vgg_16

→VGG

vgg_16(pretrained: bool = False, overrides: object = {})

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VGG-16 feature-extracting backbone (config D, no BatchNorm).

Builds a VGG with the paper-cited VGG-16 topology: per-block convolution counts (2, 2, 3, 3, 3) — adding a third $3\times3$ convolution to each of the deeper blocks 3/4/5. Approximately 138.4 M parameters in the full classifier variant (≈14.7 M in the convolutional trunk alone). Reaches a top-5 ImageNet validation error of 7.3% (Simonyan & Zisserman, ICLR 2015, Table 3, configuration D) — VGG-16 was the most widely deployed variant of the family.

Model Size

Parameters

pretrainedbool= False

Reserved for future pretrained-weight loading. Currently ignored.

**overridesobject= {}

Keyword overrides forwarded into VGGConfig.

Returns

VGG

Backbone with the VGG-16 configuration applied (or with overrides merged on top of it).

Notes

See Simonyan & Zisserman, ICLR 2015, Table 1 (configuration D).

Examples

>>> import lucid
>>> from lucid.models.vision.vgg import vgg_16
>>> model = vgg_16()
>>> x = lucid.randn(1, 3, 224, 224)
>>> out = model(x)
>>> out.last_hidden_state.shape   # (B, 512, 7, 7)
(1, 512, 7, 7)

Used by 1

lucid.models.vision.vgg

>>> import lucid >>> from lucid.models.vision.vgg import vgg_16 >>> model = vgg_16() >>> x = lucid.randn(1, 3, 224, 224) >>> out = model(x) >>> out.last_hidden_state.shape # (B, 512, 7, 7) (1, 512, 7, 7)