diff --git a/timm/models/__init__.py b/timm/models/__init__.py
index f308a580..125d4c1b 100644
--- a/timm/models/__init__.py
+++ b/timm/models/__init__.py
@@ -17,6 +17,7 @@ from .edgenext import *
 from .efficientformer import *
 from .efficientformer_v2 import *
 from .efficientnet import *
+from .efficientvit_mit import *
 from .eva import *
 from .focalnet import *
 from .gcvit import *
diff --git a/timm/models/efficientvit_mit.py b/timm/models/efficientvit_mit.py
new file mode 100644
index 00000000..de175a68
--- /dev/null
+++ b/timm/models/efficientvit_mit.py
@@ -0,0 +1,654 @@
+""" EfficientViT
+
+Paper: `Efficientvit: Enhanced linear attention for high-resolution low-computation visual recognition`
+    - https://arxiv.org/abs/2205.14756
+
+Adapted from official impl at https://github.com/mit-han-lab/efficientvit
+"""
+
+__all__ = ['EfficientViT']
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
+from ._registry import register_model, generate_default_cfgs
+from ._builder import build_model_with_cfg
+from ._manipulate import checkpoint_seq
+from functools import partial
+from timm.layers import SelectAdaptivePool2d
+from collections import OrderedDict
+
+
+def val2list(x: list or tuple or any, repeat_time=1):
+    if isinstance(x, (list, tuple)):
+        return list(x)
+    return [x for _ in range(repeat_time)]
+
+def val2tuple(x: list or tuple or any, min_len: int = 1, idx_repeat: int = -1):
+    # repeat elements if necessary
+    x = val2list(x)
+    if len(x) > 0:
+        x[idx_repeat:idx_repeat] = [x[idx_repeat] for _ in range(min_len - len(x))]
+
+    return tuple(x)
+
+def get_same_padding(kernel_size: int or tuple[int, ...]) -> int or tuple[int, ...]:
+    if isinstance(kernel_size, tuple):
+        return tuple([get_same_padding(ks) for ks in kernel_size])
+    else:
+        assert kernel_size % 2 > 0, "kernel size should be odd number"
+        return kernel_size // 2
+
+class ConvLayer(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size=3,
+        stride=1,
+        dilation=1,
+        groups=1,
+        use_bias=False,
+        dropout=0,
+        norm=nn.BatchNorm2d,
+        act_func=nn.ReLU,
+    ):
+        super(ConvLayer, self).__init__()
+
+        padding = get_same_padding(kernel_size)
+        padding *= dilation
+
+        self.dropout = nn.Dropout(dropout, inplace=False) if dropout > 0 else None
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            kernel_size=(kernel_size, kernel_size),
+            stride=(stride, stride),
+            padding=padding,
+            dilation=(dilation, dilation),
+            groups=groups,
+            bias=use_bias,
+        )
+        self.norm = norm(num_features=out_channels) if norm else None
+        self.act = act_func(inplace=True) if act_func else None
+
+    def forward(self, x):
+        if self.dropout is not None:
+            x = self.dropout(x)
+        x = self.conv(x)
+        if self.norm:
+            x = self.norm(x)
+        if self.act:
+            x = self.act(x)
+        return x
+
+
+class DSConv(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size=3,
+        stride=1,
+        use_bias=False,
+        norm=(nn.BatchNorm2d, nn.BatchNorm2d),
+        act_func=(nn.ReLU6, None),
+    ):
+        super(DSConv, self).__init__()
+
+        use_bias = val2tuple(use_bias, 2)
+        norm = val2tuple(norm, 2)
+        act_func = val2tuple(act_func, 2)
+
+        self.depth_conv = ConvLayer(
+            in_channels,
+            in_channels,
+            kernel_size,
+            stride,
+            groups=in_channels,
+            norm=norm[0],
+            act_func=act_func[0],
+            use_bias=use_bias[0],
+        )
+        self.point_conv = ConvLayer(
+            in_channels,
+            out_channels,
+            1,
+            norm=norm[1],
+            act_func=act_func[1],
+            use_bias=use_bias[1],
+        )
+
+    def forward(self, x):
+        x = self.depth_conv(x)
+        x = self.point_conv(x)
+        return x
+
+
+class MBConv(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size=3,
+        stride=1,
+        mid_channels=None,
+        expand_ratio=6,
+        use_bias=False,
+        norm=(nn.BatchNorm2d, nn.BatchNorm2d, nn.BatchNorm2d),
