diff --git a/timm/models/__init__.py b/timm/models/__init__.py
index 60fd483c44..c5b1984f20 100644
--- a/timm/models/__init__.py
+++ b/timm/models/__init__.py
@@ -35,6 +35,7 @@
 from .inception_v4 import *
 from .levit import *
 from .maxxvit import *
+from .mambaout import *
 from .metaformer import *
 from .mlp_mixer import *
 from .mobilenetv3 import *
diff --git a/timm/models/mambaout.py b/timm/models/mambaout.py
new file mode 100644
index 0000000000..c748e408ea
--- /dev/null
+++ b/timm/models/mambaout.py
@@ -0,0 +1,638 @@
+"""
+MambaOut models for image classification.
+Some implementations are modified from:
+timm (https://github.com/rwightman/pytorch-image-models),
+MetaFormer (https://github.com/sail-sg/metaformer),
+InceptionNeXt (https://github.com/sail-sg/inceptionnext)
+"""
+from collections import OrderedDict
+from typing import Optional
+
+import torch
+from torch import nn
+
+from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+from timm.layers import trunc_normal_, DropPath, LayerNorm, LayerScale, ClNormMlpClassifierHead, get_act_layer
+from ._builder import build_model_with_cfg
+from ._manipulate import checkpoint_seq
+from ._registry import register_model, generate_default_cfgs
+
+
+class Stem(nn.Module):
+    r""" Code modified from InternImage:
+        https://github.com/OpenGVLab/InternImage
+    """
+
+    def __init__(
+            self,
+            in_chs=3,
+            out_chs=96,
+            mid_norm: bool = True,
+            act_layer=nn.GELU,
+            norm_layer=LayerNorm,
+    ):
+        super().__init__()
+        self.conv1 = nn.Conv2d(
+            in_chs,
+            out_chs // 2,
+            kernel_size=3,
+            stride=2,
+            padding=1
+        )
+        self.norm1 = norm_layer(out_chs // 2) if mid_norm else None
+        self.act = act_layer()
+        self.conv2 = nn.Conv2d(
+            out_chs // 2,
+            out_chs,
+            kernel_size=3,
+            stride=2,
+            padding=1
+        )
+        self.norm2 = norm_layer(out_chs)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        if self.norm1 is not None:
+            x = x.permute(0, 2, 3, 1)
+            x = self.norm1(x)
+            x = x.permute(0, 3, 1, 2)
+        x = self.act(x)
+        x = self.conv2(x)
+        x = x.permute(0, 2, 3, 1)
+        x = self.norm2(x)
+        return x
+
+
+class DownsampleNormFirst(nn.Module):
+
+    def __init__(
+            self,
+            in_chs=96,
+            out_chs=198,
+            norm_layer=LayerNorm,
+    ):
+        super().__init__()
+        self.norm = norm_layer(in_chs)
+        self.conv = nn.Conv2d(
+            in_chs,
+            out_chs,
+            kernel_size=3,
+            stride=2,
+            padding=1
+        )
+
+    def forward(self, x):
+        x = self.norm(x)
+        x = x.permute(0, 3, 1, 2)
+        x = self.conv(x)
+        x = x.permute(0, 2, 3, 1)
+        return x
+
+
+class Downsample(nn.Module):
+
+    def __init__(
+            self,
+            in_chs=96,
+            out_chs=198,
+            norm_layer=LayerNorm,
+    ):
+        super().__init__()
+        self.conv = nn.Conv2d(
+            in_chs,
+            out_chs,
+            kernel_size=3,
+            stride=2,
+            padding=1
+        )
+        self.norm = norm_layer(out_chs)
