diff --git a/set_up_default_resources.py b/set_up_default_resources.py
index 32c4506..48e6a67 100755
--- a/set_up_default_resources.py
+++ b/set_up_default_resources.py
@@ -1,6 +1,5 @@
 #!/usr/bin/env python3
-
-#  Copyright (c) 2022 Arm Limited. All rights reserved.
+#  Copyright (c) 2021-2022 Arm Limited. All rights reserved.
 #  SPDX-License-Identifier: Apache-2.0
 #
 #  Licensed under the Apache License, Version 2.0 (the "License");
@@ -14,137 +13,244 @@
 #  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.
-
-import os, errno
-import urllib.request
-import subprocess
+import errno
 import fnmatch
+import json
 import logging
+import os
+import re
+import shutil
+import subprocess
 import sys
-
-from argparse import ArgumentParser, ArgumentTypeError
-from urllib.error import URLError
+import urllib.request
+from argparse import ArgumentParser
+from argparse import ArgumentTypeError
 from collections import namedtuple
+from urllib.error import URLError
 
 
-json_uc_res = [{
-    "use_case_name": "ad",
-    "resources": [{"name": "ad_medium_int8.tflite",
-                   "url": "https://github.com/ARM-software/ML-zoo/raw/7c32b097f7d94aae2cd0b98a8ed5a3ba81e66b18/models/anomaly_detection/micronet_medium/tflite_int8/ad_medium_int8.tflite"},
-                  {"name": "ifm0.npy",
-                   "url": "https://github.com/ARM-software/ML-zoo/raw/7c32b097f7d94aae2cd0b98a8ed5a3ba81e66b18/models/anomaly_detection/micronet_medium/tflite_int8/testing_input/input/0.npy"},
-                  {"name": "ofm0.npy",
-                   "url": "https://github.com/ARM-software/ML-zoo/raw/7c32b097f7d94aae2cd0b98a8ed5a3ba81e66b18/models/anomaly_detection/micronet_medium/tflite_int8/testing_output/Identity/0.npy"}]
-},
+json_uc_res = [
+    {
+        "use_case_name": "ad",
+        "url_prefix": [
+            "https://github.com/ARM-software/ML-zoo/raw/7c32b097f7d94aae2cd0b98a8ed5a3ba81e66b18/models/anomaly_detection/micronet_medium/tflite_int8/"
+        ],
+        "resources": [
+            {
+                "name": "ad_medium_int8.tflite",
+                "url": "{url_prefix:0}ad_medium_int8.tflite",
+            },
+            {"name": "ifm0.npy", "url": "{url_prefix:0}testing_input/input/0.npy"},
+            {"name": "ofm0.npy", "url": "{url_prefix:0}testing_output/Identity/0.npy"},
+        ],
+    },
     {
         "use_case_name": "asr",
-        "resources": [{"name": "wav2letter_pruned_int8.tflite",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/wav2letter_pruned_int8.tflite"},
-                      {"name": "ifm0.npy",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/testing_input/input_2_int8/0.npy"},
-                      {"name": "ofm0.npy",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/testing_output/Identity_int8/0.npy"}]
+        "url_prefix": [
+            "https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/"
+        ],
+        "resources": [
+            {
+                "name": "wav2letter_pruned_int8.tflite",
+                "url": "{url_prefix:0}wav2letter_pruned_int8.tflite",
+            },
+            {
+                "name": "ifm0.npy",
+                "url": "{url_prefix:0}testing_input/input_2_int8/0.npy",
+            },
+            {
+                "name": "ofm0.npy",
+                "url": "{url_prefix:0}testing_output/Identity_int8/0.npy",
+            },
+        ],
     },
     {
         "use_case_name": "img_class",
-        "resources": [{"name": "mobilenet_v2_1.0_224_INT8.tflite",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/e0aa361b03c738047b9147d1a50e3f2dcb13dbcb/models/image_classification/mobilenet_v2_1.0_224/tflite_int8/mobilenet_v2_1.0_224_INT8.tflite"},
-                      {"name": "ifm0.npy",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/e0aa361b03c738047b9147d1a50e3f2dcb13dbcb/models/image_classification/mobilenet_v2_1.0_224/tflite_int8/testing_input/tfl.quantize/0.npy"},
-                      {"name": "ofm0.npy",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/e0aa361b03c738047b9147d1a50e3f2dcb13dbcb/models/image_classification/mobilenet_v2_1.0_224/tflite_int8/testing_output/MobilenetV2/Predictions/Reshape_11/0.npy"}]
+        "url_prefix": [
+            "https://github.com/ARM-software/ML-zoo/raw/e0aa361b03c738047b9147d1a50e3f2dcb13dbcb/models/image_classification/mobilenet_v2_1.0_224/tflite_int8/"
+        ],
+        "resources": [
+            {
+                "name": "mobilenet_v2_1.0_224_INT8.tflite",
