diff --git a/evaluation/UMAP_ResNet50.ipynb b/evaluation/UMAP_ResNet50_LHQ.ipynb
similarity index 99%
rename from evaluation/UMAP_ResNet50.ipynb
rename to evaluation/UMAP_ResNet50_LHQ.ipynb
index 10f956a25b876a38f71a9e1dea7fc85cbf5c6229..082221d5d2e1b388ac0558dcd1c8786298dc41f5 100644
--- a/evaluation/UMAP_ResNet50.ipynb
+++ b/evaluation/UMAP_ResNet50_LHQ.ipynb
@@ -32,7 +32,11 @@
     "import numpy as np\n",
     "import seaborn as sns\n",
     "import math\n",
-    "import random"
+    "import random\n",
+    "from torchvision.models import resnet50\n",
+    "from PIL import Image\n",
+    "from torch.utils.data import DataLoader\n",
+    "import torchvision.transforms as transforms"
    ]
   },
   {
@@ -124,7 +128,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def filter_df(df, x=(0,0), y=(0,0), sample=10, flag='both'):\n",
+    "def filter_df(df,path_to_training_data,path_to_samples, x=(0,0), y=(0,0), sample=10, flag='both'):\n",
     "    \n",
     "    filtered_df = df[(df['umap_x'].between(*x)) & (df['umap_y'].between(*y))]\n",
     "\n",
@@ -244,11 +248,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from torchvision.models import resnet50\n",
-    "from PIL import Image\n",
-    "from torch.utils.data import DataLoader\n",
-    "import torchvision.transforms as transforms\n",
-    "\n",
     "def extract_resnet_features(path, sample='all'):\n",
     "    if sample == 'all':\n",
     "        sample_size = -1\n",