+
+    def forward(self, x):
+        x = x.permute(0, 3, 1, 2)
+        x = self.conv(x)
+        x = x.permute(0, 2, 3, 1)
+        x = self.norm(x)
+        return x
+
+
+class MlpHead(nn.Module):
+    """ MLP classification head
+    """
+
+    def __init__(
+            self,
+            in_features,
+            num_classes=1000,
+            pool_type='avg',
+            act_layer=nn.GELU,
+            mlp_ratio=4,
+            norm_layer=LayerNorm,
+            drop_rate=0.,
+            bias=True,
+    ):
+        super().__init__()
+        if mlp_ratio is not None:
+            hidden_size = int(mlp_ratio * in_features)
+        else:
+            hidden_size = None
+        self.pool_type = pool_type
+        self.in_features = in_features
+        self.hidden_size = hidden_size or in_features
+
+        self.norm = norm_layer(in_features)
+        if hidden_size:
+            self.pre_logits = nn.Sequential(OrderedDict([
+                ('fc', nn.Linear(in_features, hidden_size)),
+                ('act', act_layer()),
+                ('norm', norm_layer(hidden_size))
+            ]))
+            self.num_features = hidden_size
+        else:
+            self.num_features = in_features
+            self.pre_logits = nn.Identity()
+
+        self.fc = nn.Linear(hidden_size, num_classes, bias=bias)
+        self.head_dropout = nn.Dropout(drop_rate)
+
+    def reset(self, num_classes: int, pool_type: Optional[str] = None, reset_other: bool = False):
+        if pool_type is not None:
+            self.pool_type = pool_type
+        if reset_other:
+            self.norm = nn.Identity()
+            self.pre_logits = nn.Identity()
+            self.num_features = self.in_features
+        self.fc = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
+
+    def forward(self, x, pre_logits: bool = False):
+        if self.pool_type == 'avg':
+            x = x.mean((1, 2))
+        x = self.norm(x)
+        x = self.pre_logits(x)
+        x = self.head_dropout(x)
+        if pre_logits:
+            return x
+        x = self.fc(x)
+        return x
+
+
+class GatedConvBlock(nn.Module):
+    r""" Our implementation of Gated CNN Block: https://arxiv.org/pdf/1612.08083
+    Args:
+        conv_ratio: control the number of channels to conduct depthwise convolution.
+            Conduct convolution on partial channels can improve paraitcal efficiency.
+            The idea of partial channels is from ShuffleNet V2 (https://arxiv.org/abs/1807.11164) and
+            also used by InceptionNeXt (https://arxiv.org/abs/2303.16900) and FasterNet (https://arxiv.org/abs/2303.03667)
+    """
+
+    def __init__(
+            self,
+            dim,
+            expansion_ratio=8 / 3,
+            kernel_size=7,
+            conv_ratio=1.0,
+            ls_init_value=None,
+            norm_layer=LayerNorm,
+            act_layer=nn.GELU,
+            drop_path=0.,
+            **kwargs
+    ):
+        super().__init__()
+        self.norm = norm_layer(dim)
+        hidden = int(expansion_ratio * dim)
+        self.fc1 = nn.Linear(dim, hidden * 2)
+        self.act = act_layer()
+        conv_channels = int(conv_ratio * dim)
+        self.split_indices = (hidden, hidden - conv_channels, conv_channels)
+        self.conv = nn.Conv2d(
+            conv_channels,