+                "url": "{url_prefix:0}mobilenet_v2_1.0_224_INT8.tflite",
+            },
+            {
+                "name": "ifm0.npy",
+                "url": "{url_prefix:0}testing_input/tfl.quantize/0.npy",
+            },
+            {
+                "name": "ofm0.npy",
+                "url": "{url_prefix:0}testing_output/MobilenetV2/Predictions/Reshape_11/0.npy",
+            },
+        ],
     },
     {
         "use_case_name": "object_detection",
-        "resources": [{"name": "yolo-fastest_192_face_v4.tflite",
-                       "url": "https://github.com/emza-vs/ModelZoo/blob/v1.0/object_detection/yolo-fastest_192_face_v4.tflite?raw=true"}]
+        "url_prefix": [
+            "https://github.com/emza-vs/ModelZoo/blob/v1.0/object_detection/"
+        ],
+        "resources": [
+            {
+                "name": "yolo-fastest_192_face_v4.tflite",
+                "url": "{url_prefix:0}yolo-fastest_192_face_v4.tflite?raw=true",
+            }
+        ],
     },
     {
         "use_case_name": "kws",
-        "resources": [{"name": "ifm0.npy",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/testing_input/input/0.npy"},
-                      {"name": "ofm0.npy",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/testing_output/Identity/0.npy"},
-                      {"name": "kws_micronet_m.tflite",
-                       "url": " https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/kws_micronet_m.tflite"}]
+        "url_prefix": [
+            "https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/"
+        ],
+        "resources": [
+            {"name": "ifm0.npy", "url": "{url_prefix:0}testing_input/input/0.npy"},
+            {"name": "ofm0.npy", "url": "{url_prefix:0}testing_output/Identity/0.npy"},
+            {
+                "name": "kws_micronet_m.tflite",
+                "url": "{url_prefix:0}kws_micronet_m.tflite",
+            },
+        ],
     },
-     {
+    {
         "use_case_name": "vww",
-        "resources": [{"name": "vww4_128_128_INT8.tflite",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/7dd3b16bb84007daf88be8648983c07f3eb21140/models/visual_wake_words/micronet_vww4/tflite_int8/vww4_128_128_INT8.tflite"},
-                      {"name": "ifm0.npy",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/7dd3b16bb84007daf88be8648983c07f3eb21140/models/visual_wake_words/micronet_vww4/tflite_int8/testing_input/input/0.npy"},
-                      {"name": "ofm0.npy",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/7dd3b16bb84007daf88be8648983c07f3eb21140/models/visual_wake_words/micronet_vww4/tflite_int8/testing_output/Identity/0.npy"}]
+        "url_prefix": [
+            "https://github.com/ARM-software/ML-zoo/raw/7dd3b16bb84007daf88be8648983c07f3eb21140/models/visual_wake_words/micronet_vww4/tflite_int8/"
+        ],
+        "resources": [
+            {
+                "name": "vww4_128_128_INT8.tflite",
+                "url": "{url_prefix:0}vww4_128_128_INT8.tflite",
+            },
+            {"name": "ifm0.npy", "url": "{url_prefix:0}testing_input/input/0.npy"},
+            {"name": "ofm0.npy", "url": "{url_prefix:0}testing_output/Identity/0.npy"},
+        ],
     },
     {
         "use_case_name": "kws_asr",
-        "resources": [{"name": "wav2letter_pruned_int8.tflite",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/wav2letter_pruned_int8.tflite"},
-                      {"sub_folder": "asr", "name": "ifm0.npy",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/testing_input/input_2_int8/0.npy"},
-                      {"sub_folder": "asr", "name": "ofm0.npy",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/testing_output/Identity_int8/0.npy"},
-                      {"sub_folder": "kws", "name": "ifm0.npy",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/testing_input/input/0.npy"},
-                      {"sub_folder": "kws", "name": "ofm0.npy",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/testing_output/Identity/0.npy"},
-                      {"name": "kws_micronet_m.tflite",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/kws_micronet_m.tflite"}]
+        "url_prefix": [
+            "https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/",
+            "https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/",
+        ],
+        "resources": [
+            {
+                "name": "wav2letter_pruned_int8.tflite",
+                "url": "{url_prefix:0}wav2letter_pruned_int8.tflite",
+            },
