diff --git a/jina/executors/encoders/cv/paddlehub.py b/jina/executors/encoders/cv/paddlehub.py
index 0f9e731e85189..87f5bb13e3044 100644
--- a/jina/executors/encoders/cv/paddlehub.py
+++ b/jina/executors/encoders/cv/paddlehub.py
@@ -47,7 +47,7 @@ def __init__(self,
             kwargs.pop('pool_strategy')
         if 'output_feature' in kwargs:
             kwargs.pop('output_feature')
-        super().__init__(model_name, None, 'mean', *args, **kwargs)
+        super().__init__(model_name, output_feature=None, pool_strategy='mean', *args, **kwargs)
 
     def get_inputs_and_outputs_name(self, input_dict, output_dict):
         self.inputs_name = input_dict['image'].name
diff --git a/jina/executors/encoders/paddlehub.py b/jina/executors/encoders/paddlehub.py
new file mode 100644
index 0000000000000..10c0f72b1405d
--- /dev/null
+++ b/jina/executors/encoders/paddlehub.py
@@ -0,0 +1,58 @@
+import numpy as np
+
+from . import BaseNumericEncoder
+
+
+class PaddlehubEncoder(BaseNumericEncoder):
+    def __init__(self,
+                 model_name: str,
+                 output_feature: str,
+                 pool_strategy: str = None,
+                 channel_axis: int = -3,
+                 *args,
+                 **kwargs):
+        super().__init__(*args, **kwargs)
+        self.model_name = model_name
+        self.pool_strategy = pool_strategy
+        self.outputs_name = output_feature
+        self.inputs_name = None
+        self.channel_axis = channel_axis
+        self._default_channel_axis = -3
+
+    def post_init(self):
+        import paddlehub as hub
+        import paddle.fluid as fluid
+        module = hub.Module(name=self.model_name)
+        inputs, outputs, self.model = module.context(trainable=False)
+        self.get_inputs_and_outputs_name(inputs, outputs)
+        place = fluid.CUDAPlace(0) if self.on_gpu else fluid.CPUPlace()
+        self.exe = fluid.Executor(place)
+
+    def get_inputs_and_outputs_name(self, input_dict, output_dict):
+        raise NotImplementedError
+
+    def encode(self, data: 'np.ndarray', *args, **kwargs) -> 'np.ndarray':
+        """
+
+        :param data: a `B x T x (Channel x Height x Width)` numpy ``ndarray``, `B` is the size of the batch, `T` is the
+            number of frames
+        :return: a `B x D` numpy ``ndarray``, `D` is the output dimension
+        """
+        if self.channel_axis != self._default_channel_axis:
+            data = np.moveaxis(data, self.channel_axis, self._default_channel_axis)
+        feature_map, *_ = self.exe.run(
+            program=self.model,
+            fetch_list=[self.outputs_name],
+            feed={self.inputs_name: data.astype('float32')},
+            return_numpy=True
+        )
+        if feature_map.ndim == 2 or self.pool_strategy is None:
+            return feature_map
+        return self.get_pooling(feature_map)
+
+    def get_pooling(self, data: 'np.ndarray', axis=None) -> 'np.ndarray':
+        _reduce_axis = tuple((i for i in range(len(data.shape)) if i > 1))
+        return getattr(np, self.pool_strategy)(data, axis=_reduce_axis)
+
+    def close(self):
+        self.exe.close()