diff --git a/modules/image/semantic_segmentation/ExtremeC3_Portrait_Segmentation/README.md b/modules/image/semantic_segmentation/ExtremeC3_Portrait_Segmentation/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..4e91fcfe5278a73b6b93938f649f1b88154b5f6c
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+++ b/modules/image/semantic_segmentation/ExtremeC3_Portrait_Segmentation/README.md
@@ -0,0 +1,56 @@
+## 概述
+* 基于 ExtremeC3 模型实现的轻量化人像分割模型
+* 模型具体规格如下：
+    |model|ExtremeC3|
+    |----|----|
+    |Param|0.038 M|
+    |Flop|0.128 G|
+
+* 模型参数转换至 [ext_portrait_segmentation](https://github.com/clovaai/ext_portrait_segmentation) 项目
+* 感谢 [ext_portrait_segmentation](https://github.com/clovaai/ext_portrait_segmentation) 项目提供的开源代码和模型
+
+## 效果展示
+![](https://ai-studio-static-online.cdn.bcebos.com/1261398a98e24184852bdaff5a4e1dbd7739430f59fb47e8b84e3a2cfb976107)
+
+## API
+```python
+def Segmentation(
+    images=None,
+    paths=None,
+    batch_size=1,
+    output_dir='output',
+    visualization=False):
+```
+人像分割 API
+
+**参数**
+* images (list[np.ndarray]) : 输入图像数据列表（BGR）
+* paths (list[str]) : 输入图像路径列表
+* batch_size (int) : 数据批大小
+* output_dir (str) : 可视化图像输出目录
+* visualization (bool) : 是否可视化
+
+**返回**
+* results (list[dict{"mask":np.ndarray,"result":np.ndarray}]): 输出图像数据列表
+
+**代码示例**
+```python
+import cv2
+import paddlehub as hub
+
+model = hub.Module(name='ExtremeC3_Portrait_Segmentation')
+
+result = model.Segmentation(
+    images=[cv2.imread('/PATH/TO/IMAGE')],
+    paths=None,
+    batch_size=1,
+    output_dir='output',
+    visualization=False)
+```
+
+## 查看代码
+https://github.com/clovaai/ext_portrait_segmentation
+
+## 依赖
+paddlepaddle >= 2.0.0rc0  
+paddlehub >= 2.0.0b1
diff --git a/modules/image/semantic_segmentation/ExtremeC3_Portrait_Segmentation/module.py b/modules/image/semantic_segmentation/ExtremeC3_Portrait_Segmentation/module.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa1344ddb036a3a78b5ef1a9942a5a26c7cf3c16
--- /dev/null
+++ b/modules/image/semantic_segmentation/ExtremeC3_Portrait_Segmentation/module.py
@@ -0,0 +1,137 @@
+import os
+import cv2
+import paddle
+import numpy as np
+
+from paddle.nn import Layer
+from paddlehub.module.module import moduleinfo
+
+@moduleinfo(
+    name="ExtremeC3_Portrait_Segmentation",  # 模型名称
+    type="CV/semantic_segmentation",  # 模型类型
+    author="jm12138",  # 作者名称
+    author_email="jm12138@qq.com",  # 作者邮箱
+    summary="ExtremeC3_Portrait_Segmentation",  # 模型介绍
+    version="1.0.0"  # 版本号
+)
+class ExtremeC3_Portrait_Segmentation(Layer):
+    # 初始化函数
+    def __init__(self, name=None, directory=None):
+        super(ExtremeC3_Portrait_Segmentation, self).__init__()
+        # 设置模型路径
+        self.model_path = os.path.join(self.directory, "ExtremeC3")
+
+        # 加载模型
+        self.model = paddle.jit.load(self.model_path)
+        self.model.eval()
+
+        # 均值方差
+        self.mean = [107.304565, 115.69884, 132.35703 ]
+        self.std = [63.97182, 65.1337, 68.29726]
+
+    # 读取数据函数
+    @staticmethod
+    def load_datas(paths, images):
+        datas = []
+
+        # 读取数据列表
+        if paths is not None:
+            for im_path in paths:
+                assert os.path.isfile(im_path), "The {} isn't a valid file path.".format(im_path)
+                im = cv2.imread(im_path)
+                datas.append(im)
+
+        if images is not None:
+            datas = images
+
+        # 返回数据列表
+        return datas
+
+    # 预处理函数
+    def preprocess(self, datas, batch_size):
+        input_datas = []
+        for img in datas:
+            # 缩放
+            h, w = img.shape[:2]
+            img = cv2.resize(img, (224, 224))
+
+            # 格式转换
+            img = img.astype(np.float32)
+
+            # 归一化
+            for j in range(3):
+                img[:, :, j] -= self.mean[j]
+            for j in range(3):
+                img[:, :, j] /= self.std[j]
+            img /= 255.
