diff --git a/.gitignore b/.gitignore
index d32eacc..758b312 100644
--- a/.gitignore
+++ b/.gitignore
@@ -10,4 +10,5 @@ logs/
 tools/
 .vscode/
 convert_*
-*.pt
\ No newline at end of file
+*.pt
+*.pth
\ No newline at end of file
diff --git a/lynx/face/arcface.py b/lynx/face/arcface.py
new file mode 100644
index 0000000..750f82a
--- /dev/null
+++ b/lynx/face/arcface.py
@@ -0,0 +1,239 @@
+import torch
+from collections import namedtuple
+from torch.nn import (AdaptiveAvgPool2d, BatchNorm1d, BatchNorm2d, Conv2d, Dropout, Linear, MaxPool2d, Module, PReLU,
+                      ReLU, Sequential, Sigmoid)
+
+# Original Arcface Model
+# https://github.com/xinntao/facexlib/blob/master/facexlib/recognition/arcface_arch.py
+
+
+class Flatten(Module):
+
+    def forward(self, input):
+        return input.view(input.size(0), -1)
+
+
+def l2_norm(input, axis=1):
+    norm = torch.norm(input, 2, axis, True)
+    output = torch.div(input, norm)
+    return output
+
+
+class SEModule(Module):
+
+    def __init__(self, channels, reduction):
+        super(SEModule, self).__init__()
+        self.avg_pool = AdaptiveAvgPool2d(1)
+        self.fc1 = Conv2d(channels, channels // reduction, kernel_size=1, padding=0, bias=False)
+        self.relu = ReLU(inplace=True)
+        self.fc2 = Conv2d(channels // reduction, channels, kernel_size=1, padding=0, bias=False)
+        self.sigmoid = Sigmoid()
+
+    def forward(self, x):
+        module_input = x
+        x = self.avg_pool(x)
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        x = self.sigmoid(x)
+        return module_input * x
+
+
+class bottleneck_IR(Module):
+
+    def __init__(self, in_channel, depth, stride):
+        super(bottleneck_IR, self).__init__()
+        if in_channel == depth:
+            self.shortcut_layer = MaxPool2d(1, stride)
+        else:
+            self.shortcut_layer = Sequential(Conv2d(in_channel, depth, (1, 1), stride, bias=False), BatchNorm2d(depth))
+        self.res_layer = Sequential(
+            BatchNorm2d(in_channel), Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), PReLU(depth),
+            Conv2d(depth, depth, (3, 3), stride, 1, bias=False), BatchNorm2d(depth))
+
+    def forward(self, x):
+        shortcut = self.shortcut_layer(x)
+        res = self.res_layer(x)
+        return res + shortcut
+
+
+class bottleneck_IR_SE(Module):
+
+    def __init__(self, in_channel, depth, stride):
+        super(bottleneck_IR_SE, self).__init__()
+        if in_channel == depth:
+            self.shortcut_layer = MaxPool2d(1, stride)
+        else:
+            self.shortcut_layer = Sequential(Conv2d(in_channel, depth, (1, 1), stride, bias=False), BatchNorm2d(depth))
+        self.res_layer = Sequential(
+            BatchNorm2d(in_channel), Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), PReLU(depth),
+            Conv2d(depth, depth, (3, 3), stride, 1, bias=False), BatchNorm2d(depth), SEModule(depth, 16))
+
+    def forward(self, x):
+        shortcut = self.shortcut_layer(x)
+        res = self.res_layer(x)
+        return res + shortcut
+
+
+class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])):
+    '''A named tuple describing a ResNet block.'''
+
+
+def get_block(in_channel, depth, num_units, stride=2):
+    return [Bottleneck(in_channel, depth, stride)] + [Bottleneck(depth, depth, 1) for i in range(num_units - 1)]
+
+
+def get_blocks(num_layers):
+    if num_layers == 50:
+        blocks = [
+            get_block(in_channel=64, depth=64, num_units=3),
+            get_block(in_channel=64, depth=128, num_units=4),
+            get_block(in_channel=128, depth=256, num_units=14),
+            get_block(in_channel=256, depth=512, num_units=3)
+        ]
+    elif num_layers == 100:
+        blocks = [
+            get_block(in_channel=64, depth=64, num_units=3),
+            get_block(in_channel=64, depth=128, num_units=13),
+            get_block(in_channel=128, depth=256, num_units=30),
+            get_block(in_channel=256, depth=512, num_units=3)
+        ]
+    elif num_layers == 152:
+        blocks = [
+            get_block(in_channel=64, depth=64, num_units=3),
+            get_block(in_channel=64, depth=128, num_units=8),
+            get_block(in_channel=128, depth=256, num_units=36),
+            get_block(in_channel=256, depth=512, num_units=3)
+        ]
+    return blocks
+
+
+class Backbone(Module):
+
+    def __init__(self, num_layers, drop_ratio, mode='ir'):
+        super(Backbone, self).__init__()
+        assert num_layers in [50, 100, 152], 'num_layers should be 50,100, or 152'
+        assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se'
+        blocks = get_blocks(num_layers)
+        if mode == 'ir':
+            unit_module = bottleneck_IR
+        elif mode == 'ir_se':
