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Models/EfficientNet-B0

EfficientNet-B0

Reported on 10 benchmarks across 4 tasks · 3 papers

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

Computer Vision6 results

Image ClassificationonMAMe
Acc· 2020-07-27
88.25
best: 88.95 (EfficientNet-B3)
The MAMe Dataset: On the relevance of High Resolution and Variable Shape image properties arXiv:2007.13693
Image ClassificationonImageNet
GFLOPs· 2019-05-28
0.39
best: 1478 (InternImage-H)
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks arXiv:1905.11946
Domain GeneralizationonVizWiz-Classification
Accuracy - All Images· 2019-05-28
34.2
best: 57.2 (VOLO-D5)
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks arXiv:1905.11946
Domain GeneralizationonVizWiz-Classification
Accuracy - Clean Images· 2019-05-28
38.4
best: 450 (ViT-8/B-224)
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks arXiv:1905.11946
Domain GeneralizationonVizWiz-Classification
Accuracy - Corrupted Images· 2019-05-28
27.4
best: 51.8 (VOLO-D5)
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks arXiv:1905.11946
Image ClassificationonTCMP-300
Accuracy (% )
86.13
best: 89.64 (Swin-Base)

Methodology3 results

Domain AdaptationonVizWiz-Classification
Accuracy - All Images· 2019-05-28
34.2
best: 57.2 (VOLO-D5)
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks arXiv:1905.11946
Domain AdaptationonVizWiz-Classification
Accuracy - Clean Images· 2019-05-28
38.4
best: 450 (ViT-8/B-224)
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks arXiv:1905.11946
Domain AdaptationonVizWiz-Classification
Accuracy - Corrupted Images· 2019-05-28
27.4
best: 51.8 (VOLO-D5)
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks arXiv:1905.11946

Natural Language Processing1 result

Binary ClassificationonCBIS-DDSM
AUC· 2021-10-01
0.8418
best: 0.8483 (EfficientNet-B0 w/ TTA)
Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional Network arXiv:2110.01606