+
+            # 格式转换
+            img = img.transpose((2, 0, 1))
+            img = img[np.newaxis, ...]
+
+            input_datas.append(img)
+
+        # 数据切分
+        input_datas = np.concatenate(input_datas, 0)
+        split_num = len(datas) // batch_size + 1 if len(datas) % batch_size != 0 else len(datas) // batch_size
+        input_datas = np.array_split(input_datas, split_num)
+        return input_datas
+
+    # 后处理函数
+    def postprocess(self, outputs, datas, output_dir, visualization):
+        # 检查输出目录
+        if visualization:
+            if not os.path.exists(output_dir):
+                os.mkdir(output_dir)
+
+        # 拼接输出
+        outputs = paddle.concat(outputs)
+
+        results = []
+        for output, img, i in zip(outputs, datas, range(len(datas))):
+            # 计算MASK
+            mask = (output[0] > 0).numpy().astype('float32')
+
+            # 缩放
+            h, w = img.shape[:2]
+            mask = cv2.resize(mask, (w, h))
+
+            # 计算输出图像
+            result = (img * mask[..., np.newaxis] + (1 - mask[..., np.newaxis]) * 255).astype(np.uint8)
+
+            # 格式还原
+            mask = (mask * 255).astype(np.uint8)
+
+            # 可视化
+            if visualization:
+                cv2.imwrite(os.path.join(output_dir, 'result_mask_%d.png' % i), mask)
+                cv2.imwrite(os.path.join(output_dir, 'result_%d.png' % i), result)
+
+            results.append({
+                'mask': mask,
+                'result': result
+            })
+
+        return results
+
+    # 关键点检测函数
+    def Segmentation(self,
+                       images=None,
+                       paths=None,
+                       batch_size=1,
+                       output_dir='output',
+                       visualization=False):
+        # 加载数据处理器
+        datas = self.load_datas(paths, images)
+
+        # 获取输入数据
+        input_datas = self.preprocess(datas, batch_size)
+
+        # 模型预测
+        outputs = [self.model(paddle.to_tensor(input_data)) for input_data in input_datas]
+
+        # 结果后处理
+        results = self.postprocess(outputs, datas, output_dir, visualization)
+
+        # 返回结果
+        return results
\ No newline at end of file
diff --git a/modules/image/semantic_segmentation/SINet_Portrait_Segmentation/README.md b/modules/image/semantic_segmentation/SINet_Portrait_Segmentation/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..378319e49ab85cc2880476c1a56bd3ac763c0771
--- /dev/null
+++ b/modules/image/semantic_segmentation/SINet_Portrait_Segmentation/README.md
@@ -0,0 +1,56 @@
+## 概述
+* 基于 SINet 模型实现的轻量化人像分割模型
+* 模型具体规格如下：
+    |model|SINet|
+    |----|----|
+    |Param|0.087 M|
+    |Flop|0.064 G|
+
+* 模型参数转换至 [ext_portrait_segmentation](https://github.com/clovaai/ext_portrait_segmentation) 项目
+* 感谢 [ext_portrait_segmentation](https://github.com/clovaai/ext_portrait_segmentation) 项目提供的开源代码和模型
+
+## 效果展示
+![](https://ai-studio-static-online.cdn.bcebos.com/264c689d024942f3817bc9b290dea18812ba88e43d89457e977cd811988f0b44)
+
+## API
+```python
+def Segmentation(
+    images=None,
+    paths=None,
+    batch_size=1,
+    output_dir='output',
+    visualization=False):
+```
+人像分割 API
+
+**参数**
+* images (list[np.ndarray]) : 输入图像数据列表（BGR）
+* paths (list[str]) : 输入图像路径列表
+* batch_size (int) : 数据批大小
+* output_dir (str) : 可视化图像输出目录
+* visualization (bool) : 是否可视化
+
+**返回**
+* results (list[dict{"mask":np.ndarray,"result":np.ndarray}]): 输出图像数据列表
+
+**代码示例**
+```python
+import cv2
+import paddlehub as hub
+
+model = hub.Module(name='SINet_Portrait_Segmentation')
+
+result = model.Segmentation(
+    images=[cv2.imread('/PATH/TO/IMAGE')],
+    paths=None,
+    batch_size=1,
+    output_dir='output',
+    visualization=False)
+```
+
+## 查看代码
+https://github.com/clovaai/ext_portrait_segmentation
+
+## 依赖
+paddlepaddle >= 2.0.0rc0  
+paddlehub >= 2.0.0b1
diff --git a/modules/image/semantic_segmentation/SINet_Portrait_Segmentation/module.py b/modules/image/semantic_segmentation/SINet_Portrait_Segmentation/module.py
new file mode 100644
index 0000000000000000000000000000000000000000..5bd18353b75fb6c61e0b379072d30074c20b0761
