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SOTA
Classification d'images
Image Classification On Resisc45
Image Classification On Resisc45
Métriques
Top 1 Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
Columns
Nom du modèle
Top 1 Accuracy
Paper Title
Repository
LWGANet L1
95.70
LWGANet: A Lightweight Group Attention Backbone for Remote Sensing Visual Tasks
ResNet50 (ImageNet-supervised)
88.56
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
BYOL (ResNet200-w2)
92.53
-
-
DecoupleNet D2
95.87
DecoupleNet: A Lightweight Backbone Network With Efficient Feature Decoupling for Remote Sensing Visual Tasks
LSENet
93.49
Local semantic enhanced convnet for aerial scene recognition
MIDC-Net
87.99
A multiple-instance densely-connected ConvNet for aerial scene classification
LWGANet L0
95.49
LWGANet: A Lightweight Group Attention Backbone for Remote Sensing Visual Tasks
DeiT-B/16
92.48
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
SimCLR-v2 (ResNet152-w3 + SK)
89.77
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
SAG-ViT
-
SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers
-
MoCo-v3 (ViT-B/16)
93.35
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
SwAV (ResNet50-w5)
94.73
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
ResNet50
96.83
In-domain representation learning for remote sensing
-
MoCo-v2 (ResNet50)
85.4
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
SEER (RegNet10B)
95.61
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
CLIP (ViT-B/16)
92.7
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
DINO (DeiT-B/16)
93.97
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
AGOS
94.91
All Grains, One Scheme (AGOS): Learning Multi-grain Instance Representation for Aerial Scene Classification
LWGANet L2
96.17
LWGANet: A Lightweight Group Attention Backbone for Remote Sensing Visual Tasks
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