HyperAIHyperAI
HomeNewsLatest PapersTutorialsDatasetsWikiSOTALLM ModelsGPU LeaderboardEvents
Search
About
HyperAIHyperAI

Multi Label Image Classification On

Metrics

mAP (micro)
official split

Results

Performance results of various models on this benchmark

Model Name
mAP (micro)
official split
Paper TitleRepository
MoCo-v2 (ResNet18, fine tune)89.3NoSelf-supervised Learning in Remote Sensing: A Review-
ResNet50--In-domain representation learning for remote sensing-
DINO-MC88.75NoExtending global-local view alignment for self-supervised learning with remote sensing imagery-
ResNet50-YesBenchmarking and scaling of deep learning models for land cover image classification-
MoCo-v3 (ViT-S/16, fine tune)89.9NoSSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation-
MAE (ViT-S/16, fine tune)88.9NoSSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation-
ViTM/20-YesBenchmarking and scaling of deep learning models for land cover image classification-
WideResNet-B5-ECA-YesBenchmarking and scaling of deep learning models for land cover image classification-
MoCo-v2 (ResNet50, fine tune)91.8NoSSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation-
MLPMixer-YesBenchmarking and scaling of deep learning models for land cover image classification-
0 of 10 row(s) selected.
Multi Label Image Classification On | SOTA | HyperAI