ResNeSt-200 feature-extracting backbone (no classification head).
Builds a ResNeSt with the deep topology
[3, 24, 36, 3], radix = 2, and stem_width = 64.
Approximately 70.2M parameters and 83.9% ImageNet-1k top-1
accuracy in Zhang et al., 2022 (Table 4).
Model Size
Parameters
pretrainedbool= FalseReserved for future pretrained-weight loading. Currently
ignored.
**overridesobject= {}Keyword overrides forwarded into
ResNeStConfig.Returns
ResNeStBackbone with the ResNeSt-200 configuration applied (or
with overrides merged on top of it).
Notes
See Zhang et al., "ResNeSt: Split-Attention Networks", CVPR Workshops 2022 (arXiv:2004.08955), Table 4.
Examples
>>> import lucid
>>> from lucid.models.vision.resnest import resnest_200
>>> model = resnest_200()
>>> x = lucid.randn(1, 3, 224, 224)
>>> out = model(x)
>>> out.last_hidden_state.shape
(1, 2048, 7, 7)