+        act_func=(nn.ReLU6, nn.ReLU6, None),
+    ):
+        super(MBConv, self).__init__()
+
+        use_bias = val2tuple(use_bias, 3)
+        norm = val2tuple(norm, 3)
+        act_func = val2tuple(act_func, 3)
+        mid_channels = mid_channels or round(in_channels * expand_ratio)
+
+        self.inverted_conv = ConvLayer(
+            in_channels,
+            mid_channels,
+            1,
+            stride=1,
+            norm=norm[0],
+            act_func=act_func[0],
+            use_bias=use_bias[0],
+        )
+        self.depth_conv = ConvLayer(
+            mid_channels,
+            mid_channels,
+            kernel_size,
+            stride=stride,
+            groups=mid_channels,
+            norm=norm[1],
+            act_func=act_func[1],
+            use_bias=use_bias[1],
+        )
+        self.point_conv = ConvLayer(
+            mid_channels,
+            out_channels,
+            1,
+            norm=norm[2],
+            act_func=act_func[2],
+            use_bias=use_bias[2],
+        )
+
+    def forward(self, x):
+        x = self.inverted_conv(x)
+        x = self.depth_conv(x)
+        x = self.point_conv(x)
+        return x
+
+
+class LiteMSA(nn.Module):
+    """Lightweight multi-scale attention"""
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        heads: int or None = None,
+        heads_ratio: float = 1.0,
+        dim=8,
+        use_bias=False,
+        norm=(None, nn.BatchNorm2d),
+        act_func=(None, None),
+        kernel_func=nn.ReLU,
+        scales=(5,),
+    ):
+        super(LiteMSA, self).__init__()
+        heads = heads or int(in_channels // dim * heads_ratio)
+
+        total_dim = heads * dim
+
+        use_bias = val2tuple(use_bias, 2)
+        norm = val2tuple(norm, 2)
+        act_func = val2tuple(act_func, 2)
+
+        self.dim = dim
+        self.qkv = ConvLayer(
+            in_channels,
+            3 * total_dim,
+            1,
+            use_bias=use_bias[0],
+            norm=norm[0],
+            act_func=act_func[0],
+        )
+        self.aggreg = nn.ModuleList(
+            [
+                nn.Sequential(
+                    nn.Conv2d(
+                        3 * total_dim,
+                        3 * total_dim,
+                        scale,
+                        padding=get_same_padding(scale),
+                        groups=3 * total_dim,
+                        bias=use_bias[0],
+                    ),
+                    nn.Conv2d(3 * total_dim, 3 * total_dim, 1, groups=3 * heads, bias=use_bias[0]),
+                )
+                for scale in scales
+            ]
+        )
+        self.kernel_func = kernel_func(inplace=False)
+
+        self.proj = ConvLayer(
+            total_dim * (1 + len(scales)),
+            out_channels,
+            1,
+            use_bias=use_bias[1],
+            norm=norm[1],
+            act_func=act_func[1],
+        )
+
+    def forward(self, x):
+        B, _, H, W = list(x.size())
+
+        # generate multi-scale q, k, v
+        qkv = self.qkv(x)
+        multi_scale_qkv = [qkv]
+        for op in self.aggreg:
+            multi_scale_qkv.append(op(qkv))
+        multi_scale_qkv = torch.cat(multi_scale_qkv, dim=1)
+
+        multi_scale_qkv = torch.reshape(
+            multi_scale_qkv,
+            (
+                B,
+                -1,
+                3 * self.dim,
+                H * W,
+            ),
+        )
+        multi_scale_qkv = torch.transpose(multi_scale_qkv, -1, -2)
+        q, k, v = (
+            multi_scale_qkv[..., 0 : self.dim],
+            multi_scale_qkv[..., self.dim : 2 * self.dim],
+            multi_scale_qkv[..., 2 * self.dim :],
+        )
+
+        # lightweight global attention
+        q = self.kernel_func(q)
+        k = self.kernel_func(k)
+
+        trans_k = k.transpose(-1, -2)
+
+        v = F.pad(v, (0, 1), mode="constant", value=1)
+        kv = torch.matmul(trans_k, v)
+        out = torch.matmul(q, kv)
+        out = out[..., :-1] / (out[..., -1:] + 1e-15)
+
+        # final projecttion
+        out = torch.transpose(out, -1, -2)
+        out = torch.reshape(out, (B, -1, H, W))
+        out = self.proj(out)
+
+        return out
+
+class EfficientViTBlock(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        heads_ratio=1.0,
