diff --git a/flowvision/models/__init__.py b/flowvision/models/__init__.py
index f1af93ff..0502a7e8 100644
--- a/flowvision/models/__init__.py
+++ b/flowvision/models/__init__.py
@@ -38,6 +38,8 @@
 from .genet import *
 from .hrnet import *
 from .fan import *
+from .beit import *
+
 
 from . import style_transfer
 from . import detection
diff --git a/flowvision/models/beit.py b/flowvision/models/beit.py
new file mode 100644
index 00000000..485051b9
--- /dev/null
+++ b/flowvision/models/beit.py
@@ -0,0 +1,519 @@
+import math
+from functools import partial
+from typing import Optional, Tuple
+
+import oneflow as flow
+import oneflow.nn as nn
+import oneflow.nn.functional as F
+
+from flowvision.layers import trunc_normal_, DropPath, Mlp, PatchEmbed
+from .registry import ModelCreator
+from .utils import load_state_dict_from_url
+
+
+model_urls = {
+    "beit_base_patch16_224": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/Beit/beit_base_patch16_224.zip",
+    "beit_base_patch16_384": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/Beit/beit_base_patch16_384.zip",
+    "beit_large_patch16_224": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/Beit/beit_large_patch16_224.zip",
+    "beit_large_patch16_384": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/Beit/beit_large_patch16_384.zip",
+    "beit_large_patch16_512": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/Beit/beit_large_patch16_512.zip",
+    }
+
+
+def gen_relative_position_index(window_size: Tuple[int, int]) -> flow.Tensor:
+    num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
+    # cls to token & token 2 cls & cls to cls
+    # get pair-wise relative position index for each token inside the window
+    window_area = window_size[0] * window_size[1]
+    coords = flow.stack(
+        flow.meshgrid([flow.arange(window_size[0]), flow.arange(window_size[1])])
+    )  # 2, Wh, Ww
+    coords_flatten = flow.flatten(coords, 1)  # 2, Wh*Ww
+    relative_coords = (
+        coords_flatten[:, :, None] - coords_flatten[:, None, :]
+    )  # 2, Wh*Ww, Wh*Ww
+    relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+    relative_coords[:, :, 0] += window_size[0] - 1  # shift to start from 0
+    relative_coords[:, :, 1] += window_size[1] - 1
+    relative_coords[:, :, 0] *= 2 * window_size[1] - 1
+    relative_position_index = flow.zeros(
+        (window_area + 1,) * 2, dtype=relative_coords.dtype
+    )
+    relative_position_index[1:, 1:] = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+    relative_position_index[0, 0:] = num_relative_distance - 3
+    relative_position_index[0:, 0] = num_relative_distance - 2
+    relative_position_index[0, 0] = num_relative_distance - 1
+    return relative_position_index
+
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        dim,
+        num_heads=8,
+        qkv_bias=False,
+        attn_drop=0.0,
+        proj_drop=0.0,
+        window_size=None,
+        attn_head_dim=None,
+    ):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        if attn_head_dim is not None:
+            head_dim = attn_head_dim
+        all_head_dim = head_dim * self.num_heads
+        self.scale = head_dim ** -0.5
+
+        self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
+        if qkv_bias:
+            self.q_bias = nn.Parameter(flow.zeros(all_head_dim))
+            self.register_buffer("k_bias", flow.zeros(all_head_dim), persistent=False)
+            self.v_bias = nn.Parameter(flow.zeros(all_head_dim))
+        else:
+            self.q_bias = None
+            self.k_bias = None
+            self.v_bias = None
+
+        if window_size:
+            self.window_size = window_size
+            self.num_relative_distance = (2 * window_size[0] - 1) * (
+                2 * window_size[1] - 1
+            ) + 3
+            self.relative_position_bias_table = nn.Parameter(
+                flow.zeros(self.num_relative_distance, num_heads)
+            )  # 2*Wh-1 * 2*Ww-1, nH
+            self.register_buffer(
+                "relative_position_index", gen_relative_position_index(window_size)
+            )
+        else:
+            self.window_size = None
+            self.relative_position_bias_table = None
+            self.relative_position_index = None
+