+            {
+                "sub_folder": "asr",
+                "name": "ifm0.npy",
+                "url": "{url_prefix:0}testing_input/input_2_int8/0.npy",
+            },
+            {
+                "sub_folder": "asr",
+                "name": "ofm0.npy",
+                "url": "{url_prefix:0}testing_output/Identity_int8/0.npy",
+            },
+            {
+                "sub_folder": "kws",
+                "name": "ifm0.npy",
+                "url": "{url_prefix:1}testing_input/input/0.npy",
+            },
+            {
+                "sub_folder": "kws",
+                "name": "ofm0.npy",
+                "url": "{url_prefix:1}testing_output/Identity/0.npy",
+            },
+            {
+                "name": "kws_micronet_m.tflite",
+                "url": "{url_prefix:1}kws_micronet_m.tflite",
+            },
+        ],
     },
     {
         "use_case_name": "noise_reduction",
-        "resources": [{"name": "rnnoise_INT8.tflite",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/rnnoise_INT8.tflite"},
-                      {"name": "ifm0.npy",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/testing_input/main_input_int8/0.npy"},
-                      {"name": "ifm1.npy",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/testing_input/vad_gru_prev_state_int8/0.npy"},
-                      {"name": "ifm2.npy",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/testing_input/noise_gru_prev_state_int8/0.npy"},
-                      {"name": "ifm3.npy",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/testing_input/denoise_gru_prev_state_int8/0.npy"},
-                      {"name": "ofm0.npy",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/testing_output/Identity_int8/0.npy"},
-                      {"name": "ofm1.npy",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/testing_output/Identity_1_int8/0.npy"},
-                      {"name": "ofm2.npy",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/testing_output/Identity_2_int8/0.npy"},
-                      {"name": "ofm3.npy",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/testing_output/Identity_3_int8/0.npy"},
-                      {"name": "ofm4.npy",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/testing_output/Identity_4_int8/0.npy"},
-                     ]
+        "url_prefix": [
+            "https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/"
+        ],
+        "resources": [
+            {"name": "rnnoise_INT8.tflite", "url": "{url_prefix:0}rnnoise_INT8.tflite"},
+            {
+                "name": "ifm0.npy",
+                "url": "{url_prefix:0}testing_input/main_input_int8/0.npy",
+            },
+            {
+                "name": "ifm1.npy",
+                "url": "{url_prefix:0}testing_input/vad_gru_prev_state_int8/0.npy",
+            },
+            {
+                "name": "ifm2.npy",
+                "url": "{url_prefix:0}testing_input/noise_gru_prev_state_int8/0.npy",
+            },
+            {
+                "name": "ifm3.npy",
+                "url": "{url_prefix:0}testing_input/denoise_gru_prev_state_int8/0.npy",
+            },
+            {
+                "name": "ofm0.npy",
+                "url": "{url_prefix:0}testing_output/Identity_int8/0.npy",
+            },
+            {
+                "name": "ofm1.npy",
+                "url": "{url_prefix:0}testing_output/Identity_1_int8/0.npy",
+            },
+            {
+                "name": "ofm2.npy",
+                "url": "{url_prefix:0}testing_output/Identity_2_int8/0.npy",
+            },
+            {
+                "name": "ofm3.npy",
+                "url": "{url_prefix:0}testing_output/Identity_3_int8/0.npy",
+            },
+            {
+                "name": "ofm4.npy",
+                "url": "{url_prefix:0}testing_output/Identity_4_int8/0.npy",
+            },
+        ],
     },
     {
         "use_case_name": "inference_runner",
-        "resources": [{"name": "dnn_s_quantized.tflite",
-                       "url": "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/dnn_small/tflite_int8/dnn_s_quantized.tflite"}
-                      ]
-    },]
+        "url_prefix": [
+            "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/dnn_small/tflite_int8/"
+        ],
+        "resources": [
+            {
+                "name": "dnn_s_quantized.tflite",
+                "url": "{url_prefix:0}dnn_s_quantized.tflite",
+            }
+        ],
+    },
+]
 