+            conv_channels,
+            kernel_size=kernel_size,
+            padding=kernel_size // 2,
+            groups=conv_channels
+        )
+        self.fc2 = nn.Linear(hidden, dim)
+        self.ls = LayerScale(dim) if ls_init_value is not None else nn.Identity()
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+
+    def forward(self, x):
+        shortcut = x  # [B, H, W, C]
+        x = self.norm(x)
+        x = self.fc1(x)
+        g, i, c = torch.split(x, self.split_indices, dim=-1)
+        c = c.permute(0, 3, 1, 2)  # [B, H, W, C] -> [B, C, H, W]
+        c = self.conv(c)
+        c = c.permute(0, 2, 3, 1)  # [B, C, H, W] -> [B, H, W, C]
+        x = self.fc2(self.act(g) * torch.cat((i, c), dim=-1))
+        x = self.ls(x)
+        x = self.drop_path(x)
+        return x + shortcut
+
+
+class MambaOutStage(nn.Module):
+
+    def __init__(
+            self,
+            dim,
+            dim_out: Optional[int] = None,
+            depth: int = 4,
+            expansion_ratio=8 / 3,
+            kernel_size=7,
+            conv_ratio=1.0,
+            downsample: str = '',
+            ls_init_value: Optional[float] = None,
+            norm_layer=LayerNorm,
+            act_layer=nn.GELU,
+            drop_path=0.,
+    ):
+        super().__init__()
+        dim_out = dim_out or dim
+        self.grad_checkpointing = False
+
+        if downsample == 'conv':
+            self.downsample = Downsample(dim, dim_out, norm_layer=norm_layer)
+        elif downsample == 'conv_nf':
+            self.downsample = DownsampleNormFirst(dim, dim_out, norm_layer=norm_layer)
+        else:
+            assert dim == dim_out
+            self.downsample = nn.Identity()
+
+        self.blocks = nn.Sequential(*[
+            GatedConvBlock(
+                dim=dim_out,
+                expansion_ratio=expansion_ratio,
+                kernel_size=kernel_size,
+                conv_ratio=conv_ratio,
+                ls_init_value=ls_init_value,
+                norm_layer=norm_layer,
+                act_layer=act_layer,
+                drop_path=drop_path[j] if isinstance(drop_path, (list, tuple)) else drop_path,
+            )
+            for j in range(depth)
+        ])
+
+    def forward(self, x):
+        x = self.downsample(x)
+        if self.grad_checkpointing and not torch.jit.is_scripting():
+            x = checkpoint_seq(self.blocks, x)
+        else:
+            x = self.blocks(x)
+        return x
+
+
+class MambaOut(nn.Module):
+    r""" MetaFormer
+        A PyTorch impl of : `MetaFormer Baselines for Vision`  -
+          https://arxiv.org/abs/2210.13452
+
+    Args:
+        in_chans (int): Number of input image channels. Default: 3.
+        num_classes (int): Number of classes for classification head. Default: 1000.
+        depths (list or tuple): Number of blocks at each stage. Default: [3, 3, 9, 3].
+        dims (int): Feature dimension at each stage. Default: [96, 192, 384, 576].
+        downsample_layers: (list or tuple): Downsampling layers before each stage.
+        drop_path_rate (float): Stochastic depth rate. Default: 0.
+        output_norm: norm before classifier head. Default: partial(nn.LayerNorm, eps=1e-6).
+        head_fn: classification head. Default: nn.Linear.
+        head_dropout (float): dropout for MLP classifier. Default: 0.