+        dim=32,
+        expand_ratio=4,
+        norm=nn.BatchNorm2d,
+        act_func=nn.Hardswish,
+    ):
+        super(EfficientViTBlock, self).__init__()
+        self.context_module = ResidualBlock(
+            LiteMSA(
+                in_channels=in_channels,
+                out_channels=in_channels,
+                heads_ratio=heads_ratio,
+                dim=dim,
+                norm=(None, norm),
+            ),
+            nn.Identity(),
+        )
+        local_module = MBConv(
+            in_channels=in_channels,
+            out_channels=in_channels,
+            expand_ratio=expand_ratio,
+            use_bias=(True, True, False),
+            norm=(None, None, norm),
+            act_func=(act_func, act_func, None),
+        )
+        self.local_module = ResidualBlock(local_module, nn.Identity())
+
+    def forward(self, x):
+        x = self.context_module(x)
+        x = self.local_module(x)
+        return x
+
+
+class ResidualBlock(nn.Module):
+    def __init__(
+        self,
+        main: nn.Module or None,
+        shortcut: nn.Module or None,
+        post_act=None,
+        pre_norm: nn.Module or None = None,
+    ):
+        super(ResidualBlock, self).__init__()
+
+        self.pre_norm = pre_norm
+        self.main = main
+        self.shortcut = shortcut
+        self.post_act = post_act(inplace=True) if post_act else nn.Identity()
+
+    def forward_main(self, x):
+        if self.pre_norm is None:
+            return self.main(x)
+        else:
+            return self.main(self.pre_norm(x))
+
+    def forward(self, x):
+        if self.main is None:
+            res = x
+        elif self.shortcut is None:
+            res = self.forward_main(x)
+        else:
+            res = self.forward_main(x) + self.shortcut(x)
+            if self.post_act:
+                res = self.post_act(res)
+        return res
+
+class ClsHead(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        width_list,
+        n_classes=1000,
+        dropout=0,
+        norm=nn.BatchNorm2d,
+        act_func=nn.Hardswish,
+        global_pool='avg',
+    ):
+        super(ClsHead, self).__init__()
+        self.ops = nn.Sequential(
+            ConvLayer(in_channels, width_list[0], 1, norm=norm, act_func=act_func),
+            SelectAdaptivePool2d(pool_type=global_pool, flatten=True, input_fmt='NCHW'),
+            nn.Linear(width_list[0], width_list[1], bias=False),
+            nn.LayerNorm(width_list[1]),
+            act_func(inplace=True),
+            nn.Dropout(dropout, inplace=False) if dropout else nn.Identity(),
+            nn.Linear(width_list[1], n_classes, bias=True),
+        )
+
+    def forward(self, x):
+        x = self.ops(x)
+        return x
+
+
+class EfficientViT(nn.Module):
+    def __init__(
+        self,
+        in_chans=3,
+        width_list=[],
+        depth_list=[],
+        dim=32,
+        expand_ratio=4,
+        norm=nn.BatchNorm2d,
+        act_func=nn.Hardswish,
+        global_pool='avg',
+        head_width_list=[],
+        head_dropout=0.0,
+        num_classes=1000,
+    ):
+        super(EfficientViT, self).__init__()
+        self.grad_checkpointing = False
+        self.global_pool = global_pool
+        # input stem
+        input_stem = [
+            ('in_conv', ConvLayer(
+                in_channels=3,
+                out_channels=width_list[0],
+                kernel_size=3,
+                stride=2,
+                norm=norm,
+                act_func=act_func,
+            ))
+        ]
+        stem_block = 0
+        for _ in range(depth_list[0]):
+            block = self.build_local_block(
+                in_channels=width_list[0],
+                out_channels=width_list[0],
+                stride=1,
+                expand_ratio=1,
+                norm=norm,
+                act_func=act_func,
+            )
+            input_stem.append((f'res{stem_block}', ResidualBlock(block, nn.Identity())))
+            stem_block += 1
+        in_channels = width_list[0]
+        self.stem = nn.Sequential(OrderedDict(input_stem))
+
+        self.feature_info = []
+        stages = []
+        stage_idx = 0
+        for w, d in zip(width_list[1:3], depth_list[1:3]):
+            stage = []
