diff --git a/mindspore-jina/Dockerfile b/mindspore-jina/MindsporeLeNet/Dockerfile
similarity index 100%
rename from mindspore-jina/Dockerfile
rename to mindspore-jina/MindsporeLeNet/Dockerfile
diff --git a/mindspore-jina/config.yml b/mindspore-jina/MindsporeLeNet/config.yml
similarity index 100%
rename from mindspore-jina/config.yml
rename to mindspore-jina/MindsporeLeNet/config.yml
diff --git a/mindspore-jina/lenet/.keep b/mindspore-jina/MindsporeLeNet/lenet/.keep
similarity index 100%
rename from mindspore-jina/lenet/.keep
rename to mindspore-jina/MindsporeLeNet/lenet/.keep
diff --git a/mindspore-jina/manifest.yml b/mindspore-jina/MindsporeLeNet/manifest.yml
similarity index 100%
rename from mindspore-jina/manifest.yml
rename to mindspore-jina/MindsporeLeNet/manifest.yml
diff --git a/mindspore-jina/MindsporeLeNet/requirements.txt b/mindspore-jina/MindsporeLeNet/requirements.txt
new file mode 100644
index 0000000..734db7b
--- /dev/null
+++ b/mindspore-jina/MindsporeLeNet/requirements.txt
@@ -0,0 +1 @@
+jina
diff --git a/mindspore-jina/tests/__init__.py b/mindspore-jina/MindsporeLeNet/tests/__init__.py
similarity index 100%
rename from mindspore-jina/tests/__init__.py
rename to mindspore-jina/MindsporeLeNet/tests/__init__.py
diff --git a/mindspore-jina/tests/test_mindsporelenet.py b/mindspore-jina/MindsporeLeNet/tests/test_mindsporelenet.py
similarity index 100%
rename from mindspore-jina/tests/test_mindsporelenet.py
rename to mindspore-jina/MindsporeLeNet/tests/test_mindsporelenet.py
diff --git a/mindspore-jina/__init__.py b/mindspore-jina/__init__.py
deleted file mode 100644
index 67884a9..0000000
--- a/mindspore-jina/__init__.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import numpy as np
-from jina.executors.encoders.frameworks import BaseMindsporeEncoder
-
-
-class MindsporeLeNet(BaseMindsporeEncoder):
-    """
-    :class:`MindsporeLeNet` Encoding image into vectors using mindspore.
-    """
-
-    def encode(self, data, *args, **kwargs):
-        # data is B x D, where D = 28 * 28
-        # LeNet only accepts BCHW format where H=W=32
-        # hence we need to do some simple transform
-        from mindspore import Tensor
-
-        data = np.pad(data.reshape([-1, 1, 28, 28]),
-                      [(0, 0), (0, 0), (0, 4), (0, 4)]).astype('float32')
-        return self.model(Tensor(data)).asnumpy()
-
-    def get_cell(self):
-        from .lenet.src.lenet import LeNet5
-        class LeNet5Embed(LeNet5):
-            def construct(self, x):
-                x = self.conv1(x)
-                x = self.relu(x)
-                x = self.max_pool2d(x)
-                x = self.conv2(x)
-                x = self.relu(x)
-                x = self.max_pool2d(x)
-                x = self.flatten(x)
-                x = self.fc1(x)
-                x = self.relu(x)
-                x = self.fc2(x)
-                x = self.relu(x)
-                return x
-
-        return LeNet5Embed()