EfficientNet is designed to achieve top accuracy while utilizing fewer parameters, in addition
to less computational resources compared to previous models. In this paper, we are …

Non-small cell lung cancer (NSCLC) accounts for a high proportion of 85% among all lung
cancer and has a significantly higher mortality rate (22.7%) compared to other cancers. …

In terms of context, the aim of this dissertation is to aid neuroradiologists in their clinical
judgment regarding the early detection of AD by using DL. To that aim, the system design …

… EficientNet-B0 is the base model of the EfficientNet family and … pre-trained models available
in the PyTorch library. This transfer … of the pruning functions of the PyTorch library, the same …

… The aggregate of the proposed loss-function and the two-stage EficientNet training
achieved \(2.10\%\) improvement, which is measured with the area under the receiver-operating-…

… Then use the EficientNet model to classify and identify the histopathological images of …
The tensor image data was generated by Pytorch deep learning library with real-time data …

… The face manipulation identification framework was programmed and trained using PyTorch…
of the suggested model over existing models, which includes EficientNet [13], ResNet-50 [23]…

… The EfficientNet model versions are trained generally without injecting privacy constraints
using the PyTorch framework [49]. Since the validation accuracy for all the models remains the …

… To harness the full potential of deep learning, we relied on the PyTorch framework for its …
The graphs depict the validation accuracies of EficientNet, Googl_eNet and ResNet60 for …

… deep network, an object detection algorithm of UAV vehicle based on Eficientnet is proposed.
Firstly, Eficientnet, the lightweight network, is introduced as the feature extraction network …