 # Valid NPU configurations:
 valid_npu_config_names = [
-        'ethos-u55-32', 'ethos-u55-64',
-        'ethos-u55-128', 'ethos-u55-256',
-        'ethos-u65-256','ethos-u65-512']
+    "ethos-u55-32",
+    "ethos-u55-64",
+    "ethos-u55-128",
+    "ethos-u55-256",
+    "ethos-u65-256",
+    "ethos-u65-512",
+]
 
 # Default NPU configurations (these are always run when the models are optimised)
 default_npu_config_names = [valid_npu_config_names[2], valid_npu_config_names[4]]
 
 # NPU config named tuple
-NPUConfig = namedtuple('NPUConfig',['config_name',
-                                    'memory_mode',
-                                    'system_config',
-                                    'ethos_u_npu_id',
-                                    'ethos_u_config_id',
-                                    'arena_cache_size'])
+NPUConfig = namedtuple(
+    "NPUConfig",
+    [
+        "config_name",
+        "memory_mode",
+        "system_config",
+        "ethos_u_npu_id",
+        "ethos_u_config_id",
+        "arena_cache_size",
+    ],
+)
 
 # The internal SRAM size for Corstone-300 implementation on MPS3 specified by AN552
-mps3_max_sram_sz = 2 * 1024 * 1024 # 2 MiB (2 banks of 1 MiB each)
+mps3_max_sram_sz = 2 * 1024 * 1024  # 2 MiB (2 banks of 1 MiB each)
 
 
-def call_command(command: str) -> str:
+def call_command(command: str, verbose: bool = True) -> str:
     """
     Helpers function that call subprocess and return the output.
 
@@ -152,10 +258,13 @@
     ----------
     command (string):  Specifies the command to run.
     """
-    logging.info(command)
-    proc = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True)
+    if verbose:
+        logging.info(command)
+    proc = subprocess.run(
+        command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True
+    )
     log = proc.stdout.decode("utf-8")
-    if proc.returncode == 0:
+    if proc.returncode == 0 and verbose:
         logging.info(log)
     else:
         logging.error(log)
@@ -163,7 +272,9 @@
     return log
 
