convnext_base¶
- torchvision.models.convnext_base(*, weights: Optional[ConvNeXt_Base_Weights] = None, progress: bool = True, **kwargs: Any) ConvNeXt [source]¶
ConvNeXt Base model architecture from the A ConvNet for the 2020s paper.
- Parameters:
weights (
ConvNeXt_Base_Weights
, optional) – The pretrained weights to use. SeeConvNeXt_Base_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.convnext.ConvNext
base class. Please refer to the source code for more details about this class.
- class torchvision.models.ConvNeXt_Base_Weights(value)[source]¶
The model builder above accepts the following values as the
weights
parameter.ConvNeXt_Base_Weights.DEFAULT
is equivalent toConvNeXt_Base_Weights.IMAGENET1K_V1
. You can also use strings, e.g.weights='DEFAULT'
orweights='IMAGENET1K_V1'
.ConvNeXt_Base_Weights.IMAGENET1K_V1:
These weights improve upon the results of the original paper by using a modified version of TorchVision’s new training recipe. Also available as
ConvNeXt_Base_Weights.DEFAULT
.acc@1 (on ImageNet-1K)
84.062
acc@5 (on ImageNet-1K)
96.87
min_size
height=32, width=32
categories
tench, goldfish, great white shark, … (997 omitted)
recipe
num_params
88591464
GFLOPS
15.36
File size
338.1 MB
The inference transforms are available at
ConvNeXt_Base_Weights.IMAGENET1K_V1.transforms
and perform the following preprocessing operations: AcceptsPIL.Image
, batched(B, C, H, W)
and single(C, H, W)
imagetorch.Tensor
objects. The images are resized toresize_size=[232]
usinginterpolation=InterpolationMode.BILINEAR
, followed by a central crop ofcrop_size=[224]
. Finally the values are first rescaled to[0.0, 1.0]
and then normalized usingmean=[0.485, 0.456, 0.406]
andstd=[0.229, 0.224, 0.225]
.