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(all_head_dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def _get_rel_pos_bias(self):
+        relative_position_bias = self.relative_position_bias_table[
+            self.relative_position_index.view(-1)
+        ].view(
+            self.window_size[0] * self.window_size[1] + 1,
+            self.window_size[0] * self.window_size[1] + 1,
+            -1,
+        )  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(
+            2, 0, 1
+        ).contiguous()  # nH, Wh*Ww, Wh*Ww
+        return relative_position_bias.unsqueeze(0)
+
+    def forward(self, x, shared_rel_pos_bias: Optional[flow.Tensor] = None):
+        B, N, C = x.shape
+
+        qkv_bias = (
+            flow.cat((self.q_bias, self.k_bias, self.v_bias))
+            if self.q_bias is not None
+            else None
+        )
+        qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
+        qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv.unbind(0)  # make torchscript happy (cannot use tensor as tuple)
+
+        q = q * self.scale
+        attn = q @ k.transpose(-2, -1)
+
+        if self.relative_position_bias_table is not None:
+            attn = attn + self._get_rel_pos_bias()
+        if shared_rel_pos_bias is not None:
+            attn = attn + shared_rel_pos_bias
+
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class Block(nn.Module):
+    def __init__(
+        self,
+        dim,
+        num_heads,
+        mlp_ratio=4.0,
+        qkv_bias=False,
+        drop=0.0,
+        attn_drop=0.0,
+        drop_path=0.0,
+        init_values=None,
+        act_layer=nn.GELU,
+        norm_layer=nn.LayerNorm,
+        window_size=None,
+        attn_head_dim=None,
+    ):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            attn_drop=attn_drop,
+            proj_drop=drop,
+            window_size=window_size,
+            attn_head_dim=attn_head_dim,
+        )
+        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(
+            in_features=dim,
+            hidden_features=mlp_hidden_dim,
+            act_layer=act_layer,
+            drop=drop,
+        )
+
+        if init_values:
+            self.gamma_1 = nn.Parameter(
+                init_values * flow.ones(dim), requires_grad=True
+            )
+            self.gamma_2 = nn.Parameter(
+                init_values * flow.ones(dim), requires_grad=True
+            )
+        else:
+            self.gamma_1, self.gamma_2 = None, None
+
+    def forward(self, x, shared_rel_pos_bias: Optional[flow.Tensor] = None):
+        if self.gamma_1 is None:
+            x = x + self.drop_path(
+                self.attn(self.norm1(x), shared_rel_pos_bias=shared_rel_pos_bias)
+            )
+            x = x + self.drop_path(self.mlp(self.norm2(x)))
+        else:
+            x = x + self.drop_path(
+                self.gamma_1
+                * self.attn(self.norm1(x), shared_rel_pos_bias=shared_rel_pos_bias)
+            )
+            x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
+        return x
+
+
+class RelativePositionBias(nn.Module):
+    def __init__(self, window_size, num_heads):
+        super().__init__()
+        self.window_size = window_size
+        self.window_area = window_size[0] * window_size[1]
+        num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
+        self.relative_position_bias_table = nn.Parameter(
+            flow.zeros(num_relative_distance, num_heads)
+        )
+        # trunc_normal_(self.relative_position_bias_table, std=.02)
+        self.register_buffer(
+            "relative_position_index", gen_relative_position_index(window_size)
+        )
+
+    def forward(self):
+        relative_position_bias = self.relative_position_bias_table[
+            self.relative_position_index.view(-1)
+        ].view(
+            self.window_area + 1, self.window_area + 1, -1
+        )  # Wh*Ww,Wh*Ww,nH
+        return relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+
+
+class Beit(nn.Module):
+    """ Vision Transformer with support for patch or hybrid CNN input stage
+    """
+
+    def __init__(
+        self,
+        img_size=224,
+        patch_size=16,
+        in_chans=3,
+        num_classes=1000,
+        global_pool="avg",