 
-def get_default_npu_config_from_name(config_name: str, arena_cache_size: int = 0) -> NPUConfig:
+def get_default_npu_config_from_name(
+    config_name: str, arena_cache_size: int = 0
+) -> NPUConfig:
     """
     Gets the file suffix for the tflite file from the
     `accelerator_config` string.
@@ -183,10 +294,12 @@
                config name
     """
     if config_name not in valid_npu_config_names:
-        raise ValueError(f"""
+        raise ValueError(
+            f"""
             Invalid Ethos-U NPU configuration.
             Select one from {valid_npu_config_names}.
-            """)
+            """
+        )
 
     strings_ids = ["ethos-u55-", "ethos-u65-"]
     processor_ids = ["U55", "U65"]
@@ -195,28 +308,43 @@
     system_configs = ["Ethos_U55_High_End_Embedded", "Ethos_U65_High_End"]
     memory_modes_arena = {
         # For shared SRAM memory mode, we use the MPS3 SRAM size by default
-        "Shared_Sram"    : mps3_max_sram_sz if arena_cache_size <= 0 else arena_cache_size,
+        "Shared_Sram": mps3_max_sram_sz if arena_cache_size <= 0 else arena_cache_size,
         # For dedicated SRAM memory mode, we do no override the arena size. This is expected to
         # be defined in the vela configuration file instead.
-        "Dedicated_Sram" : None if arena_cache_size <= 0 else arena_cache_size
+        "Dedicated_Sram": None if arena_cache_size <= 0 else arena_cache_size,
     }
 
     for i in range(len(strings_ids)):
         if config_name.startswith(strings_ids[i]):
             npu_config_id = config_name.replace(strings_ids[i], prefix_ids[i])
-            return NPUConfig(config_name=config_name,
-                             memory_mode=memory_modes[i],
-                             system_config=system_configs[i],
-                             ethos_u_npu_id=processor_ids[i],
-                             ethos_u_config_id=npu_config_id,
-                             arena_cache_size=memory_modes_arena[memory_modes[i]])
+            return NPUConfig(
+                config_name=config_name,
+                memory_mode=memory_modes[i],
+                system_config=system_configs[i],
+                ethos_u_npu_id=processor_ids[i],
+                ethos_u_config_id=npu_config_id,
+                arena_cache_size=memory_modes_arena[memory_modes[i]],
+            )
 
     return None
 
 
-def set_up_resources(run_vela_on_models: bool = False,
-                     additional_npu_config_names: list = (),
-                     arena_cache_size: int = 0):
+def remove_tree_dir(dir_path):
+    try:
+        # Remove the full directory
+        shutil.rmtree(dir_path)
+        # Re-create an empty one
+        os.mkdir(dir_path)
+    except Exception as e:
+        logging.error(f"Failed to delete {dir_path}.")
+
+
+def set_up_resources(
+    run_vela_on_models: bool = False,
+    additional_npu_config_names: list = (),
+    arena_cache_size: int = 0,
+    check_clean_folder: bool = False,
+):
     """
     Helpers function that retrieve the output from a command.
 
@@ -229,8 +357,26 @@
                             as the cache size. If 0, the default values, as per
                             the NPU config requirements, are used.
     """
+    # Paths
     current_file_dir = os.path.dirname(os.path.abspath(__file__))
-    download_dir = os.path.abspath(os.path.join(current_file_dir, "resources_downloaded"))
+    download_dir = os.path.abspath(
+        os.path.join(current_file_dir, "resources_downloaded")
+    )
+    metadata_file_path = os.path.join(
+        download_dir, "resources_downloaded_metadata.json"
+    )
+
+    metadata_dict = dict()
+    vela_version = "3.2.0"
+
+    # Check if the current directory is a git repository
+    is_git_repo = os.path.exists(os.path.join(current_file_dir, ".git"))
+    git_commit_hash = ""
+    setup_script_hash_changed = False
+    if is_git_repo:
+        # If the current directory is a git script then extract the set_up_default_resorces.py hash
+        command = f"git log -1 --pretty=tformat:%H  {os.path.abspath(__file__)}"
+        git_commit_hash = call_command(command, False)
 
