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efficientnet_b2

torchvision.models.efficientnet_b2(*, weights: Optional[torchvision.models.efficientnet.EfficientNet_B2_Weights] = None, progress: bool = True, **kwargs: Any)torchvision.models.efficientnet.EfficientNet[source]

EfficientNet B2 model architecture from the EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks paper.

Parameters
  • weights (EfficientNet_B2_Weights, optional) – The pretrained weights to use. See EfficientNet_B2_Weights below for more details, and possible values. By default, no pre-trained weights are used.

  • progress (bool, optional) – If True, displays a progress bar of the download to stderr. Default is True.

  • **kwargs – parameters passed to the torchvision.models.efficientnet.EfficientNet base class. Please refer to the source code for more details about this class.

class torchvision.models.EfficientNet_B2_Weights(value)[source]

The model builder above accepts the following values as the weights parameter. EfficientNet_B2_Weights.DEFAULT is equivalent to EfficientNet_B2_Weights.IMAGENET1K_V1. You can also use strings, e.g. weights='DEFAULT' or weights='IMAGENET1K_V1'.

EfficientNet_B2_Weights.IMAGENET1K_V1:

These weights are ported from the original paper. Also available as EfficientNet_B2_Weights.DEFAULT.

acc@1 (on ImageNet-1K)

80.608

acc@5 (on ImageNet-1K)

95.31

categories

tench, goldfish, great white shark, … (997 omitted)

min_size

height=1, width=1

recipe

link

num_params

9109994

The inference transforms are available at EfficientNet_B2_Weights.IMAGENET1K_V1.transforms and perform the following preprocessing operations: Accepts PIL.Image, batched (B, C, H, W) and single (C, H, W) image torch.Tensor objects. The images are resized to resize_size=[288] using interpolation=InterpolationMode.BICUBIC, followed by a central crop of crop_size=[288]. Finally the values are first rescaled to [0.0, 1.0] and then normalized using mean=[0.485, 0.456, 0.406] and std=[0.229, 0.224, 0.225].

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