+            unit_module = bottleneck_IR_SE
+        self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False), BatchNorm2d(64), PReLU(64))
+        self.output_layer = Sequential(
+            BatchNorm2d(512), Dropout(drop_ratio), Flatten(), Linear(512 * 7 * 7, 512), BatchNorm1d(512))
+        modules = []
+        for block in blocks:
+            for bottleneck in block:
+                modules.append(unit_module(bottleneck.in_channel, bottleneck.depth, bottleneck.stride))
+        self.body = Sequential(*modules)
+
+    def forward(self, x):
+        x = self.input_layer(x)
+        x = self.body(x)
+        x = self.output_layer(x)
+        return l2_norm(x)
+
+
+# MobileFaceNet
+
+
+class Conv_block(Module):
+
+    def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1):
+        super(Conv_block, self).__init__()
+        self.conv = Conv2d(
+            in_c, out_channels=out_c, kernel_size=kernel, groups=groups, stride=stride, padding=padding, bias=False)
+        self.bn = BatchNorm2d(out_c)
+        self.prelu = PReLU(out_c)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.prelu(x)
+        return x
+
+
+class Linear_block(Module):
+
+    def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1):
+        super(Linear_block, self).__init__()
+        self.conv = Conv2d(
+            in_c, out_channels=out_c, kernel_size=kernel, groups=groups, stride=stride, padding=padding, bias=False)
+        self.bn = BatchNorm2d(out_c)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return x
+
+
+class Depth_Wise(Module):
+
+    def __init__(self, in_c, out_c, residual=False, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=1):
+        super(Depth_Wise, self).__init__()
+        self.conv = Conv_block(in_c, out_c=groups, kernel=(1, 1), padding=(0, 0), stride=(1, 1))
+        self.conv_dw = Conv_block(groups, groups, groups=groups, kernel=kernel, padding=padding, stride=stride)
+        self.project = Linear_block(groups, out_c, kernel=(1, 1), padding=(0, 0), stride=(1, 1))
+        self.residual = residual
+
+    def forward(self, x):
+        if self.residual:
+            short_cut = x
+        x = self.conv(x)
+        x = self.conv_dw(x)
+        x = self.project(x)
+        if self.residual:
+            output = short_cut + x
+        else:
+            output = x
+        return output
+
+
+class Residual(Module):
+
+    def __init__(self, c, num_block, groups, kernel=(3, 3), stride=(1, 1), padding=(1, 1)):
+        super(Residual, self).__init__()
+        modules = []
+        for _ in range(num_block):
+            modules.append(
+                Depth_Wise(c, c, residual=True, kernel=kernel, padding=padding, stride=stride, groups=groups))
+        self.model = Sequential(*modules)
+
+    def forward(self, x):
+        return self.model(x)
+
+
+class MobileFaceNet(Module):
+
+    def __init__(self, embedding_size):
+        super(MobileFaceNet, self).__init__()
+        self.conv1 = Conv_block(3, 64, kernel=(3, 3), stride=(2, 2), padding=(1, 1))
+        self.conv2_dw = Conv_block(64, 64, kernel=(3, 3), stride=(1, 1), padding=(1, 1), groups=64)
+        self.conv_23 = Depth_Wise(64, 64, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=128)
+        self.conv_3 = Residual(64, num_block=4, groups=128, kernel=(3, 3), stride=(1, 1), padding=(1, 1))
+        self.conv_34 = Depth_Wise(64, 128, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=256)
+        self.conv_4 = Residual(128, num_block=6, groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1))
+        self.conv_45 = Depth_Wise(128, 128, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=512)
+        self.conv_5 = Residual(128, num_block=2, groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1))
+        self.conv_6_sep = Conv_block(128, 512, kernel=(1, 1), stride=(1, 1), padding=(0, 0))
+        self.conv_6_dw = Linear_block(512, 512, groups=512, kernel=(7, 7), stride=(1, 1), padding=(0, 0))
+        self.conv_6_flatten = Flatten()
+        self.linear = Linear(512, embedding_size, bias=False)
+        self.bn = BatchNorm1d(embedding_size)
+
+    def forward(self, x):
+        out = self.conv1(x)
+        out = self.conv2_dw(out)
+        out = self.conv_23(out)
+        out = self.conv_3(out)
+        out = self.conv_34(out)
+        out = self.conv_4(out)
+        out = self.conv_45(out)
+        out = self.conv_5(out)
+        out = self.conv_6_sep(out)
+        out = self.conv_6_dw(out)
+        out = self.conv_6_flatten(out)
+        out = self.linear(out)
+        out = self.bn(out)
+        return l2_norm(out)
diff --git a/lynx/face/face_encoder.py b/lynx/face/face_encoder.py
index f0e0434..60ee736 100644
--- a/lynx/face/face_encoder.py
+++ b/lynx/face/face_encoder.py
@@ -3,6 +3,12 @@
 