     try:
         #   1.1 Does the download dir exist?
@@ -238,12 +384,35 @@
     except OSError as e:
         if e.errno == errno.EEXIST:
             logging.info("'resources_downloaded' directory exists.")
+            # Check and clean?
+            if check_clean_folder and os.path.isfile(metadata_file_path):
+                with open(metadata_file_path) as (metadata_file):
+                    metadata_dict = json.load(metadata_file)
+                    vela_in_metadata = metadata_dict["ethosu_vela_version"]
+                    if vela_in_metadata != vela_version:
+                        # Check if all the resources needs to be removed and regenerated.
+                        # This can happen when the Vela version has changed.
+                        logging.info(
+                            f"Vela version in metadata is {vela_in_metadata}, current {vela_version}. Removing the resources and re-download them."
+                        )
+                        remove_tree_dir(download_dir)
+                        metadata_dict = dict()
+                    else:
+                        # Check if the set_up_default_resorces.py has changed from last setup, only if this is a git repo
+                        if is_git_repo:
+                            setup_script_hash_changed = not (
+                                metadata_dict["set_up_script_hash"] == git_commit_hash
+                            )
         else:
             raise
 
     # 1.2 Does the virtual environment exist?
-    env_python = str(os.path.abspath(os.path.join(download_dir, "env", "bin", "python3")))
-    env_activate = str(os.path.abspath(os.path.join(download_dir, "env", "bin", "activate")))
+    env_python = str(
+        os.path.abspath(os.path.join(download_dir, "env", "bin", "python3"))
+    )
+    env_activate = str(
+        os.path.abspath(os.path.join(download_dir, "env", "bin", "activate"))
+    )
     if not os.path.isdir(os.path.join(download_dir, "env")):
         os.chdir(download_dir)
         # Create the virtual environment
@@ -254,8 +423,9 @@
             command = f"{env_python} -m {c}"
             call_command(command)
         os.chdir(current_file_dir)
+
     # 1.3 Make sure to have all the requirement
-    requirements = ["ethos-u-vela==3.2.0"]
+    requirements = [f"ethos-u-vela=={vela_version}"]
     command = f"{env_python} -m pip freeze"
     packages = call_command(command)
     for req in requirements:
@@ -264,41 +434,64 @@
             call_command(command)
 
     # 2. Download models
+    logging.info("Downloading resources.")
     for uc in json_uc_res:
+        use_case_name = uc["use_case_name"]
+        res_url_prefix = uc["url_prefix"]
         try:
             #  Does the usecase_name download dir exist?
-            os.mkdir(os.path.join(download_dir, uc["use_case_name"]))
+            os.mkdir(os.path.join(download_dir, use_case_name))
         except OSError as e:
-            if e.errno != errno.EEXIST:
-                logging.error(f"Error creating {uc['use_case_name']} directory.")
+            if e.errno == errno.EEXIST:
+                # The usecase_name download dir exist
+                if setup_script_hash_changed:
+                    for idx, metadata_uc_url_prefix in enumerate(
+                        [
+                            f
+                            for f in metadata_dict["resources_info"]
+                            if f["use_case_name"] == use_case_name
+                        ][0]["url_prefix"]
+                    ):
+                        if metadata_uc_url_prefix != res_url_prefix[idx]:
+                            logging.info(f"Removing {use_case_name} resources.")
+                            remove_tree_dir(os.path.join(download_dir, use_case_name))
+                            break
+            elif e.errno != errno.EEXIST:
+                logging.error(f"Error creating {use_case_name} directory.")
                 raise
 