+            for i in range(d):
+                stride = 2 if i == 0 else 1
+                block = self.build_local_block(
+                    in_channels=in_channels,
+                    out_channels=w,
+                    stride=stride,
+                    expand_ratio=expand_ratio,
+                    norm=norm,
+                    act_func=act_func,
+                )
+                block = ResidualBlock(block, nn.Identity() if stride == 1 else None)
+                stage.append(block)
+                in_channels = w
+            stages.append(nn.Sequential(*stage))
+            self.feature_info += [dict(num_chs=in_channels, reduction=stride, module=f'stages.{stage_idx}')]
+            stage_idx += 1
+        
+        for w, d in zip(width_list[3:], depth_list[3:]):
+            stage = []
+            block = self.build_local_block(
+                in_channels=in_channels,
+                out_channels=w,
+                stride=2,
+                expand_ratio=expand_ratio,
+                norm=norm,
+                act_func=act_func,
+                fewer_norm=True,
+            )
+            stage.append(ResidualBlock(block, None))
+            in_channels = w
+
+            for _ in range(d):
+                stage.append(
+                    EfficientViTBlock(
+                        in_channels=in_channels,
+                        dim=dim,
+                        expand_ratio=expand_ratio,
+                        norm=norm,
+                        act_func=act_func,
+                    )
+                )
+            stages.append(nn.Sequential(*stage))
+            self.feature_info += [dict(num_chs=in_channels, reduction=stride, module=f'stages.{stage_idx}')]
+            stage_idx += 1
+        
+        self.stages = nn.Sequential(*stages)
+        self.num_features = in_channels
+        self.head_width_list = head_width_list
+        self.head_dropout = head_dropout
+        if num_classes > 0:
+            self.head = ClsHead(self.num_features, self.head_width_list, n_classes=num_classes, dropout=self.head_dropout, global_pool=self.global_pool)
+        else:
+            if global_pool is not None:
+                self.head = SelectAdaptivePool2d(pool_type=global_pool, flatten=True, input_fmt='NCHW')
+            else:
+                self.head = nn.Identity()
+
+    @staticmethod
+    def build_local_block(
+        in_channels: int,
+        out_channels: int,
+        stride: int,
+        expand_ratio: float,
+        norm: str,
+        act_func: str,
+        fewer_norm: bool = False,
+    ):
+        if expand_ratio == 1:
+            block = DSConv(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                stride=stride,
+                use_bias=(True, False) if fewer_norm else False,
+                norm=(None, norm) if fewer_norm else norm,
+                act_func=(act_func, None),
+            )
+        else:
+            block = MBConv(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                stride=stride,
+                expand_ratio=expand_ratio,
+                use_bias=(True, True, False) if fewer_norm else False,
+                norm=(None, None, norm) if fewer_norm else norm,
+                act_func=(act_func, act_func, None),
+            )
+        return block
+
+    @torch.jit.ignore
+    def group_matcher(self, coarse=False):
+        matcher = dict(
+            stem=r'^stem',  # stem and embed
+            blocks=[(r'^stages\.(\d+)', None)]
+        )
+        return matcher
+
+    @torch.jit.ignore
+    def set_grad_checkpointing(self, enable=True):
+        self.grad_checkpointing = enable
+
+    @torch.jit.ignore
+    def get_classifier(self):
+        return self.head
+
+    def reset_classifier(self, num_classes, global_pool=None, dropout=0):
+        self.num_classes = num_classes
+        if global_pool is not None:
+            self.global_pool = global_pool
+        if num_classes > 0:
+            self.head = ClsHead(self.num_features, self.head_width_list, n_classes=num_classes, dropout=self.head_dropout, global_pool=global_pool)
+        else:
+            if global_pool is not None:
+                self.head = SelectAdaptivePool2d(pool_type=global_pool, flatten=True, input_fmt='NCHW')
