diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -1,2 +1,78 @@
-# retinaface
+# Retinaface Face Detection (Pytorch)
 
+*Authors: David Wang*
+
+## Desription
+
+This opertator detects faces in the images by using RetinaFace Detector[1]. It will returns the bounding box positions and the confidence scores of detected faces. This repo is a adopataion from [2].
+
+
+```python
+from towhee import ops
+
+model = ops.face_detection.retinaface()
+embedding = model(img)
+```
+
+## Factory Constructor
+
+Create the operator via the following factory method
+
+***ops.face_detection.retinaface()***
+
+
+## Interface
+
+A face detection operator takes an image as input. it generates the bounding box position and confidence score back to ndarray.
+
+**Args:**
+
+​	***framework***
+
+​  the framework of the model
+
+​  supported types: `str`, default is 'pytorch'
+
+**Parameters:**
+
+​	***image***
+
+​	the image to detect faces.
+
+​	supported types: numpy.ndarray
+
+**Returns:**: *numpy.ndarray*
+
+​	The detected face bounding boxes.
+
+## Code Example
+
+get detected face bounding boxes from './img1.jpg'. 
+
+ *Write the pipeline in simplified style*:
+
+```python
+import towhee.DataCollection as dc
+
+dc.glob('./img1.jpg')
+  .image_decode.cv2()
+  .face_detection.retinaface()
+  .to_list()
+```
+
+*Write a same pipeline with explicit inputs/outputs name specifications:*
+
+```python
+import towhee.DataCollection as dc
+
+dc.glob['path']('./img1.jpg')
+  .image_decode.cv2['path', 'img']()
+  .face_detection.retinaface()
+  .to_list()
+```
+
+
+## Reference
+
+[1]. https://arxiv.org/abs/1905.00641  
+[2]. https://github.com/biubug6/Pytorch_Retinaface
diff --git a/__init__.py b/__init__.py
new file mode 100644
index 0000000..98ce600
--- /dev/null
+++ b/__init__.py
@@ -0,0 +1,19 @@
+# Copyright 2021 Zilliz. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .retinaface import Retinaface
+
+def retinaface():
+    return Retinaface()
+
diff --git a/pytorch_retinaface_mobilenet_widerface.pth b/pytorch_retinaface_mobilenet_widerface.pth
new file mode 100644
index 0000000..287f82f
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+++ b/pytorch_retinaface_mobilenet_widerface.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4b1e031a840c18c24b3f5229c2971285eb4f897cf05f6568f955ae1110b60c88
+size 1821417
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..08ed5ee
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1 @@
+torch
\ No newline at end of file
diff --git a/retinaface.py b/retinaface.py
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--- /dev/null
+++ b/retinaface.py
@@ -0,0 +1,48 @@
+# Copyright 2022 Zilliz. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import sys
+import os
+from typing import NamedTuple, List
+
+from PIL import Image
+import torch
+from torchvision import transforms
+from pathlib import Path
+import numpy
+
+import towhee
+from towhee.operator import Operator
+from towhee.types.image_utils import to_pil
+from towhee._types import Image
+
+from timm.data import resolve_data_config
+from timm.data.transforms_factory import create_transform
+
+@register(output_schema=['bboxes', 'scores'])
+class Retinaface(Operator):
+    """
+    Retinaface
+    """
+    def __init__(self, framework: str = 'pytorch') -> None:
+        super().__init__()
+        sys.path.append(str(Path(__file__).parent))
+        from retinaface_impl import Model
+        self.model = Model()
+
+    @arg(1, to_image_color('RGB') )
+    def __call__(self, image: 'towhee._types.Image'):
+        img = torch.FloatTensor(numpy.asarray(to_pil(image)))
+        bboxes, keypoints = self.model(img)
+        return bboxes[:,:4],  bboxes[:,4]
diff --git a/retinaface_impl.py b/retinaface_impl.py
new file mode 100644
index 0000000..4921173
--- /dev/null
+++ b/retinaface_impl.py
@@ -0,0 +1,41 @@
+# Copyright 2021 Zilliz. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+
+import torch
+
+from towhee.models.retina_face.retinaface import RetinaFace
+from towhee.models.retina_face.configs import build_configs
+from towhee.models.utils.pretrained_utils import load_pretrained_weights
+
+class Model:
+    """
+    Pytorch model class
+    """
+    def __init__(self):
+        model_name = 'cfg_mnet'
+        cfg = build_configs(model_name)
+        self._model = RetinaFace(cfg=cfg, phase='test')
+        load_pretrained_weights(self._model, 'mnet', None, os.path.dirname(__file__) + '/pytorch_retinaface_mobilenet_widerface.pth')
+        self._model.eval()
+
+    def __call__(self, img_tensor: torch.Tensor):
+        outputs = self._model.inference(img_tensor)
+        return outputs
+
+    def train(self):
+        """
+        For training model
+        """
+        pass