… EfficientNet-B0 behaves slightly differently compared to the other two models tested here.
This may be because EfficientNets … here, it is observed that EfficientNet-B0 yields the best loss …

… and leverages the EfficientNet architecture to … PyTorch is executed with various other
EfficientNet models, such as B0, B1, B2, B3, but it gives the best possible results with the EfficientNet…

… use the same PyTorch Image Models codebase (Wightman… EfficientNetV2 models still
outperform EfficientNets by a large … and 3.1x faster in inference than EfficientNet-B7. Since we are …

… In this paper, a PyTorch-based EfficientNet model has been discussed which has been
used to classify about 23 types of diseases. The previous work conducted by various scholars …

… on EfficientNet and a triplet attention mechanism is proposed to achieve accurate end-to-end
classification. The model was built on EfficientNet, … the shortcoming of EfficientNet in feature …

In this paper, we study the EfficientNet family pre-trained on … input resolution in EfficientNet
architectures significantly boost … We also show that, surprisingly, EfficientNets in their "vanilla …

… We adopted EfficientNet and MixNet, two families of deep neural networks to build an expert
… The experimental results showed that the application of EfficientNet and MixNet on the …

… We utilized PyTorch for training the model. Our hybrid model based on transfer learning
utilizing VGG11 Efficientnet reached a categorization accuracy of adenocarcinoma and non-…

… pipelines developed on the EfficientNet framework, aimed at enhancing the detection of
COVID-19 from CT scans. These include the application of spatial attention (A. EfficientNet), a …

… In this report, we introduce a lung image segmentation model utilizing the U-Net architecture
with the efficientnet-b0 serving as the encoder and a subsequent decoder architecture. The …