+        embed_dim=768,
+        depth=12,
+        num_heads=12,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        drop_rate=0.0,
+        attn_drop_rate=0.0,
+        drop_path_rate=0.0,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6),
+        init_values=None,
+        use_abs_pos_emb=True,
+        use_rel_pos_bias=False,
+        use_shared_rel_pos_bias=False,
+        head_init_scale=0.001,
+    ):
+        super().__init__()
+        self.num_classes = num_classes
+        self.global_pool = global_pool
+        self.num_features = (
+            self.embed_dim
+        ) = embed_dim  # num_features for consistency with other models
+        self.grad_checkpointing = False
+
+        self.patch_embed = PatchEmbed(
+            img_size=img_size,
+            patch_size=patch_size,
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+        )
+        num_patches = self.patch_embed.num_patches
+
+        self.cls_token = nn.Parameter(flow.zeros(1, 1, embed_dim))
+        # self.mask_token = nn.Parameter(flow.zeros(1, 1, embed_dim))
+        self.pos_embed = (
+            nn.Parameter(flow.zeros(1, num_patches + 1, embed_dim))
+            if use_abs_pos_emb
+            else None
+        )
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        if use_shared_rel_pos_bias:
+            self.rel_pos_bias = RelativePositionBias(
+                window_size=self.patch_embed.grid_size, num_heads=num_heads
+            )
+        else:
+            self.rel_pos_bias = None
+
+        dpr = [
+            x.item() for x in flow.linspace(0, drop_path_rate, depth)
+        ]  # stochastic depth decay rule
+        self.blocks = nn.ModuleList(
+            [
+                Block(
+                    dim=embed_dim,
+                    num_heads=num_heads,
+                    mlp_ratio=mlp_ratio,
+                    qkv_bias=qkv_bias,
+                    drop=drop_rate,
+                    attn_drop=attn_drop_rate,
+                    drop_path=dpr[i],
+                    norm_layer=norm_layer,
+                    init_values=init_values,
+                    window_size=self.patch_embed.grid_size
+                    if use_rel_pos_bias
+                    else None,
+                )
+                for i in range(depth)
+            ]
+        )
+        use_fc_norm = self.global_pool == "avg"
+        self.norm = nn.Identity() if use_fc_norm else norm_layer(embed_dim)
+        self.fc_norm = norm_layer(embed_dim) if use_fc_norm else None
+        self.head = (
+            nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+        )
+
+        self.apply(self._init_weights)
+        if self.pos_embed is not None:
+            trunc_normal_(self.pos_embed, std=0.02)
+        trunc_normal_(self.cls_token, std=0.02)
+        # trunc_normal_(self.mask_token, std=.02)
+        self.fix_init_weight()
+        if isinstance(self.head, nn.Linear):
+            trunc_normal_(self.head.weight, std=0.02)
+            self.head.weight.data.mul_(head_init_scale)
+            self.head.bias.data.mul_(head_init_scale)
+
+    def fix_init_weight(self):
+        def rescale(param, layer_id):
+            param.div_(math.sqrt(2.0 * layer_id))
+
+        for layer_id, layer in enumerate(self.blocks):
+            rescale(layer.attn.proj.weight.data, layer_id + 1)
+            rescale(layer.mlp.fc2.weight.data, layer_id + 1)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=0.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    def no_weight_decay(self):
+        nwd = {"pos_embed", "cls_token"}
+        for n, _ in self.named_parameters():
+            if "relative_position_bias_table" in n:
+                nwd.add(n)
+        return nwd
+
+    def set_grad_checkpointing(self, enable=True):
+        self.grad_checkpointing = enable
+
+    def group_matcher(self, coarse=False):
+        matcher = dict(
+            stem=r"^cls_token|pos_embed|patch_embed|rel_pos_bias",  # stem and embed
+            blocks=[(r"^blocks\.(\d+)", None), (r"^norm", (99999,))],
+        )
+        return matcher
+
+    def get_classifier(self):
+        return self.head
+
+    def reset_classifier(self, num_classes, global_pool=None):
+        self.num_classes = num_classes
+        if global_pool is not None:
