Upload A_U_Net_Based_CNN_Autoencoder_for_Preprocessing_Noisy_Images_in_Classification_Tasks.ipynb

Notebook/A_U_Net_Based_CNN_Autoencoder_for_Preprocessing_Noisy_Images_in_Classification_Tasks.ipynb

@@ -1594,7 +1594,7 @@
         "# Point to the extracted dataset, not the zip file location\n",
         "dataset_root = \"/content/my_data/CroppedYale\"\n",
         "\n",
-        "# 1. Check mislabeled images\n",
+        "# Check mislabeled images\n",
         "mismatches = []\n",
         "for i, row in df.iterrows():\n",
         "    folder_name = os.path.basename(os.path.dirname(row[\"full_path\"]))\n",
@@ -1603,7 +1603,7 @@
         "\n",
         "print(f\"Found {len(mismatches)} mislabeled images (class vs folder mismatch).\")\n",
         "\n",
-        "# 2. Compare classes vs folders\n",
+        "# Compare classes vs folders\n",
         "classes_in_df = set(df[\"class\"].unique())\n",
         "folders_in_fs = {f for f in os.listdir(dataset_root) if os.path.isdir(os.path.join(dataset_root, f))}\n",
         "\n",