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Add maxvit_rmlp_tiny_rw_256 model def and weights w/ 84.2 top-1 @ 256, 84.8 @ 320
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timm/models/maxxvit.py

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@@ -121,6 +121,9 @@ def _cfg(url='', **kwargs):
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'maxvit_rmlp_nano_rw_256': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/maxvit_rmlp_nano_rw_256_sw-c17bb0d6.pth',
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input_size=(3, 256, 256), pool_size=(8, 8)),
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'maxvit_rmlp_tiny_rw_256': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights-maxx/maxvit_rmlp_tiny_rw_256_sw-2da819a5.pth',
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input_size=(3, 256, 256), pool_size=(8, 8)),
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'maxvit_tiny_pm_256': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)),
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'maxxvit_nano_rw_256': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)),
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@@ -515,6 +518,13 @@ def _next_cfg(
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stem_width=(32, 64),
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**_rw_max_cfg(rel_pos_type='mlp'),
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),
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maxvit_rmlp_tiny_rw_256=MaxxVitCfg(
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embed_dim=(64, 128, 256, 512),
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depths=(2, 2, 5, 2),
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block_type=('M',) * 4,
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stem_width=(32, 64),
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**_rw_max_cfg(rel_pos_type='mlp'),
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),
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maxvit_tiny_pm_256=MaxxVitCfg(
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embed_dim=(64, 128, 256, 512),
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depths=(2, 2, 5, 2),
@@ -1721,6 +1731,11 @@ def maxvit_rmlp_nano_rw_256(pretrained=False, **kwargs):
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return _create_maxxvit('maxvit_rmlp_nano_rw_256', pretrained=pretrained, **kwargs)
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@register_model
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def maxvit_rmlp_tiny_rw_256(pretrained=False, **kwargs):
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return _create_maxxvit('maxvit_rmlp_tiny_rw_256', pretrained=pretrained, **kwargs)
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@register_model
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def maxvit_tiny_pm_256(pretrained=False, **kwargs):
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return _create_maxxvit('maxvit_tiny_pm_256', pretrained=pretrained, **kwargs)

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