diff --git a/02_feature_design&training.ipynb b/02_feature_design&training.ipynb
index 76d566c..dbc2782 100755
--- a/02_feature_design&training.ipynb
+++ b/02_feature_design&training.ipynb
@@ -348,12 +348,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false,
     "pycharm": {
-     "name": "#%%\n",
-     "is_executing": true
+     "name": "#%%\n"
     }
    },
    "outputs": [
@@ -406,6 +405,18 @@
       "needs_background": "light"
      },
      "output_type": "display_data"
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[1;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
+      "Input \u001B[1;32mIn [27]\u001B[0m, in \u001B[0;36m<cell line: 3>\u001B[1;34m()\u001B[0m\n\u001B[0;32m      1\u001B[0m model \u001B[38;5;241m=\u001B[39m SpecDetector(model_path\u001B[38;5;241m=\u001B[39mmodel_file, blk_sz\u001B[38;5;241m=\u001B[39mblk_sz, channel_num\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mlen\u001B[39m(selected_bands))\n\u001B[0;32m      2\u001B[0m \u001B[38;5;66;03m# 画图验证效果\u001B[39;00m\n\u001B[1;32m----> 3\u001B[0m \u001B[43mvisualization_evaluation\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdetector\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata_path\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43mr\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mF:\u001B[39;49m\u001B[38;5;124;43m\\\u001B[39;49m\u001B[38;5;124;43mzhouchao\u001B[39;49m\u001B[38;5;124;43m\\\u001B[39;49m\u001B[38;5;124;43m617\u001B[39;49m\u001B[38;5;124;43m\\\u001B[39;49m\u001B[38;5;124;43m617_pure_tobacco\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mselected_bands\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mselected_bands\u001B[49m\u001B[43m)\u001B[49m\n",
+      "File \u001B[1;32m~\\PycharmProjects\\tobacco-industry\\utils.py:112\u001B[0m, in \u001B[0;36mvisualization_evaluation\u001B[1;34m(detector, data_path, selected_bands)\u001B[0m\n\u001B[0;32m    110\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m idx, image_path \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28menumerate\u001B[39m(image_paths):\n\u001B[0;32m    111\u001B[0m     \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mopen\u001B[39m(image_path, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mrb\u001B[39m\u001B[38;5;124m'\u001B[39m) \u001B[38;5;28;01mas\u001B[39;00m f:\n\u001B[1;32m--> 112\u001B[0m         data \u001B[38;5;241m=\u001B[39m \u001B[43mf\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mread\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    113\u001B[0m     img \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39mfrombuffer(data, dtype\u001B[38;5;241m=\u001B[39mnp\u001B[38;5;241m.\u001B[39mfloat32)\u001B[38;5;241m.\u001B[39mreshape((nrows, \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m, ncols))\u001B[38;5;241m.\u001B[39mtranspose(\u001B[38;5;241m0\u001B[39m, \u001B[38;5;241m2\u001B[39m, \u001B[38;5;241m1\u001B[39m)\n\u001B[0;32m    114\u001B[0m     nbands \u001B[38;5;241m=\u001B[39m img\u001B[38;5;241m.\u001B[39mshape[\u001B[38;5;241m2\u001B[39m]\n",
+      "\u001B[1;31mKeyboardInterrupt\u001B[0m: "
+     ]
     }
    ],
    "source": [
@@ -420,8 +431,7 @@
    "metadata": {
     "collapsed": false,
     "pycharm": {
-     "name": "#%%\n",
-     "is_executing": true
+     "name": "#%%\n"
     }
    },
    "outputs": [],
@@ -437,8 +447,7 @@
    "metadata": {
     "collapsed": false,
     "pycharm": {
-     "name": "#%%\n",
-     "is_executing": true
+     "name": "#%%\n"
     }
    }
   }
diff --git a/main.py b/main.py
index b116dd3..15bfc52 100755
--- a/main.py
+++ b/main.py
@@ -11,6 +11,7 @@ selected_model = "rf_8x8_c4_400_13.model"
 def main():
     model_path = os.path.join(ROOT_DIR, "models", selected_model)
     detector = SpecDetector(model_path, blk_sz=8, channel_num=4)
+    _ = detector.predict(np.ones((600, 1024, 4)))
     total_len = nrows * ncols * nbands * 4
     if not os.access(img_fifo_path, os.F_OK):
         os.mkfifo(img_fifo_path, 0o777)