diff --git a/vta/tutorials/resnet.py b/vta/tutorials/resnet.py
index 7930bfe750c6e..13161586480ea 100644
--- a/vta/tutorials/resnet.py
+++ b/vta/tutorials/resnet.py
@@ -37,7 +37,7 @@
 
 import numpy as np
 import requests
-#from matplotlib import pyplot as plt
+from matplotlib import pyplot as plt
 from PIL import Image
 
 import tvm
@@ -107,10 +107,6 @@ def classify(m, image):
 # Read in ImageNet Categories
 synset = eval(open(os.path.join(data_dir, categ_fn)).read())
 
-# Download pre-tuned op parameters of conv2d for ARM CPU used in VTA
-# autotvm.tophub.check_backend('vta')
-
-
 ######################################################################
 # Setup the Pynq Board's RPC Server
 # ---------------------------------
@@ -152,16 +148,13 @@ def classify(m, image):
 # ------------------------
 # Build the ResNet graph runtime, and configure the parameters.
 
-# Set ``device=arm_cpu`` to run inference on the CPU
+# Set ``device=vtacpu`` to run inference on the CPU
 # or ``device=vta`` to run inference on the FPGA.
 device = "vta"
 
-# Derive the TVM target
-if device == "vta":
-    target = env.target
-elif device == "arm_cpu":
-    target = env.target_vta_cpu
-ctx = remote.context(str(target))
+# TVM target and context
+target = tvm.target.create("llvm -device={}".format(device))
+ctx = remote.ext_dev(0) if device == "vta" else remote.cpu(0)
 
 # TVM module
 m = None
@@ -233,8 +226,8 @@ def classify(m, image):
 response = requests.get(image_url)
 image = Image.open(BytesIO(response.content)).resize((224, 224))
 # Show Image
-# plt.imshow(image)
-# plt.show()
+plt.imshow(image)
+plt.show()
 # Set the input
 image = process_image(image)
 m.set_input('data', image)
@@ -263,60 +256,60 @@ def classify(m, image):
 # Comment the `if False:` out to run the demo
 
 # Early exit - remove for Demo
-# if False:
-
-#     import cv2
-#     import pafy
-#     from IPython.display import clear_output
-
-#     # Helper to crop an image to a square (224, 224)
-#     # Takes in an Image object, returns an Image object
-#     def thumbnailify(image, pad=15):
-#         w, h = image.size
-#         crop = ((w-h)//2+pad, pad, h+(w-h)//2-pad, h-pad)
-#         image = image.crop(crop)
-#         image = image.resize((224, 224))
-#         return image
-
-#     # 16:16 inches
-#     plt.rcParams['figure.figsize'] = [16, 16]
-
-#     # Stream the video in
-#     url = "https://www.youtube.com/watch?v=PJlmYh27MHg&t=2s"
-#     video = pafy.new(url)
-#     best = video.getbest(preftype="mp4")
-#     cap = cv2.VideoCapture(best.url)
-
-#     # Process one frame out of every 48 for variety
-#     count = 0
-#     guess = ""
-#     while(count<2400):
-
-#         # Capture frame-by-frame
-#         ret, frame = cap.read()
-
-#         # Process one every 48 frames
-#         if count % 48 == 1:
-#             frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-#             frame = Image.fromarray(frame)
-#             # Crop and resize
-#             thumb = np.array(thumbnailify(frame))
-#             image = process_image(thumb)
-#             guess = classify(m, image)
-
-#             # Insert guess in frame
-#             frame = cv2.rectangle(thumb,(0,0),(200,0),(0,0,0),50)
-#             cv2.putText(frame, guess, (5,15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (256,256,256), 1, cv2.LINE_AA)
-
-#             plt.imshow(thumb)
-#             plt.axis('off')
-#             plt.show()
-#             if cv2.waitKey(1) & 0xFF == ord('q'):
-#                 break
-#             clear_output(wait=True)
-
-#         count += 1
-
-#     # When everything done, release the capture
-#     cap.release()
-#     cv2.destroyAllWindows()
+if False:
+
+    import cv2
+    import pafy
+    from IPython.display import clear_output
+
+    # Helper to crop an image to a square (224, 224)
+    # Takes in an Image object, returns an Image object
+    def thumbnailify(image, pad=15):
+        w, h = image.size
+        crop = ((w-h)//2+pad, pad, h+(w-h)//2-pad, h-pad)
+        image = image.crop(crop)
+        image = image.resize((224, 224))
+        return image
+
+    # 16:16 inches
+    plt.rcParams['figure.figsize'] = [16, 16]
+
+    # Stream the video in
+    url = "https://www.youtube.com/watch?v=PJlmYh27MHg&t=2s"
+    video = pafy.new(url)
+    best = video.getbest(preftype="mp4")
+    cap = cv2.VideoCapture(best.url)
+
+    # Process one frame out of every 48 for variety
+    count = 0
+    guess = ""
+    while(count<2400):
+
+        # Capture frame-by-frame
+        ret, frame = cap.read()
+
+        # Process one every 48 frames
+        if count % 48 == 1:
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            frame = Image.fromarray(frame)
+            # Crop and resize
+            thumb = np.array(thumbnailify(frame))
+            image = process_image(thumb)
+            guess = classify(m, image)
+
+            # Insert guess in frame
+            frame = cv2.rectangle(thumb,(0,0),(200,0),(0,0,0),50)
+            cv2.putText(frame, guess, (5,15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (256,256,256), 1, cv2.LINE_AA)
+
+            plt.imshow(thumb)
+            plt.axis('off')
+            plt.show()
+            if cv2.waitKey(1) & 0xFF == ord('q'):
+                break
+            clear_output(wait=True)
+
+        count += 1
+
+    # When everything done, release the capture
+    cap.release()
+    cv2.destroyAllWindows()