diff --git a/setup.cfg b/setup.cfg
index b72b7fe..6b55a3b 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -4,7 +4,7 @@
 
 [metadata]
 name = dataset-image-annotator
-version = 0.0.25
+version = 0.0.26
 description = Image annotation tool
 author = Oleg Korsak
 author_email = kamikaze.is.waiting.you@gmail.com
@@ -39,7 +39,7 @@ install_requires =
 # The usage of test_requires is discouraged, see `Dependency Management` docs
 # tests_require = pytest; pytest-cov
 # Require a specific Python version, e.g. Python 2.7 or >= 3.4
-python_requires = >=3.10
+python_requires = >=3.11
 
 [options.packages.find]
 where = src
@@ -66,7 +66,6 @@ web =
     passlib[argon2]==1.7.4
     PyJWT==2.7.0
     python-dateutil==2.8.2
-    opencv-python==4.8.0.74
     python3-commons==0.0.25
     SQLAlchemy==1.4.46
     uvicorn[standard]==0.23.1
diff --git a/src/dataset_image_annotator/__main__.py b/src/dataset_image_annotator/__main__.py
index 4a7f30e..f323bdf 100644
--- a/src/dataset_image_annotator/__main__.py
+++ b/src/dataset_image_annotator/__main__.py
@@ -7,7 +7,6 @@
 from pathlib import Path
 from typing import Sequence, Mapping
 
-import cv2
 import numpy as np
 import rawpy
 from PySide6.QtCore import QFile, QIODevice, QDir, QFileInfo, QModelIndex, Qt, QStringListModel
@@ -87,46 +86,8 @@ def get_raw_thumbnail(path: Path):
             return thumb
 
 
-def anonymize_image(image):
-    # Convert the image to grayscale
-    decoded_image = cv2.imdecode(np.frombuffer(image.data, np.uint8), -1)
-    gray = cv2.cvtColor(decoded_image, cv2.COLOR_BGR2GRAY)
-
-    # Load the cascade classifier for detecting cars
-    cascades = (
-        str(Path(Path(__file__).parent, 'cars.xml')),
-        str(Path(cv2.data.haarcascades, 'haarcascade_russian_plate_number.xml'))
-    )
-
-    for cascade in cascades:
-        car_cascade = cv2.CascadeClassifier(cascade)
-
-        # Detect cars in the image
-        cars = car_cascade.detectMultiScale(gray, 1.1, 1)
-
-        # Iterate over the detected cars
-        for (x, y, w, h) in cars:
-            # Blur the license plate
-            decoded_image[y + h - 20:y + h, x:x + w] = cv2.GaussianBlur(
-                decoded_image[y + h - 20:y + h, x:x + w], (23, 23), 30)
-
-    # Load the cascade classifier for detecting faces
-    face_cascade = cv2.CascadeClassifier(str(Path(cv2.data.haarcascades, 'haarcascade_frontalface_default.xml')))
-
-    # Detect faces in the image
-    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
-
-    # Iterate over the detected faces
-    for (x, y, w, h) in faces:
-        # Blur the face
-        decoded_image[y:y + h, x:x + w] = cv2.GaussianBlur(decoded_image[y:y + h, x:x + w], (23, 23), 30)
-
-    return cv2.imencode('.jpg', decoded_image)[1].tobytes()
-
-
 def generate_thumbnail(thumbnail_dir_path: Path, path: Path) -> QPixmap:
     thumbnail = get_raw_thumbnail(path)
-    thumbnail = anonymize_image(thumbnail)
 
     thumb_pixmap = QPixmap()
     thumb_pixmap.loadFromData(thumbnail)
diff --git a/src/dataset_image_annotator/cars.xml b/src/dataset_image_annotator/cars.xml
deleted file mode 100644
index 6e9f225..0000000
--- a/src/dataset_image_annotator/cars.xml
+++ /dev/null
@@ -1,3654 +0,0 @@
-<?xml version="1.0"?>
-<!-- Add more negative to training. -->
-<opencv_storage>
-<cars3 type_id="opencv-haar-classifier">
-  <size>
-    20 20</size>
-  <stages>
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-            <left_val>-0.7191650867462158</left_val>
-            <right_val>0.7359663248062134</right_val></_></_>
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-            <left_val>0.5866637229919434</left_val>
-            <right_val>-0.5909150242805481</right_val></_></_>
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-      <stage_threshold>-1.0691740512847900</stage_threshold>
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