EfficientNetV2 are a family of image classification models, which achieve better parameter efficiency and faster training speed than prior arts.

Constructs an EfficientNetV2-S architecture from EfficientNetV2: Smaller Models and Faster Training. Parameters: weights ( EfficientNet_V2_S_Weights , optional) ...

Instantiates the EfficientNetV2B0 architecture. ... This function returns a Keras image classification model, optionally loaded with weights pre-trained on ...

... weights. All the model builders internally rely on the torchvision.models.efficientnet.EfficientNet base class. Please refer to the source code for more ...

To evaluate pretrained models against Imagenet validation set, run imagenet_eval.ipynb. EfficientNet V2. The example below creates an EfficientNetV2-S model ...

As part of this blog, we are going to be looking into the EfficientNetV2 architecture in detail with code implementation in PyTorch.

Published weights are capable of scoring 83.9%top 1 accuracy and 96.7% top 5 accuracy on imagenet. efficientnetv2_b0_imagenet, 5.92M, EfficientNet B-style ...

This is a package with EfficientNetV2 model variants adapted to Keras functional API. I rewrote them this way so that the usage is similar to keras.applications ...

EfficientNet V2 Weights Variants. There are 3 weight variants: imagenet - pretrained on Imagenet1k; imagenet-21k - pretrained on Imagenet21k; imagenet-21k-ft1k ...

Our study shows in EfficientNets: (1) training with very large image sizes is slow; (2) depthwise convolu- tions are slow in early layers. (3) equally scaling ...