Image Classification on EuroSAT

Metric: Accuracy (%) (higher is better)

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#	Model↕	Accuracy (%)▼	Extra Data	Paper	Date↕	Code
1	DeepEnsembling	99.41	No	-	-	Code
2	IMP+MTP(IntenImage-XL)	99.24	No	MTP: Advancing Remote Sensing Foundation Model v...	2024-03-20	Code
3	µ2Net+ (ViT-L/16)	99.22	No	A Continual Development Methodology for Large-sc...	2022-09-15	Code
4	µ2Net (ViT-L/16)	99.2	No	An Evolutionary Approach to Dynamic Introduction...	2022-05-25	Code
5	ResNet50	99.2	Yes	In-domain representation learning for remote sen...	2019-11-15	Code
6	WaveMix	98.96	No	Which Backbone to Use: A Resource-efficient Doma...	2024-06-09	Code
7	MoCo-v2 (ResNet18, fine tune)	98.9	Yes	Self-supervised Learning in Remote Sensing: A Re...	2022-06-27	Code
8	DINO-MC (Wide ResNet)	98.78	Yes	Extending global-local view alignment for self-s...	2023-03-12	Code
9	MAE+MTP(ViT-L+RVSA)	98.78	No	MTP: Advancing Remote Sensing Foundation Model v...	2024-03-20	Code
10	MAE+MTP(ViT-B+RVSA)	98.76	No	MTP: Advancing Remote Sensing Foundation Model v...	2024-03-20	Code
11	MSMatch Multispectral	98.65	No	MSMatch: Semi-Supervised Multispectral Scene Cla...	2021-03-18	Code
12	MSMatch RGB	98.14	No	MSMatch: Semi-Supervised Multispectral Scene Cla...	2021-03-18	Code
13	SEER (RegNet10B - linear eval)	97.5	Yes	Vision Models Are More Robust And Fair When Pret...	2022-02-16	Code
14	DINO-MC (WRN linear eval))	95.7	Yes	Extending global-local view alignment for self-s...	2023-03-12	Code
15	MoCo-v2 (ResNet18, linear eval)	94.4	Yes	Self-supervised Learning in Remote Sensing: A Re...	2022-06-27	Code

#1DeepEnsembling
99.41
Accuracy (%)
No paperCode
#2IMP+MTP(IntenImage-XL)SOTA
99.24
Accuracy (%)· 2024-03-20
MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining Code
#3µ2Net+ (ViT-L/16)SOTA
99.22
Accuracy (%)· 2022-09-15
A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems Code
#4µ2Net (ViT-L/16)
99.2
Accuracy (%)· 2022-05-25
An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems Code
#5ResNet50SOTA
99.2
Accuracy (%)· Extra Data· 2019-11-15
In-domain representation learning for remote sensing Code
#6WaveMix
98.96
Accuracy (%)· 2024-06-09
Which Backbone to Use: A Resource-efficient Domain Specific Comparison for Computer Vision Code
#7MoCo-v2 (ResNet18, fine tune)
98.9
Accuracy (%)· Extra Data· 2022-06-27
Self-supervised Learning in Remote Sensing: A Review Code
#8DINO-MC (Wide ResNet)
98.78
Accuracy (%)· Extra Data· 2023-03-12
Extending global-local view alignment for self-supervised learning with remote sensing imagery Code
#9MAE+MTP(ViT-L+RVSA)
98.78
Accuracy (%)· 2024-03-20
MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining Code
#10MAE+MTP(ViT-B+RVSA)
98.76
Accuracy (%)· 2024-03-20
MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining Code
#11MSMatch Multispectral
98.65
Accuracy (%)· 2021-03-18
MSMatch: Semi-Supervised Multispectral Scene Classification with Few Labels Code
#12MSMatch RGB
98.14
Accuracy (%)· 2021-03-18
MSMatch: Semi-Supervised Multispectral Scene Classification with Few Labels Code
#13SEER (RegNet10B - linear eval)
97.5
Accuracy (%)· Extra Data· 2022-02-16
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision Code
#14DINO-MC (WRN linear eval))
95.7
Accuracy (%)· Extra Data· 2023-03-12
Extending global-local view alignment for self-supervised learning with remote sensing imagery Code
#15MoCo-v2 (ResNet18, linear eval)
94.4
Accuracy (%)· Extra Data· 2022-06-27
Self-supervised Learning in Remote Sensing: A Review Code