+    """
+
+    def __init__(
+            self,
+            in_chans=3,
+            num_classes=1000,
+            global_pool='avg',
+            depths=(3, 3, 9, 3),
+            dims=(96, 192, 384, 576),
+            norm_layer=LayerNorm,
+            act_layer=nn.GELU,
+            conv_ratio=1.0,
+            expansion_ratio=8/3,
+            kernel_size=7,
+            stem_mid_norm=True,
+            ls_init_value=None,
+            downsample='conv',
+            drop_path_rate=0.,
+            drop_rate=0.,
+            head_fn='default',
+    ):
+        super().__init__()
+        self.num_classes = num_classes
+        self.drop_rate = drop_rate
+        self.output_fmt = 'NHWC'
+        if not isinstance(depths, (list, tuple)):
+            depths = [depths]  # it means the model has only one stage
+        if not isinstance(dims, (list, tuple)):
+            dims = [dims]
+        act_layer = get_act_layer(act_layer)
+
+        num_stage = len(depths)
+        self.num_stage = num_stage
+        self.feature_info = []
+
+        self.stem = Stem(
+            in_chans,
+            dims[0],
+            mid_norm=stem_mid_norm,
+            act_layer=act_layer,
+            norm_layer=norm_layer,
+        )
+        prev_dim = dims[0]
+        dp_rates = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
+        cur = 0
+        curr_stride = 4
+        self.stages = nn.Sequential()
+        for i in range(num_stage):
+            dim = dims[i]
+            stride = 2 if curr_stride == 2 or i > 0 else 1
+            curr_stride *= stride
+            stage = MambaOutStage(
+                dim=prev_dim,
+                dim_out=dim,
+                depth=depths[i],
+                kernel_size=kernel_size,
+                conv_ratio=conv_ratio,
+                expansion_ratio=expansion_ratio,
+                downsample=downsample if i > 0 else '',
+                ls_init_value=ls_init_value,
+                norm_layer=norm_layer,
+                act_layer=act_layer,
+                drop_path=dp_rates[i],
+            )
+            self.stages.append(stage)
+            prev_dim = dim
+            # NOTE feature_info use currently assumes stage 0 == stride 1, rest are stride 2
+            self.feature_info += [dict(num_chs=prev_dim, reduction=curr_stride, module=f'stages.{i}')]
+            cur += depths[i]
+
+        if head_fn == 'default':
+            # specific to this model, unusual norm -> pool -> fc -> act -> norm -> fc combo
+            self.head = MlpHead(
+                prev_dim,
+                num_classes,
+                pool_type=global_pool,
+                drop_rate=drop_rate,
+                norm_layer=norm_layer,
+            )
+        else:
+            # more typical norm -> pool -> fc -> act -> fc
+            self.head = ClNormMlpClassifierHead(
+                prev_dim,
+                num_classes,
+                hidden_size=int(prev_dim * 4),
+                pool_type=global_pool,
+                norm_layer=norm_layer,
+                drop_rate=drop_rate,
+            )
+        self.num_features = prev_dim
+        self.head_hidden_size = self.head.num_features
+
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, (nn.Conv2d, nn.Linear)):
+            trunc_normal_(m.weight, std=.02)
+            if m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+
+    @torch.jit.ignore
+    def group_matcher(self, coarse=False):
+        return dict(
+            stem=r'^stem',
+            blocks=r'^stages\.(\d+)' if coarse else [
+                (r'^stages\.(\d+)\.downsample', (0,)),  # blocks
+                (r'^stages\.(\d+)\.blocks\.(\d+)', None),
+            ]
+        )
+
+    @torch.jit.ignore
+    def set_grad_checkpointing(self, enable=True):
+        for s in self.stages:
+            s.grad_checkpointing = enable
+
+    @torch.jit.ignore
+    def get_classifier(self) -> nn.Module:
+        return self.head.fc
+
+    def reset_classifier(self, num_classes: int, global_pool: Optional[str] = None):
+        self.num_classes = num_classes
+        self.head.reset(num_classes, global_pool)
+
+    def forward_features(self, x):
+        x = self.stem(x)
+        x = self.stages(x)
+        return x
+
+    def forward_head(self, x, pre_logits: bool = False):
+        x = self.head(x, pre_logits=pre_logits) if pre_logits else self.head(x)
+        return x
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        x = self.forward_head(x)
+        return x
+
+
+def checkpoint_filter_fn(state_dict, model):
+    if 'model' in state_dict:
+        state_dict = state_dict['model']
+    if 'stem.conv1.weight' in state_dict:
+        return state_dict
+
+    import re
+    out_dict = {}
+    for k, v in state_dict.items():
+        k = k.replace('downsample_layers.0.', 'stem.')
+        k = re.sub(r'stages.([0-9]+).([0-9]+)', r'stages.\1.blocks.\2', k)
+        k = re.sub(r'downsample_layers.([0-9]+)', r'stages.\1.downsample', k)
+        # remap head names
+        if k.startswith('norm.'):
+            # this is moving to head since it's after the pooling
+            k = k.replace('norm.', 'head.norm.')
