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플랫폼
홈
SOTA
객체 검출
Object Detection On Coco Minival
Object Detection On Coco Minival
평가 지표
AP50
AP75
APL
APM
APS
box AP
평가 결과
이 벤치마크에서 각 모델의 성능 결과
Columns
모델 이름
AP50
AP75
APL
APM
APS
box AP
Paper Title
Co-DETR
-
-
-
-
-
65.9
DETRs with Collaborative Hybrid Assignments Training
M3I Pre-training (InternImage-H)
-
-
-
-
-
65.0
Towards All-in-one Pre-training via Maximizing Multi-modal Mutual Information
InternImage-H
-
-
-
-
-
65.0
InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions
Co-DETR (Swin-L)
-
-
-
-
-
64.7
DETRs with Collaborative Hybrid Assignments Training
Focal-Stable-DINO (Focal-Huge, no TTA)
81.5
71.4
78.5
68.5
50.4
64.6
A Strong and Reproducible Object Detector with Only Public Datasets
EVA
82.1
70.8
78.5
68.4
49.4
64.5
EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
ViT-CoMer
-
-
-
-
-
64.3
ViT-CoMer: Vision Transformer with Convolutional Multi-scale Feature Interaction for Dense Predictions
FocalNet-H (DINO)
-
-
-
-
-
64.2
Focal Modulation Networks
InternImage-XL
-
-
-
-
-
64.2
InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions
CP-DETR-L Swin-L(Fine tuning separately in COCO)
-
-
-
-
-
64.1
CP-DETR: Concept Prompt Guide DETR Toward Stronger Universal Object Detection
RevCol-H(DINO)
-
-
-
-
-
63.8
Reversible Column Networks
DINO (Swin-L)
-
-
-
-
-
63.2
DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
Grounding DINO
-
-
-
-
-
63.0
Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
SwinV2-G (HTC++)
-
-
-
-
-
62.5
Swin Transformer V2: Scaling Up Capacity and Resolution
GLEE-Pro
-
-
-
-
-
62.0
General Object Foundation Model for Images and Videos at Scale
Florence-CoSwin-H
-
-
-
-
-
62
Florence: A New Foundation Model for Computer Vision
ViTDet, ViT-H Cascade (multiscale)
-
-
-
-
-
61.3
Exploring Plain Vision Transformer Backbones for Object Detection
GLIP (Swin-L, multi-scale)
-
-
-
-
-
60.8
Grounded Language-Image Pre-training
Soft Teacher + Swin-L (HTC++, multi-scale)
-
-
-
-
-
60.7
End-to-End Semi-Supervised Object Detection with Soft Teacher
UNINEXT-H
77.5
66.7
75.3
64.8
45.1
60.6
Universal Instance Perception as Object Discovery and Retrieval
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