+            self.global_pool = global_pool
+        self.head = (
+            nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+        )
+
+    def forward_features(self, x):
+        x = self.patch_embed(x)
+        x = flow.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
+        if self.pos_embed is not None:
+            x = x + self.pos_embed
+        x = self.pos_drop(x)
+
+        rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None
+        for blk in self.blocks:
+            x = blk(x, shared_rel_pos_bias=rel_pos_bias)
+        x = self.norm(x)
+        return x
+
+    def forward_head(self, x, pre_logits: bool = False):
+        if self.fc_norm is not None:
+            x = x[:, 1:].mean(dim=1)
+            x = self.fc_norm(x)
+        else:
+            x = x[:, 0]
+        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
+
+
+@ModelCreator.register_model
+def beit_base_patch16_224(pretrained=False, progress=True, **kwargs):
+    model_kwargs = dict(
+        patch_size=16,
+        embed_dim=768,
+        depth=12,
+        num_heads=12,
+        mlp_ratio=4,
+        use_abs_pos_emb=False,
+        use_rel_pos_bias=True,
+        init_values=0.1,
+        **kwargs
+    )
+    model = Beit(**model_kwargs)
+    if pretrained:
+        state_dict = load_state_dict_from_url(
+            model_urls["beit_base_patch16_224"],
+            model_dir="./checkpoints",
+            progress=progress,
+        )
+        model.load_state_dict(state_dict)
+    return model
+
+
+@ModelCreator.register_model
+def beit_base_patch16_384(pretrained=False, progress=True, **kwargs):
+    model_kwargs = dict(
+        img_size=384,
+        patch_size=16,
+        embed_dim=768,
+        depth=12,
+        num_heads=12,
+        mlp_ratio=4,
+        use_abs_pos_emb=False,
+        use_rel_pos_bias=True,
+        init_values=0.1,
+        **kwargs
+    )
+    model = Beit(**model_kwargs)
+    if pretrained:
+        state_dict = load_state_dict_from_url(
+            model_urls["beit_base_patch16_384"],
+            model_dir="./checkpoints",
+            progress=progress,
+        )
+        model.load_state_dict(state_dict)
+    return model
+
+@ModelCreator.register_model
+def beit_large_patch16_224(pretrained=False, progress=True, **kwargs):
+    model_kwargs = dict(
+        patch_size=16,
+        embed_dim=1024,
+        depth=24,
+        num_heads=16,
+        mlp_ratio=4,
+        qkv_bias=True,
+        use_abs_pos_emb=False,
+        use_rel_pos_bias=True,
+        init_values=1e-5,
+        **kwargs
+    )
+    model = Beit(**model_kwargs)
+    if pretrained:
+        state_dict = load_state_dict_from_url(
+            model_urls["beit_large_patch16_224"],
+            model_dir="./checkpoints",
+            progress=progress,
+        )
+        model.load_state_dict(state_dict)
+    return model
+
+
+@ModelCreator.register_model
+def beit_large_patch16_384(pretrained=False, progress=True, **kwargs):
+    model_kwargs = dict(
+        img_size=384,
+        patch_size=16,
+        embed_dim=1024,
+        depth=24,
+        num_heads=16,
+        mlp_ratio=4,
+        qkv_bias=True,
+        use_abs_pos_emb=False,
+        use_rel_pos_bias=True,
+        init_values=1e-5,
+        **kwargs
+    )
+    model = Beit(**model_kwargs)
+    if pretrained:
+        state_dict = load_state_dict_from_url(
+            model_urls["beit_large_patch16_384"],
+            model_dir="./checkpoints",
+            progress=progress,
+        )
+        model.load_state_dict(state_dict)
+    return model
+
+
+@ModelCreator.register_model
+def beit_large_patch16_512(pretrained=False, progress=True, **kwargs):
+    model_kwargs = dict(
+        img_size=512,
+        patch_size=16,
+        embed_dim=1024,
+        depth=24,
+        num_heads=16,
+        mlp_ratio=4,
+        qkv_bias=True,
+        use_abs_pos_emb=False,
+        use_rel_pos_bias=True,
+        init_values=1e-5,
+        **kwargs
+    )
+    model = Beit(**model_kwargs)
+    if pretrained:
+        state_dict = load_state_dict_from_url(
+            model_urls["beit_large_patch16_512"],
+            model_dir="./checkpoints",
+            progress=progress,
+        )
+        model.load_state_dict(state_dict)
+    return model
+