diff --git a/train/src/ui/monitoring.py b/train/src/ui/monitoring.py
index 2c50fbe..e8c8132 100644
--- a/train/src/ui/monitoring.py
+++ b/train/src/ui/monitoring.py
@@ -396,30 +396,36 @@ def prepare_segmentation_data(state, img_dir, ann_dir, palette, target_classes):
     datasets = os.listdir(temp_project_seg_dir)
     os.makedirs(os.path.join(g.project_seg_dir, img_dir), exist_ok=True)
     os.makedirs(os.path.join(g.project_seg_dir, ann_dir), exist_ok=True)
-    for dataset in datasets:
-        if not os.path.isdir(os.path.join(temp_project_seg_dir, dataset)):
-            if dataset == "meta.json":
-                shutil.move(os.path.join(temp_project_seg_dir, "meta.json"), g.project_seg_dir)
-            continue
-        # convert masks to required format and save to general ann_dir
-        mask_files = os.listdir(os.path.join(temp_project_seg_dir, dataset, ann_dir))
-        for mask_file in mask_files:
-            mask = cv2.imread(os.path.join(temp_project_seg_dir, dataset, ann_dir, mask_file))[
-                :, :, ::-1
-            ]
-            result = np.zeros((mask.shape[0], mask.shape[1]), dtype=np.int32)
-            # human masks to machine masks
-            for color_idx, color in enumerate(palette):
-                colormap = np.where(np.all(mask == color, axis=-1))
-                result[colormap] = color_idx
-            cv2.imwrite(os.path.join(g.project_seg_dir, ann_dir, mask_file), result)
-
-        imgfiles_to_move = os.listdir(os.path.join(temp_project_seg_dir, dataset, img_dir))
-        for filename in imgfiles_to_move:
-            shutil.move(
-                os.path.join(temp_project_seg_dir, dataset, img_dir, filename),
-                os.path.join(g.project_seg_dir, img_dir),
-            )
+    total_items = sly.Project(g.project_dir, sly.OpenMode.READ).total_items
+    with TqdmProgress(
+        message="Converting masks to required format",
+        total=total_items,
+    ) as p:
+        for dataset in datasets:
+            if not os.path.isdir(os.path.join(temp_project_seg_dir, dataset)):
+                if dataset == "meta.json":
+                    shutil.move(os.path.join(temp_project_seg_dir, "meta.json"), g.project_seg_dir)
+                continue
+            # convert masks to required format and save to general ann_dir
+            mask_files = os.listdir(os.path.join(temp_project_seg_dir, dataset, ann_dir))
+            for mask_file in mask_files:
+                mask = cv2.imread(os.path.join(temp_project_seg_dir, dataset, ann_dir, mask_file))[
+                    :, :, ::-1
+                ]
+                result = np.zeros((mask.shape[0], mask.shape[1]), dtype=np.int32)
+                # human masks to machine masks
+                for color_idx, color in enumerate(palette):
+                    colormap = np.where(np.all(mask == color, axis=-1))
+                    result[colormap] = color_idx
+                cv2.imwrite(os.path.join(g.project_seg_dir, ann_dir, mask_file), result)
+                p.update(1)
+
+            imgfiles_to_move = os.listdir(os.path.join(temp_project_seg_dir, dataset, img_dir))
+            for filename in imgfiles_to_move:
+                shutil.move(
+                    os.path.join(temp_project_seg_dir, dataset, img_dir, filename),
+                    os.path.join(g.project_seg_dir, img_dir),
+                )
 
 
 def run_benchmark(api: sly.Api, task_id, classes, cfg, state, remote_dir):