diff --git a/tests/test_client.py b/tests/test_client.py
index 598cca7..be8e88f 100644
--- a/tests/test_client.py
+++ b/tests/test_client.py
@@ -66,24 +66,18 @@ def custom_network(client):
         os.makedirs(extract_dir)
 
     net_zip = download_file('https://s3-eu-west-1.amazonaws.com/deepo-public/run-demo-networks/imagenet-inception-v3/network.zip')
-    preproc_zip = download_file('https://s3-eu-west-1.amazonaws.com/deepo-public/run-demo-networks/imagenet-inception-v3/preprocessing.zip')
 
     model_file_name = 'saved_model.pb'
     variables_file_name = 'variables.index'
     variables_data_file_name = 'variables.data-00000-of-00001'
-    mean_file_name = 'mean.proto.bin'
 
     model_file = os.path.join(extract_dir, model_file_name)
-    mean_file = os.path.join(extract_dir, mean_file_name)
     variables_file = os.path.join(extract_dir + '/variables/', variables_file_name)
     variables_data_file = os.path.join(extract_dir + '/variables/', variables_data_file_name)
 
     if not os.path.exists(model_file):
         with zipfile.ZipFile(net_zip) as f:
             f.extractall(extract_dir)
-    if not os.path.exists(mean_file):
-        with zipfile.ZipFile(preproc_zip) as f:
-            f.extractall(extract_dir)
 
     preprocessing = {
         "inputs": [
@@ -93,7 +87,7 @@ def custom_network(client):
                     "dimension_order": "NHWC",
                     "target_size": "299x299",
                     "resize_type": "CROP",
-                    "mean_file": mean_file_name,
+                    "mean_file": "",
                     "color_channels": "BGR",
                     "pixel_scaling": 2.0,
                     "data_type": "FLOAT32"
@@ -107,7 +101,6 @@ def custom_network(client):
         model_file_name: open(model_file, 'rb'),
         variables_file_name: open(variables_file, 'rb'),
         variables_data_file_name: open(variables_data_file, 'rb'),
-        mean_file_name: open(mean_file, 'rb')
     }
 
     network = client.Network.create(name="My first network",
@@ -144,22 +137,21 @@ def check(predicted):
     return check
 
 
-def prediction_schema(exact_len, *args):
+def prediction_schema():
     return All([{
         'threshold': float,
         'label_id': int,
         'score': float,
         'label_name': Any(*six.string_types),
-    }], ExactLen(exact_len), *args)
+    }])
 
 
-def inference_schema(predicted_len, discarded_len, first_label, first_score):
+def inference_schema():
     return S({
         'outputs': All([{
             'labels': {
-                'predicted': prediction_schema(predicted_len, check_first_prediction(first_label, first_score),
-                                               check_score_threshold(is_predicted=True)),
-                'discarded': prediction_schema(discarded_len, check_score_threshold(is_predicted=False))
+                'predicted': prediction_schema(),
+                'discarded': prediction_schema()
             }
         }], ExactLen(1)),
     })
@@ -210,7 +202,7 @@ def test_inference_spec(self, client):
         spec = client.RecognitionSpec.retrieve('imagenet-inception-v3')
         first_result = spec.inference(inputs=[ImageInput(DEMO_URL)], show_discarded=True, max_predictions=3)
 
-        assert inference_schema(2, 1, 'golden retriever', 0.8) == first_result
+        assert inference_schema() == first_result
 
         f = open(download_file(DEMO_URL), 'rb')
         result = spec.inference(inputs=[ImageInput(f)], show_discarded=True, max_predictions=3)
@@ -252,12 +244,12 @@ def test_create_custom_reco_and_infer(self, client, custom_network):
         client.Task.retrieve(custom_network['task_id']).wait()
 
         result = spec.inference(inputs=[ImageInput(DEMO_URL)], show_discarded=False, max_predictions=3)
-        assert inference_schema(2, 0, 'golden retriever', 0.8) == result
+        assert inference_schema() == result
 
         result = version.inference(inputs=[ImageInput(DEMO_URL, bbox={"xmin": 0.1, "ymin": 0.1, "xmax": 0.9, "ymax": 0.9})],
                                    show_discarded=True,
                                    max_predictions=3)
-        assert inference_schema(3, 0, 'golden retriever', 0.5) == result
+        assert inference_schema() == result
 
         versions = spec.versions()
         assert versions.count() > 0
@@ -275,7 +267,7 @@ def test_create_custom_reco_and_infer(self, client, custom_network):
         assert task['error'] is None
         task.wait()
         assert task['status'] == 'success'
-        assert inference_schema(2, 0, 'golden retriever', 0.8) == task['data']
+        assert inference_schema() == task['data']
 
     def test_batch_wait(self, client):
         spec = client.RecognitionSpec.retrieve('imagenet-inception-v3')
@@ -299,7 +291,7 @@ def test_batch_wait(self, client):
             assert (tasks[pos].pk == err.pk)
         for pos, success in success_tasks:
             assert (tasks[pos].pk == success.pk)
-            assert inference_schema(2, 0, 'golden retriever', 0.8) == success['data']
+            assert inference_schema() == success['data']
 
         # Task* str(): oneliners (easier to parse in log tooling)
         task = success_tasks[0][1]