Image Classification On Eurosat

Metrics

Accuracy (%)

Results

Performance results of various models on this benchmark

		Paper Title
IMP+MTP(IntenImage-XL)	99.24	MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining
µ2Net+ (ViT-L/16)	99.22	A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems
µ2Net (ViT-L/16)	99.2	An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems
ResNet50	99.2	In-domain representation learning for remote sensing
WaveMix	98.96	Which Backbone to Use: A Resource-efficient Domain Specific Comparison for Computer Vision
MoCo-v2 (ResNet18, fine tune)	98.9	Self-supervised Learning in Remote Sensing: A Review
DINO-MC (Wide ResNet)	98.78	Extending global-local view alignment for self-supervised learning with remote sensing imagery
MAE+MTP(ViT-L+RVSA)	98.78	MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining
MAE+MTP(ViT-B+RVSA)	98.76	MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining
MSMatch Multispectral	98.65	MSMatch: Semi-Supervised Multispectral Scene Classification with Few Labels
MSMatch RGB	98.14	MSMatch: Semi-Supervised Multispectral Scene Classification with Few Labels
SEER (RegNet10B - linear eval)	97.5	Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
DINO-MC (WRN linear eval))	95.7	Extending global-local view alignment for self-supervised learning with remote sensing imagery
MoCo-v2 (ResNet18, linear eval)	94.4	Self-supervised Learning in Remote Sensing: A Review

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Image Classification On Eurosat

Metrics

Accuracy (%)

Results

Performance results of various models on this benchmark

		Paper Title
IMP+MTP(IntenImage-XL)	99.24	MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining
µ2Net+ (ViT-L/16)	99.22	A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems
µ2Net (ViT-L/16)	99.2	An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems
ResNet50	99.2	In-domain representation learning for remote sensing
WaveMix	98.96	Which Backbone to Use: A Resource-efficient Domain Specific Comparison for Computer Vision
MoCo-v2 (ResNet18, fine tune)	98.9	Self-supervised Learning in Remote Sensing: A Review
DINO-MC (Wide ResNet)	98.78	Extending global-local view alignment for self-supervised learning with remote sensing imagery
MAE+MTP(ViT-L+RVSA)	98.78	MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining
MAE+MTP(ViT-B+RVSA)	98.76	MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining
MSMatch Multispectral	98.65	MSMatch: Semi-Supervised Multispectral Scene Classification with Few Labels
MSMatch RGB	98.14	MSMatch: Semi-Supervised Multispectral Scene Classification with Few Labels
SEER (RegNet10B - linear eval)	97.5	Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
DINO-MC (WRN linear eval))	95.7	Extending global-local view alignment for self-supervised learning with remote sensing imagery
MoCo-v2 (ResNet18, linear eval)	94.4	Self-supervised Learning in Remote Sensing: A Review

0 of 14 row(s) selected.