diff --git a/examples/quantization_aware_training/torch/anomalib/main.py b/examples/quantization_aware_training/torch/anomalib/main.py
index f21dbdd642c..20fb234a90d 100644
--- a/examples/quantization_aware_training/torch/anomalib/main.py
+++ b/examples/quantization_aware_training/torch/anomalib/main.py
@@ -12,13 +12,17 @@
 import os
 import re
 import subprocess
+import tarfile
 from copy import deepcopy
 from pathlib import Path
 from typing import List
+from urllib.request import urlretrieve
 
 import torch
 from anomalib import TaskType
 from anomalib.data import MVTec
+from anomalib.data.image import mvtec
+from anomalib.data.utils import download
 from anomalib.deploy import ExportType
 from anomalib.engine import Engine
 from anomalib.models import Stfpm
@@ -27,14 +31,39 @@
 
 HOME_PATH = Path.home()
 DATASET_PATH = HOME_PATH / ".cache/nncf/datasets/mvtec"
-CHECKPOINT_PATH = HOME_PATH / ".cache/nncf/models/stfpm_mvtec"
+CHECKPOINT_PATH = HOME_PATH / ".cache/nncf/models/anomalib"
 ROOT = Path(__file__).parent.resolve()
 FP32_RESULTS_ROOT = ROOT / "fp32"
 INT8_RESULTS_ROOT = ROOT / "int8"
-CHECKPOINT_URL = "https://huggingface.co/alexsu52/stfpm_mvtec_capsule/resolve/main/qat/model.ckpt"
+CHECKPOINT_URL = "https://storage.openvinotoolkit.org/repositories/nncf/examples/torch/anomalib/stfpm_mvtec.ckpt"
 USE_PRETRAINED = True
 
 
+def download_and_extract(root: Path, info: download.DownloadInfo) -> None:
+    root.mkdir(parents=True, exist_ok=True)
+    downloaded_file_path = root / info.url.split("/")[-1]
+    print(f"Downloading the {info.name} dataset.")
+    with download.DownloadProgressBar(unit="B", unit_scale=True, miniters=1, desc=info.name) as progress_bar:
+        urlretrieve(
+            url=f"{info.url}",
+            filename=downloaded_file_path,
+            reporthook=progress_bar.update_to,
+        )
+    print("Checking the hash of the downloaded file.")
+    download.check_hash(downloaded_file_path, info.hashsum)
+    print(f"Extracting the {info.name} dataset.")
+    with tarfile.open(downloaded_file_path) as tar_file:
+        tar_file.extractall(root)
+    print("Cleaning up files.")
+    downloaded_file_path.unlink()
+
+
+def create_dataset(root: Path) -> MVTec:
+    if not root.exists():
+        download_and_extract(root, mvtec.DOWNLOAD_INFO)
+    return MVTec(root)
+
+
 def run_benchmark(model_path: Path, shape: List[int]) -> float:
     command = f"benchmark_app -m {model_path} -d CPU -api async -t 15"
     command += f' -shape "[{",".join(str(x) for x in shape)}]"'
@@ -65,14 +94,14 @@ def main():
     print(os.linesep + "[Step 1] Prepare the model and dataset")
 
     model = Stfpm()
-    datamodule = MVTec(root=DATASET_PATH)
+    datamodule = create_dataset(root=DATASET_PATH)
 
     # Create an engine for the original model
     engine = Engine(task=TaskType.SEGMENTATION, default_root_dir=FP32_RESULTS_ROOT)
     if USE_PRETRAINED:
         # Load the pretrained checkpoint
         CHECKPOINT_PATH.mkdir(parents=True, exist_ok=True)
-        ckpt_path = CHECKPOINT_PATH / "model.ckpt"
+        ckpt_path = CHECKPOINT_PATH / "stfpm_mvtec.ckpt"
         torch.hub.download_url_to_file(CHECKPOINT_URL, ckpt_path)
     else:
         # (Optional) Train the model from scratch