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Models/EAT-M

EAT-M

Reported on 6 benchmarks across 2 tasks · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Audio3 results

Audio ClassificationonESC-50
Accuracy (5-fold)· uses extra data· 2022-04-25
96.3
best: 99.1 (OmniVec2)
End-to-End Audio Strikes Back: Boosting Augmentations Towards An Efficient Audio Classification Network arXiv:2204.11479
Audio ClassificationonESC-50
Top-1 Accuracy· uses extra data· 2022-04-25
96.3
best: 99.1 (OmniVec2)
End-to-End Audio Strikes Back: Boosting Augmentations Towards An Efficient Audio Classification Network arXiv:2204.11479
Audio ClassificationonAudioSet
Test mAP· 2022-04-25
0.426
best: 0.558 (OmniVec2)
End-to-End Audio Strikes Back: Boosting Augmentations Towards An Efficient Audio Classification Network arXiv:2204.11479

Methodology3 results

ClassificationonESC-50
Accuracy (5-fold)· uses extra data· 2022-04-25
96.3
best: 99.1 (OmniVec2)
End-to-End Audio Strikes Back: Boosting Augmentations Towards An Efficient Audio Classification Network arXiv:2204.11479
ClassificationonESC-50
Top-1 Accuracy· uses extra data· 2022-04-25
96.3
best: 99.1 (OmniVec2)
End-to-End Audio Strikes Back: Boosting Augmentations Towards An Efficient Audio Classification Network arXiv:2204.11479
ClassificationonAudioSet
Test mAP· 2022-04-25
0.426
best: 0.558 (OmniVec2)
End-to-End Audio Strikes Back: Boosting Augmentations Towards An Efficient Audio Classification Network arXiv:2204.11479