#6Q2L-TResL(resolution 448)
96.1
mAP· 2021-07-22
Query2Label: A Simple Transformer Way to Multi-Label Classification Code
#7MSRN(pretrain from MS-COCO)SOTA
96
mAP· 2021-06-22
Multi-layered Semantic Representation Network for Multi-label Image Classification Code
#8TResNet-L (resolution 448, pretrain from MS-COCO)SOTA
95.8
mAP· 2020-09-29
Asymmetric Loss For Multi-Label Classification Code
#9SSGRL (pretrain from MS-COCO)SOTA
95
mAP· 2019-08-20
Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition Code
#10TDRG-R101(448×448)
95
mAP· 2021-10-10
Transformer-based Dual Relation Graph for Multi-label Image Recognition Code
#11MCAR (ResNet101, 448x448)
94.8
mAP· 2020-07-03
Learning to Discover Multi-Class Attentional Regions for Multi-Label Image Recognition Code
#12TResNet-L (resolution 448, pretrain from ImageNet)
94.6
mAP· 2020-09-29
Asymmetric Loss For Multi-Label Classification Code
#13ML-GCN (pretrain from ImageNet)SOTA
94
mAP· 2019-04-07
Multi-Label Image Recognition with Graph Convolutional Networks Code
#14SSGRL (pretrain from ImageNet)
93.4
mAP· 2019-08-20
Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition Code
#15Ours PF-DLDLSOTA
93.4
mAP· 2016-11-06
Deep Label Distribution Learning with Label Ambiguity Code
#16ViT-B-16 (ImageNet-21K pretrained)
93.1
mAP· 2021-04-22
ImageNet-21K Pretraining for the Masses Code
#17FeV+LV (pretrain from ImageNet)SOTA
92
mAP· 2015-04-22
Exploit Bounding Box Annotations for Multi-label Object Recognition