+            else:
+                self.head = nn.Identity()
+
+    def forward_features(self, x):
+        x = self.stem(x)
+        if self.grad_checkpointing and not torch.jit.is_scripting():
+            x = checkpoint_seq(self.stages, x)
+        else:
+            x = self.stages(x)
+        return x
+
+    def forward_head(self, x, pre_logits: bool = False):
+        return x if pre_logits else self.head(x)
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        x = self.forward_head(x)
+        return x
+
+
+def checkpoint_filter_fn(state_dict, model):
+    target_keys = list(model.state_dict().keys())
+    if 'state_dict' in state_dict.keys():
+        state_dict = state_dict['state_dict']
+    out_dict = {}
+    for i, (k, v) in enumerate(state_dict.items()):
+        out_dict[target_keys[i]] = v
+    return out_dict
+
+
+def _cfg(url='', **kwargs):
+    return {
+        'url': url,
+        'num_classes': 1000,
+        'mean': IMAGENET_DEFAULT_MEAN,
+        'std': IMAGENET_DEFAULT_STD,
+        'first_conv': 'stem.in_conv.conv',
+        'classifier': 'head',
+        **kwargs,
+    }
+
+
+default_cfgs = generate_default_cfgs(
+    {
+        'efficientvit_b0.r224_in1k': _cfg(
+            # url='https://drive.google.com/file/d/1ganFBZmmvCTpgUwiLb8ePD6NBNxRyZDk/view?usp=drive_link'
+        ),
+        'efficientvit_b1.r224_in1k': _cfg(
+            # url='https://drive.google.com/file/d/1hKN_hvLG4nmRzbfzKY7GlqwpR5uKpOOk/view?usp=share_link'
+        ),
+        'efficientvit_b1.r256_in1k': _cfg(
+            # url='https://drive.google.com/file/d/1hXcG_jB0ODMOESsSkzVye-58B4F3Cahs/view?usp=share_link'
+        ),
+        'efficientvit_b1.r288_in1k': _cfg(
+            # url='https://drive.google.com/file/d/1sE_Suz9gOOUO7o5r9eeAT4nKK8Hrbhsu/view?usp=share_link'
+        ),
+        'efficientvit_b2.r224_in1k': _cfg(
+            # url='https://drive.google.com/file/d/1DiM-iqVGTrq4te8mefHl3e1c12u4qR7d/view?usp=share_link'
+        ),
+        'efficientvit_b2.r256_in1k': _cfg(
+            # url='https://drive.google.com/file/d/192OOk4ISitwlyW979M-FSJ_fYMMW9HQz/view?usp=share_link'
+        ),
+        'efficientvit_b2.r288_in1k': _cfg(
+            # url='https://drive.google.com/file/d/1aodcepOyne667hvBAGpf9nDwmd5g0NpU/view?usp=share_link'
+        ),
+        'efficientvit_b3.r224_in1k': _cfg(
+            # url='https://drive.google.com/file/d/18RZDGLiY8KsyJ7LGic4mg1JHwd-a_ky6/view?usp=share_link'
+        ),
+        'efficientvit_b3.r256_in1k': _cfg(
+            # url='https://drive.google.com/file/d/1y1rnir4I0XiId-oTCcHhs7jqnrHGFi-g/view?usp=share_link'
+        ),
+        'efficientvit_b3.r288_in1k': _cfg(
+            # url='https://drive.google.com/file/d/1KfwbGtlyFgslNr4LIHERv6aCfkItEvRk/view?usp=share_link'
+        ),
+    }
+)
+
+
+def _create_efficientvit(variant, pretrained=False, **kwargs):
+    model = build_model_with_cfg(
+        EfficientViT,
+        variant,
+        pretrained,
+        pretrained_filter_fn=checkpoint_filter_fn,
+        **kwargs
+    )
+    return model
+
+
+@register_model
+def efficientvit_b0(pretrained=False, **kwargs):
+    model_args = dict(width_list=[8, 16, 32, 64, 128], depth_list=[1, 2, 2, 2, 2], dim=16, head_width_list=[1024, 1280])
+    return _create_efficientvit('efficientvit_b0', pretrained=pretrained, **dict(model_args, **kwargs))
+
+@register_model
+def efficientvit_b1(pretrained=False, **kwargs):
+    model_args = dict(width_list=[16, 32, 64, 128, 256], depth_list=[1, 2, 3, 3, 4], dim=16, head_width_list=[1536, 1600])
+    return _create_efficientvit('efficientvit_b0', pretrained=pretrained, **dict(model_args, **kwargs))
+
+@register_model
+def efficientvit_b2(pretrained=False, **kwargs):
+    model_args = dict(width_list=[24, 48, 96, 192, 384], depth_list=[1, 3, 4, 4, 6], dim=32, head_width_list=[2304, 2560])
+    return _create_efficientvit('efficientvit_b0', pretrained=pretrained, **dict(model_args, **kwargs))
+
+@register_model
+def efficientvit_b3(pretrained=False, **kwargs):
+    model_args = dict(width_list=[32, 64, 128, 256, 512], depth_list=[1, 4, 6, 6, 9], dim=32, head_width_list=[2304, 2560])
+    return _create_efficientvit('efficientvit_b0', pretrained=pretrained, **dict(model_args, **kwargs))