--- /dev/null
+++ b/modules/image/semantic_segmentation/SINet_Portrait_Segmentation/module.py
@@ -0,0 +1,137 @@
+import os
+import cv2
+import paddle
+import numpy as np
+
+from paddle.nn import Layer
+from paddlehub.module.module import moduleinfo
+
+@moduleinfo(
+    name="SINet_Portrait_Segmentation",  # 模型名称
+    type="CV/semantic_segmentation",  # 模型类型
+    author="jm12138",  # 作者名称
+    author_email="jm12138@qq.com",  # 作者邮箱
+    summary="SINet_Portrait_Segmentation",  # 模型介绍
+    version="1.0.0"  # 版本号
+)
+class SINet_Portrait_Segmentation(Layer):
+    # 初始化函数
+    def __init__(self, name=None, directory=None):
+        super(SINet_Portrait_Segmentation, self).__init__()
+        # 设置模型路径
+        self.model_path = os.path.join(self.directory, "SINet")
+
+        # 加载模型
+        self.model = paddle.jit.load(self.model_path)
+        self.model.eval()
+
+        # 均值方差
+        self.mean = [107.304565, 115.69884, 132.35703 ]
+        self.std = [63.97182, 65.1337, 68.29726]
+
+    # 读取数据函数
+    @staticmethod
+    def load_datas(paths, images):
+        datas = []
+
+        # 读取数据列表
+        if paths is not None:
+            for im_path in paths:
+                assert os.path.isfile(im_path), "The {} isn't a valid file path.".format(im_path)
+                im = cv2.imread(im_path)
+                datas.append(im)
+
+        if images is not None:
+            datas = images
+
+        # 返回数据列表
+        return datas
+
+    # 预处理函数
+    def preprocess(self, datas, batch_size):
+        input_datas = []
+        for img in datas:
+            # 缩放
+            h, w = img.shape[:2]
+            img = cv2.resize(img, (224, 224))
+
+            # 格式转换
+            img = img.astype(np.float32)
+
+            # 归一化
+            for j in range(3):
+                img[:, :, j] -= self.mean[j]
+            for j in range(3):
+                img[:, :, j] /= self.std[j]
+            img /= 255.
+
+            # 格式转换
+            img = img.transpose((2, 0, 1))
+            img = img[np.newaxis, ...]
+
+            input_datas.append(img)
+
+        # 数据切分
+        input_datas = np.concatenate(input_datas, 0)
+        split_num = len(datas) // batch_size + 1 if len(datas) % batch_size != 0 else len(datas) // batch_size
+        input_datas = np.array_split(input_datas, split_num)
+        return input_datas
+
+    # 后处理函数
+    def postprocess(self, outputs, datas, output_dir, visualization):
+        # 检查输出目录
+        if visualization:
+            if not os.path.exists(output_dir):
+                os.mkdir(output_dir)
+
+        # 拼接输出
+        outputs = paddle.concat(outputs)
+
+        results = []
+        for output, img, i in zip(outputs, datas, range(len(datas))):
+            # 计算MASK
+            mask = 1 - (output[0] > 0).numpy().astype('float32')
+
+            # 缩放
+            h, w = img.shape[:2]
+            mask = cv2.resize(mask, (w, h))
+
+            # 计算输出图像
+            result = (img * mask[..., np.newaxis] + (1 - mask[..., np.newaxis]) * 255).astype(np.uint8)
+
+            # 格式还原
+            mask = (mask * 255).astype(np.uint8)
+
+            # 可视化
+            if visualization:
+                cv2.imwrite(os.path.join(output_dir, 'result_mask_%d.png' % i), mask)
+                cv2.imwrite(os.path.join(output_dir, 'result_%d.png' % i), result)
+
+            results.append({
+                'mask': mask,
+                'result': result
+            })
+
+        return results
+
+    # 关键点检测函数
+    def Segmentation(self,
+                       images=None,
+                       paths=None,
+                       batch_size=1,
+                       output_dir='output',
+                       visualization=False):
+        # 加载数据处理器
+        datas = self.load_datas(paths, images)
+
+        # 获取输入数据
+        input_datas = self.preprocess(datas, batch_size)
+
+        # 模型预测
+        outputs = [self.model(paddle.to_tensor(input_data)) for input_data in input_datas]
+
+        # 结果后处理
+        results = self.postprocess(outputs, datas, output_dir, visualization)
+
+        # 返回结果
+        return results