ConvNeXt-Base backbone (Liu et al., 2022).
Builds the canonical ConvNeXt-B configuration:
depths=(3, 3, 27, 3), dims=(128, 256, 512, 1024). Final
feature width is 1024. Approximately 89M parameters — Swin-B
equivalent.
Model Size
Parameters
pretrainedbool= FalseIf
True, loads ImageNet-1k or ImageNet-22k pretrained
weights when available. Defaults to False.**overridesobject= {}Keyword overrides on top of the canonical ConvNeXt-B config.
Returns
ConvNeXtA ConvNeXt backbone returning a flat
feature.
Notes
ConvNeXt-B reaches 83.8% top-1 on ImageNet-1k (224x224) and 85.8% top-1 at 384x384 after ImageNet-22k pretraining (Liu et al., 2022, Tables 1 and 11).
Examples
>>> import lucid
>>> from lucid.models.vision.convnext import convnext_base
>>> model = convnext_base()
>>> x = lucid.randn(1, 3, 224, 224)
>>> model.forward_features(x).shape
(1, 1024)