 import torch
 import numpy as np
+from .arcface import Backbone
+from torch.hub import download_url_to_file, get_dir
+from urllib.parse import urlparse
+import os
+
+ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
 
 __all__ = [
     "FaceEncoderArcFace",
@@ -43,8 +49,27 @@ def get_landmarks_from_image(image):
 
     return keypoints
 
-from facexlib.utils import load_file_from_url
-from facexlib.recognition.arcface_arch import Backbone
+
+def load_file_from_url(url, model_dir=None, progress=True, file_name=None, save_dir=None):
+    """Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py
+    """
+    if model_dir is None:
+        hub_dir = get_dir()
+        model_dir = os.path.join(hub_dir, 'checkpoints')
+
+    if save_dir is None:
+        save_dir = os.path.join(ROOT_DIR, model_dir)
+    os.makedirs(save_dir, exist_ok=True)
+
+    parts = urlparse(url)
+    filename = os.path.basename(parts.path)
+    if file_name is not None:
+        filename = file_name
+    cached_file = os.path.abspath(os.path.join(save_dir, filename))
+    if not os.path.exists(cached_file):
+        print(f'Downloading: "{url}" to {cached_file}\n')
+        download_url_to_file(url, cached_file, hash_prefix=None, progress=progress)
+    return cached_file
 
 def init_recognition_model(model_name, half=False, device='cuda', model_rootpath=None):
     print("Initializing recognition model:", model_name)
diff --git a/lynx/requirements.txt b/lynx/requirements.txt
index 5e76ee6..7e383cc 100644
--- a/lynx/requirements.txt
+++ b/lynx/requirements.txt
@@ -1,2 +1 @@
-facexlib
 insightface
\ No newline at end of file