diff --git a/nnunetv2/training/dataloading/utils.py b/nnunetv2/training/dataloading/utils.py
index 352d18285..7c14bd167 100644
--- a/nnunetv2/training/dataloading/utils.py
+++ b/nnunetv2/training/dataloading/utils.py
@@ -92,9 +92,11 @@ def _convert_to_npy(npz_file: str, unpack_segmentation: bool = True, overwrite_e
     try:
         a = np.load(npz_file)  # inexpensive, no compression is done here. This just reads metadata
         if overwrite_existing or not isfile(npz_file[:-3] + "npy"):
-            np.save(npz_file[:-3] + "npy", a['data'])
+            if not os.path.exists(npz_file[:-3] + "npy"):
+                np.save(npz_file[:-3] + "npy", a['data'])
         if unpack_segmentation and (overwrite_existing or not isfile(npz_file[:-4] + "_seg.npy")):
-            np.save(npz_file[:-4] + "_seg.npy", a['seg'])
+            if not os.path.exists(npz_file[:-4] + "_seg.npy"):
+                np.save(npz_file[:-4] + "_seg.npy", a['seg'])
     except KeyboardInterrupt:
         if isfile(npz_file[:-3] + "npy"):
             os.remove(npz_file[:-3] + "npy")
diff --git a/scripts/generate_json.ipynb b/scripts/generate_json.ipynb
index 28056abe3..2b3fe1073 100755
--- a/scripts/generate_json.ipynb
+++ b/scripts/generate_json.ipynb
@@ -11,36 +11,22 @@
     "from nnunetv2.dataset_conversion.generate_dataset_json import generate_dataset_json\n",
     "import os\n",
     "\n",
-    "dataset = \"/media/eolika/49755d50-5426-4672-87cc-2d1a5a3747ad/nnUNet/nnUNet_raw/Dataset103_mosaic_cardiovascular\"\n",
+    "dataset = \"/media/eolika/49755d50-5426-4672-87cc-2d1a5a3747ad/nnUNet/nnUNet_raw/Dataset102_mosaic_muscles\"\n",
     "\n",
     "generate_dataset_json(output_folder=dataset,\n",
     "                      channel_names={0: \"CT\"},\n",
     "                      labels={\n",
     "                          \"background\": 0,\n",
-    "                          \"heart_myocardium\": 1,\n",
-    "                          \"heart_atrium_left\": 2,\n",
-    "                          \"heart_ventricle_left\": 3,\n",
-    "                          \"heart_atrium_right\": 4,\n",
-    "                          \"heart_ventricle_right\": 5,\n",
-    "                          \"aorta\": 6,\n",
-    "                          \"pulmonary_vein\": 7,\n",
-    "                          \"pulmonary_artery\": 8,\n",
-    "                          \"brachiocephalic_trunk\": 9,\n",
-    "                          \"subclavian_artery_right\": 10,\n",
-    "                          \"subclavian_artery_left\": 11,\n",
-    "                          \"common_carotid_artery_right\": 12,\n",
-    "                          \"common_carotid_artery_left\": 13,\n",
-    "                          \"brachiocephalic_vein_left\": 14,\n",
-    "                          \"brachiocephalic_vein_right\": 15,\n",
-    "                          \"superior_vena_cava\": 16,\n",
-    "                          \"inferior_vena_cava\": 17,\n",
-    "                          \"portal_vein_and_splenic_vein\": 18,\n",
-    "                          \"iliac_artery_left\": 19,\n",
-    "                          \"iliac_artery_right\": 20,\n",
-    "                          \"iliac_vena_left\": 21,\n",
-    "                          \"iliac_vena_right\": 22,\n",
-    "                          \"liver_vessels\": 23,\n",
-    "                          \"lung_vessels\": 24\n",
+    "                          \"gluteus_maximus_left\": 1,\n",
+    "                          \"gluteus_maximus_right\": 2,\n",
+    "                          \"gluteus_medius_left\": 3,\n",
+    "                          \"gluteus_medius_right\": 4,\n",
+    "                          \"gluteus_minimus_left\": 5,\n",
+    "                          \"gluteus_minimus_right\": 6,\n",
+    "                          \"autochthon_left\": 7,\n",
+    "                          \"autochthon_right\": 8,\n",
+    "                          \"iliopsoas_left\": 9,\n",
+    "                          \"iliopsoas_right\": 10\n",
     "                        },\n",
     "                        num_training_cases=len(os.listdir(f'{dataset}/imagesTr')), \n",
     "                        file_ending='.nii.gz',\n",
@@ -48,7 +34,7 @@
     "                        reference='BlueMind AI Inc',\n",
     "                        release='1.0.0',\n",
     "                        overwrite_image_reader_writer='NibabelIOWithReorient',\n",
-    "                        description=\"cardiovascular\")"
+    "                        description=\"muscles\")"
    ]
   },
   {