Multi Label Image Classification On

Metrics

mAP (micro)

official split

Results

Performance results of various models on this benchmark

			Paper Title	Code
MoCo-v2 (ResNet18, fine tune)	89.3	No	Self-supervised Learning in Remote Sensing: A Review
ResNet50	-	-	In-domain representation learning for remote sensing
DINO-MC	88.75	No	Extending global-local view alignment for self-supervised learning with remote sensing imagery
ResNet50	-	Yes	Benchmarking and scaling of deep learning models for land cover image classification
MoCo-v3 (ViT-S/16, fine tune)	89.9	No	SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation
MAE (ViT-S/16, fine tune)	88.9	No	SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation
ViTM/20	-	Yes	Benchmarking and scaling of deep learning models for land cover image classification
WideResNet-B5-ECA	-	Yes	Benchmarking and scaling of deep learning models for land cover image classification
MoCo-v2 (ResNet50, fine tune)	91.8	No	SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation
MLPMixer	-	Yes	Benchmarking and scaling of deep learning models for land cover image classification

0 of 10 row(s) selected.

Multi Label Image Classification On

Metrics

mAP (micro)

official split

Results

Performance results of various models on this benchmark

			Paper Title	Code
MoCo-v2 (ResNet18, fine tune)	89.3	No	Self-supervised Learning in Remote Sensing: A Review
ResNet50	-	-	In-domain representation learning for remote sensing
DINO-MC	88.75	No	Extending global-local view alignment for self-supervised learning with remote sensing imagery
ResNet50	-	Yes	Benchmarking and scaling of deep learning models for land cover image classification
MoCo-v3 (ViT-S/16, fine tune)	89.9	No	SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation
MAE (ViT-S/16, fine tune)	88.9	No	SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation
ViTM/20	-	Yes	Benchmarking and scaling of deep learning models for land cover image classification
WideResNet-B5-ECA	-	Yes	Benchmarking and scaling of deep learning models for land cover image classification
MoCo-v2 (ResNet50, fine tune)	91.8	No	SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation
MLPMixer	-	Yes	Benchmarking and scaling of deep learning models for land cover image classification

0 of 10 row(s) selected.

Multi Label Image Classification On | SOTA | HyperAI超神经