+        elif k.startswith('head.'):
+            k = k.replace('head.fc1.', 'head.pre_logits.fc.')
+            k = k.replace('head.norm.', 'head.pre_logits.norm.')
+            k = k.replace('head.fc2.', 'head.fc.')
+        out_dict[k] = v
+
+    return out_dict
+
+
+def _cfg(url='', **kwargs):
+    return {
+        'url': url,
+        'num_classes': 1000, 'input_size': (3, 224, 224), 'test_input_size': (3, 288, 288),
+        'pool_size': (7, 7), 'crop_pct': 1.0, 'interpolation': 'bicubic',
+        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
+        'first_conv': 'stem.conv1', 'classifier': 'head.fc',
+        **kwargs
+    }
+
+
+default_cfgs = generate_default_cfgs({
+    # original weights
+    'mambaout_femto.in1k': _cfg(
+        hf_hub_id='timm/'),
+    'mambaout_kobe.in1k': _cfg(
+        hf_hub_id='timm/'),
+    'mambaout_tiny.in1k': _cfg(
+        hf_hub_id='timm/'),
+    'mambaout_small.in1k': _cfg(
+        hf_hub_id='timm/'),
+    'mambaout_base.in1k': _cfg(
+        hf_hub_id='timm/'),
+
+    # timm experiments below
+    'mambaout_small_rw.sw_e450_in1k': _cfg(
+        hf_hub_id='timm/',
+    ),
+    'mambaout_base_short_rw.sw_e500_in1k': _cfg(
+        hf_hub_id='timm/',
+        crop_pct=0.95, test_crop_pct=1.0,
+    ),
+    'mambaout_base_tall_rw.sw_e500_in1k': _cfg(
+        hf_hub_id='timm/',
+        crop_pct=0.95, test_crop_pct=1.0,
+    ),
+    'mambaout_base_wide_rw.sw_e500_in1k': _cfg(
+        hf_hub_id='timm/',
+        crop_pct=0.95, test_crop_pct=1.0,
+    ),
+    'mambaout_base_plus_rw.sw_e150_in12k_ft_in1k': _cfg(
+        hf_hub_id='timm/',
+    ),
+    'mambaout_base_plus_rw.sw_e150_in12k': _cfg(
+        hf_hub_id='timm/',
+        num_classes=11821,
+    ),
+    'test_mambaout': _cfg(input_size=(3, 160, 160), test_input_size=(3, 192, 192), pool_size=(5, 5)),
+})
+
+
+def _create_mambaout(variant, pretrained=False, **kwargs):
+    model = build_model_with_cfg(
+        MambaOut, variant, pretrained,
+        pretrained_filter_fn=checkpoint_filter_fn,
+        feature_cfg=dict(out_indices=(0, 1, 2, 3), flatten_sequential=True),
+        **kwargs,
+    )
+    return model
+
+
+# a series of MambaOut models
+@register_model
+def mambaout_femto(pretrained=False, **kwargs):
+    model_args = dict(depths=(3, 3, 9, 3), dims=(48, 96, 192, 288))
+    return _create_mambaout('mambaout_femto', pretrained=pretrained, **dict(model_args, **kwargs))
+
+# Kobe Memorial Version with 24 Gated CNN blocks
+@register_model
+def mambaout_kobe(pretrained=False, **kwargs):
+    model_args = dict(depths=[3, 3, 15, 3], dims=[48, 96, 192, 288])
+    return _create_mambaout('mambaout_kobe', pretrained=pretrained, **dict(model_args, **kwargs))
+
+@register_model
+def mambaout_tiny(pretrained=False, **kwargs):
+    model_args = dict(depths=[3, 3, 9, 3], dims=[96, 192, 384, 576])
+    return _create_mambaout('mambaout_tiny', pretrained=pretrained, **dict(model_args, **kwargs))
+
+
+@register_model
+def mambaout_small(pretrained=False, **kwargs):
+    model_args = dict(depths=[3, 4, 27, 3], dims=[96, 192, 384, 576])