+        reg_expr_str = r"{url_prefix:(.*\d)}"
+        reg_expr_pattern = re.compile(reg_expr_str)
         for res in uc["resources"]:
             res_name = res["name"]
-            res_url = res["url"]
+            url_prefix_idx = int(reg_expr_pattern.search(res["url"]).group(1))
+            res_url = res_url_prefix[url_prefix_idx] + re.sub(
+                reg_expr_str, "", res["url"]
+            )
+
+            sub_folder = ""
             if "sub_folder" in res:
                 try:
                     #  Does the usecase_name/sub_folder download dir exist?
-                    os.mkdir(os.path.join(download_dir, uc["use_case_name"], res["sub_folder"]))
+                    os.mkdir(
+                        os.path.join(download_dir, use_case_name, res["sub_folder"])
+                    )
                 except OSError as e:
                     if e.errno != errno.EEXIST:
-                        logging.error(f"Error creating {uc['use_case_name']} / {res['sub_folder']} directory.")
+                        logging.error(
+                            f"Error creating {use_case_name} / {res['sub_folder']} directory."
+                        )
                         raise
-                res_dst = os.path.join(download_dir,
-                                       uc["use_case_name"],
-                                       res["sub_folder"],
-                                       res_name)
-            else:
-                res_dst = os.path.join(download_dir,
-                                       uc["use_case_name"],
-                                       res_name)
+                sub_folder = res["sub_folder"]
+
+            res_dst = os.path.join(download_dir, use_case_name, sub_folder, res_name)
 
             if os.path.isfile(res_dst):
                 logging.info(f"File {res_dst} exists, skipping download.")
             else:
                 try:
                     g = urllib.request.urlopen(res_url)
-                    with open(res_dst, 'b+w') as f:
+                    with open(res_dst, "b+w") as f:
                         f.write(g.read())
                         logging.info(f"- Downloaded {res_url} to {res_dst}.")
                 except URLError:
@@ -314,18 +507,26 @@
     # Note: To avoid to run vela twice on the same model, it's supposed that
     # downloaded model names don't contain the 'vela' word.
     if run_vela_on_models is True:
-        config_file = os.path.join(current_file_dir, "scripts", "vela", "default_vela.ini")
-        models = [os.path.join(dirpath, f)
-                  for dirpath, dirnames, files in os.walk(download_dir)
-                  for f in fnmatch.filter(files, '*.tflite') if "vela" not in f]
+        config_file = os.path.join(
+            current_file_dir, "scripts", "vela", "default_vela.ini"
+        )
+        models = [
+            os.path.join(dirpath, f)
+            for dirpath, dirnames, files in os.walk(download_dir)
+            for f in fnmatch.filter(files, "*.tflite")
+            if "vela" not in f
+        ]
 
         # Consolidate all config names while discarding duplicates:
         config_names = list(set(default_npu_config_names + additional_npu_config_names))
 
         # Get npu config tuple for each config name in a list:
-        npu_configs = [get_default_npu_config_from_name(name, arena_cache_size) for name in config_names]
+        npu_configs = [
+            get_default_npu_config_from_name(name, arena_cache_size)
+            for name in config_names
+        ]
 
-        logging.info(f'All models will be optimised for these configs:')
+        logging.info(f"All models will be optimised for these configs:")
         for config in npu_configs:
             logging.info(config)
 
@@ -340,25 +541,32 @@
                 vela_command_arena_cache_size = ""
 
                 if config.arena_cache_size:
-                    vela_command_arena_cache_size = f"--arena-cache-size={config.arena_cache_size}"
+                    vela_command_arena_cache_size = (
+                        f"--arena-cache-size={config.arena_cache_size}"
+                    )
 
-                vela_command = (f". {env_activate} && vela {model} " +
-                       f"--accelerator-config={config.config_name} " +
-                       "--optimise Performance " +
-                       f"--config {config_file} " +
-                       f"--memory-mode={config.memory_mode} " +
-                       f"--system-config={config.system_config} " +
-                       f"--output-dir={output_dir} " +
-                       f"{vela_command_arena_cache_size}")
+                vela_command = (
+                    f". {env_activate} && vela {model} "
+                    + f"--accelerator-config={config.config_name} "
+                    + "--optimise Performance "
+                    + f"--config {config_file} "
+                    + f"--memory-mode={config.memory_mode} "
+                    + f"--system-config={config.system_config} "
+                    + f"--output-dir={output_dir} "
+                    + f"{vela_command_arena_cache_size}"
+                )
 
