diff --git a/python/tvm/relay/frontend/caffe.py b/python/tvm/relay/frontend/caffe.py
index 78f97af57147..b7bcbde0de63 100644
--- a/python/tvm/relay/frontend/caffe.py
+++ b/python/tvm/relay/frontend/caffe.py
@@ -258,9 +258,9 @@ def convert_concat(self, op):
         in_expr = (self.exp_tab.get_expr(inputs[i])
                    for i in range(len(inputs)))
 
-        params = dict()
-        params['axis'] = op.concat_param.axis
-        out = _op.concatenate(in_expr, axis=params['axis'])
+        c_params = dict()
+        c_params['axis'] = op.concat_param.axis
+        out = _op.concatenate(in_expr, axis=c_params['axis'])
 
         return out
 
diff --git a/tutorials/frontend/from_caffe.py b/tutorials/frontend/from_caffe.py
deleted file mode 100644
index 0e9e5322ca71..000000000000
--- a/tutorials/frontend/from_caffe.py
+++ /dev/null
@@ -1,141 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you 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.
-"""
-Compile Caffe Models
-====================
-**Author**: `Chen Feng <https://github.com/fernchen>`_
-
-This article is an introductory tutorial to deploy Caffe models with relay.
-
-To get started, Caffe package needs to be installed.
-
-In order to install Caffe, please refer to https://caffe.berkeleyvision.org/installation.html.
-
-If your os is ubuntu version >= 17,04, pre-compiled caffe can be install by:
-
-.. code-block:: bash
-    
-    # install caffe
-    sudo apt install caffe-cpu
-
-
-Below you can file an example on how to compile Caffe model using TVM.
-"""
-######################################################################
-# Download pretrained Caffe model
-# -------------------------------
-from tvm.contrib.download import download_testdata
-
-proto_file_url = ("https://github.com/shicai/MobileNet-Caffe/raw/"
-                        "master/mobilenet_v2_deploy.prototxt")
-blob_file_url = ("https://github.com/shicai/MobileNet-Caffe/blob/"
-                        "master/mobilenet_v2.caffemodel?raw=true")
-
-proto_file = download_testdata(proto_file_url, "mobilenetv2.prototxt", module="model")
-blob_file = download_testdata(blob_file_url, "mobilenetv2.caffemodel", module="model")
-
-######################################################################
-# Load a test image
-# -----------------
-# A single cat dominates the examples!
-from PIL import Image
-import numpy as np
-from matplotlib import pyplot as plt
-
-image_url = "https://github.com/dmlc/mxnet.js/blob/master/data/cat.png?raw=true"
-image_path = download_testdata(image_url, 'cat.png', module='data')
-resized_image = Image.open(image_path).resize((224, 224))
-plt.imshow(resized_image)
-plt.show()
-image_data = np.asarray(resized_image).astype(np.float32)
-
-# Preprocess image
-image_data -= np.array([103.939, 116.779, 123.68], dtype=np.float32)
-image_data /= 58.8
-
-# RGB2BGR for Caffe model
-image_data = image_data[..., ::-1]
-
-# Add a dimension to the image and transpose it so that we have NCHW format layout
-image_data = np.expand_dims(image_data, axis=0)
-image_data = np.transpose(image_data, (0, 3, 1, 2))
-
-######################################################################
-# Compile the model with relay
-# ----------------------------
-from google.protobuf import text_format
-import caffe
-from caffe.proto import caffe_pb2 as pb
-
-# Get Caffe model from buffer
-init_net = pb.NetParameter()
-predict_net = pb.NetParameter()
-with open(proto_file, 'r') as f:
-    text_format.Merge(f.read(), predict_net)
-with open(blob_file, 'rb') as f:
-    init_net.ParseFromString(f.read())
-# Caffe model input layer name, shape and dtype
-shape_dict = {'data': image_data.shape}
-dtype_dict = {'data': 'float32'}
-
-import tvm
-from tvm import relay
-mod, params = relay.frontend.from_caffe(init_net, predict_net, shape_dict, dtype_dict)
-
-# Build the module against to x86 CPU
-target = 'llvm'
-with tvm.transform.PassContext(opt_level=3):
-    lib = relay.build(mod, target=target,params=params)
-
-######################################################################
-# Execute on TVM
-# --------------
-from tvm.contrib import graph_runtime
-
-# Create a runtime executor module
-ctx = tvm.cpu(0)
-m = graph_runtime.GraphModule(lib['default'](ctx))
-
-# Feed input data
-m.set_input('data', tvm.nd.array(image_data))
-
-# execute
-m.run()
-
-# Get output
-tvm_output = m.get_output(0).asnumpy()
-
-######################################################################
-# Display results
-# ---------------
-
-# Load label file
-synset_url = ''.join(['https://gist.githubusercontent.com/zhreshold/',
-                      '4d0b62f3d01426887599d4f7ede23ee5/raw/',
-                      '596b27d23537e5a1b5751d2b0481ef172f58b539/',
-                      'imagenet1000_clsid_to_human.txt'])
-synset_name = 'imagenet1000_clsid_to_human.txt'
-synset_path = download_testdata(synset_url, synset_name, module='data')
-with open(synset_path) as f:
-    synset = eval(f.read())
-
-# Get Top1 prediction
-predictions = np.squeeze(tvm_output)
-prediction = np.argmax(predictions)
-
-# Convert id to class name and show the result
-print("The image prediction result is: id " + str(prediction) + " name: " + synset[prediction])