HyperAI
HyperAI초신경
홈
플랫폼
문서
뉴스
연구 논문
튜토리얼
데이터셋
백과사전
SOTA
LLM 모델
GPU 랭킹
컨퍼런스
전체 검색
소개
서비스 약관
개인정보 처리방침
한국어
HyperAI
HyperAI초신경
Toggle Sidebar
전체 사이트 검색...
⌘
K
Command Palette
Search for a command to run...
플랫폼
홈
SOTA
다중 레이블 분류
Multi Label Classification On Ms Coco
Multi Label Classification On Ms Coco
평가 지표
mAP
평가 결과
이 벤치마크에서 각 모델의 성능 결과
Columns
모델 이름
mAP
Paper Title
ADDS(ViT-L-336, resolution 1344)
93.54
Open Vocabulary Multi-Label Classification with Dual-Modal Decoder on Aligned Visual-Textual Features
ADDS(ViT-L-336, resolution 640)
93.41
Open Vocabulary Multi-Label Classification with Dual-Modal Decoder on Aligned Visual-Textual Features
ADDS(ViT-L-336, resolution 336)
91.76
Open Vocabulary Multi-Label Classification with Dual-Modal Decoder on Aligned Visual-Textual Features
ML-Decoder(TResNet-XL, resolution 640)
91.4
ML-Decoder: Scalable and Versatile Classification Head
Q2L-CvT(ImageNet-21K pretraining, resolution 384)
91.3
Query2Label: A Simple Transformer Way to Multi-Label Classification
MLD-TResNet-L-AAM[640x640]
91.30
Combining Metric Learning and Attention Heads For Accurate and Efficient Multilabel Image Classification
ML-Decoder(TResNet-L, resolution 640)
91.1
ML-Decoder: Scalable and Versatile Classification Head
Q2L-SwinL(ImageNet-21K pretraining, resolution 384)
90.5
Query2Label: A Simple Transformer Way to Multi-Label Classification
IDA-SwinL
90.3
Causality Compensated Attention for Contextual Biased Visual Recognition
CCD-SwinL
90.3
Contextual Debiasing for Visual Recognition With Causal Mechanisms
Q2L-TResL(ImageNet-21K pretraining, resolution 640)
90.3
Query2Label: A Simple Transformer Way to Multi-Label Classification
MlTr-XL(ImageNet-21K pretraining, resolution 384)
90.0
MlTr: Multi-label Classification with Transformer
TResNet-L-V2, (ImageNet-21K-P pretraining, resolution 640)
89.8
ImageNet-21K Pretraining for the Masses
MlTr-L(ImageNet-21K pretraining, resolution 384)
88.5
MlTr: Multi-label Classification with Transformer
TResNet-XL (resolution 640)
88.4
Asymmetric Loss For Multi-Label Classification
TResNet-L-V2, (ImageNet-21K-P pretraining, resolution 448)
88.4
ImageNet-21K Pretraining for the Masses
GKGNet(resolution 576)
87.7
GKGNet: Group K-Nearest Neighbor based Graph Convolutional Network for Multi-Label Image Recognition
M3TR(ImageNet-21K-P pretraining, resolution 448)
87.5
M3TR: Multi-modal Multi-label Recognition with Transformer
GKGNet(resolution 448)
86.7
GKGNet: Group K-Nearest Neighbor based Graph Convolutional Network for Multi-Label Image Recognition
TResNet-L (resolution 448)
86.6
Asymmetric Loss For Multi-Label Classification
0 of 34 row(s) selected.
Previous
Next
Multi Label Classification On Ms Coco | SOTA | HyperAI초신경