                 # we want the name to include the configuration suffix. For example: vela_H128,
                 # vela_Y512 etc.
-                new_suffix = "_vela_" + config.ethos_u_config_id + '.tflite'
-                new_vela_optimised_model_path = (
-                    vela_optimised_model_path.replace("_vela.tflite", new_suffix))
+                new_suffix = "_vela_" + config.ethos_u_config_id + ".tflite"
+                new_vela_optimised_model_path = vela_optimised_model_path.replace(
+                    "_vela.tflite", new_suffix
+                )
 
                 if os.path.isfile(new_vela_optimised_model_path):
-                    logging.info(f"File {new_vela_optimised_model_path} exists, skipping optimisation.")
+                    logging.info(
+                        f"File {new_vela_optimised_model_path} exists, skipping optimisation."
+                    )
                     optimisation_skipped = True
                     continue
 
@@ -366,36 +574,63 @@
 
                 # rename default vela model
                 os.rename(vela_optimised_model_path, new_vela_optimised_model_path)
-                logging.info(f"Renaming {vela_optimised_model_path} to {new_vela_optimised_model_path}.")
-
+                logging.info(
+                    f"Renaming {vela_optimised_model_path} to {new_vela_optimised_model_path}."
+                )
 
         # If any optimisation was skipped, show how to regenerate:
         if optimisation_skipped:
             logging.warning("One or more optimisations were skipped.")
-            logging.warning(f"To optimise all the models, please remove the directory {download_dir}.")
+            logging.warning(
+                f"To optimise all the models, please remove the directory {download_dir}."
+            )
+
+    # 4. Collect and write metadata
+    logging.info("Collecting and write metadata.")
+    metadata_dict["ethosu_vela_version"] = vela_version
+    metadata_dict["set_up_script_hash"] = git_commit_hash.strip("\n")
+    metadata_dict["resources_info"] = json_uc_res
+
+    with open(metadata_file_path, "w") as metadata_file:
+        json.dump(metadata_dict, metadata_file, indent=4)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     parser = ArgumentParser()
-    parser.add_argument("--skip-vela",
-                        help="Do not run Vela optimizer on downloaded models.",
-                        action="store_true")
-    parser.add_argument("--additional-ethos-u-config-name",
-                        help=f"""Additional (non-default) configurations for Vela:
+    parser.add_argument(
+        "--skip-vela",
+        help="Do not run Vela optimizer on downloaded models.",
+        action="store_true",
+    )
+    parser.add_argument(
+        "--additional-ethos-u-config-name",
+        help=f"""Additional (non-default) configurations for Vela:
                         {valid_npu_config_names}""",
-                        default=[], action="append")
-    parser.add_argument("--arena-cache-size",
-                        help="Arena cache size in bytes (if overriding the defaults)",
-                        type=int,
-                        default=0)
+        default=[],
+        action="append",
+    )
+    parser.add_argument(
+        "--arena-cache-size",
+        help="Arena cache size in bytes (if overriding the defaults)",
+        type=int,
+        default=0,
+    )
+    parser.add_argument(
+        "--clean",
+        help="Clean the disctory and optimize the downloaded resources",
+        action="store_true",
+    )
     args = parser.parse_args()
 
     if args.arena_cache_size < 0:
-        raise ArgumentTypeError('Arena cache size cannot not be less than 0')
+        raise ArgumentTypeError("Arena cache size cannot not be less than 0")
 
-    logging.basicConfig(filename='log_build_default.log', level=logging.DEBUG)
+    logging.basicConfig(filename="log_build_default.log", level=logging.DEBUG)
     logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))
 
-    set_up_resources(not args.skip_vela,
-                     args.additional_ethos_u_config_name,
-                     args.arena_cache_size)
+    set_up_resources(
+        not args.skip_vela,
+        args.additional_ethos_u_config_name,
+        args.arena_cache_size,
+        args.clean,
+    )