+    return _create_mambaout('mambaout_small', pretrained=pretrained, **dict(model_args, **kwargs))
+
+
+@register_model
+def mambaout_base(pretrained=False, **kwargs):
+    model_args = dict(depths=[3, 4, 27, 3], dims=[128, 256, 512, 768])
+    return _create_mambaout('mambaout_base', pretrained=pretrained, **dict(model_args, **kwargs))
+
+
+@register_model
+def mambaout_small_rw(pretrained=False, **kwargs):
+    model_args = dict(
+        depths=[3, 4, 27, 3],
+        dims=[96, 192, 384, 576],
+        stem_mid_norm=False,
+        downsample='conv_nf',
+        ls_init_value=1e-6,
+        head_fn='norm_mlp',
+    )
+    return _create_mambaout('mambaout_small_rw', pretrained=pretrained, **dict(model_args, **kwargs))
+
+
+@register_model
+def mambaout_base_short_rw(pretrained=False, **kwargs):
+    model_args = dict(
+        depths=(3, 3, 25, 3),
+        dims=(128, 256, 512, 768),
+        expansion_ratio=3.0,
+        conv_ratio=1.25,
+        stem_mid_norm=False,
+        downsample='conv_nf',
+        ls_init_value=1e-6,
+        head_fn='norm_mlp',
+    )
+    return _create_mambaout('mambaout_base_short_rw', pretrained=pretrained, **dict(model_args, **kwargs))
+
+
+@register_model
+def mambaout_base_tall_rw(pretrained=False, **kwargs):
+    model_args = dict(
+        depths=(3, 4, 30, 3),
+        dims=(128, 256, 512, 768),
+        expansion_ratio=2.5,
+        conv_ratio=1.25,
+        stem_mid_norm=False,
+        downsample='conv_nf',
+        ls_init_value=1e-6,
+        head_fn='norm_mlp',
+    )
+    return _create_mambaout('mambaout_base_tall_rw', pretrained=pretrained, **dict(model_args, **kwargs))
+
+
+@register_model
+def mambaout_base_wide_rw(pretrained=False, **kwargs):
+    model_args = dict(
+        depths=(3, 4, 27, 3),
+        dims=(128, 256, 512, 768),
+        expansion_ratio=3.0,
+        conv_ratio=1.5,
+        stem_mid_norm=False,
+        downsample='conv_nf',
+        ls_init_value=1e-6,
+        act_layer='silu',
+        head_fn='norm_mlp',
+    )
+    return _create_mambaout('mambaout_base_wide_rw', pretrained=pretrained, **dict(model_args, **kwargs))
+
+
+@register_model
+def mambaout_base_plus_rw(pretrained=False, **kwargs):
+    model_args = dict(
+        depths=(3, 4, 30, 3),
+        dims=(128, 256, 512, 768),
+        expansion_ratio=3.0,
+        conv_ratio=1.5,
+        stem_mid_norm=False,
+        downsample='conv_nf',
+        ls_init_value=1e-6,
+        act_layer='silu',
+        head_fn='norm_mlp',
+    )
+    return _create_mambaout('mambaout_base_plus_rw', pretrained=pretrained, **dict(model_args, **kwargs))
+
+
+@register_model
+def test_mambaout(pretrained=False, **kwargs):
+    model_args = dict(
+        depths=(1, 1, 3, 1),
+        dims=(16, 32, 48, 64),
+        expansion_ratio=3,
+        stem_mid_norm=False,
+        downsample='conv_nf',
+        ls_init_value=1e-4,
+        act_layer='silu',
+        head_fn='norm_mlp',
+    )
+    return _create_mambaout('test_mambaout', pretrained=pretrained, **dict(model_args, **kwargs))