Remote Sensing Image Classification on FireRisk

Metric: Accuracy (%) (higher is better)

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#	Model↕	Accuracy (%)▼	Extra Data	Paper	Date↕	Code
1	MAE (ViT-B/16)	65.29	No	FireRisk: A Remote Sensing Dataset for Fire Risk...	2023-03-13	Code
2	DINO (ViT-B/16)	63.36	No	FireRisk: A Remote Sensing Dataset for Fire Risk...	2023-03-13	Code
3	ViT-B/16	63.31	No	FireRisk: A Remote Sensing Dataset for Fire Risk...	2023-03-13	Code
4	ResNet-50	63.2	No	FireRisk: A Remote Sensing Dataset for Fire Risk...	2023-03-13	Code

#1MAE (ViT-B/16)SOTA
65.29
Accuracy (%)· 2023-03-13
FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning Code
#2DINO (ViT-B/16)
63.36
Accuracy (%)· 2023-03-13
FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning Code
#3ViT-B/16
63.31
Accuracy (%)· 2023-03-13
FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning Code
#4ResNet-50
63.2
Accuracy (%)· 2023-03-13
FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning Code