CoAtNet-3 backbone (Dai et al., 2021).
Larger variant: blocks_per_stage=(2, 6, 14, 2),
dims=(192, 384, 768, 1536), stem_width=192,
attn_heads=(24, 48). Approximately 168M parameters, 84.5%
ImageNet-1k top-1 at 224×224 (Table 5). With ImageNet-21k or
JFT-3B pretraining the paper reports 86.5% / 87.8% at 384x384.
Model Size
Parameters
pretrainedbool= FalseNo checkpoint is published for
coatnet_3, so True raises
NotImplementedError rather than return randomly initialised weights.**overridesobject= {}Keyword overrides applied on top of the canonical CoAtNet-3
config; each must name a field of
CoAtNetConfig (e.g.
image_size=384, in_channels=1).Examples
>>> import lucid
>>> from lucid.models.vision.coatnet import coatnet_3
>>> model = coatnet_3()
>>> out = model(lucid.randn(1, 3, 224, 224))
>>> out.last_hidden_state.shape
(1, 1536, 7, 7)