add fastdup version to example

Author: dnth

examples/analysing-image-classification-dataset.ipynb

@@ -451,6 +451,28 @@
     "In this example we run fastdup by providing the annotations."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7f69d8b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.918'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import fastdup\n",
+    "fastdup.__version__"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 6,
@@ -493,7 +515,6 @@
     }
    ],
    "source": [
-    "import fastdup\n",
     "work_dir = 'fastdup_imagenette'\n",
     "\n",
     "fd = fastdup.create(work_dir=work_dir, input_dir=data_dir) \n",

examples/analyzing-object-detection-dataset.ipynb

@@ -93,6 +93,17 @@
     "Note: runtime on a 2 core colab node may be slow, it is recommended to run on 32 core machine to get x16 speedup. "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6d204bad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import fastdup\n",
+    "fastdup.__version__"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -104,7 +115,6 @@
    "source": [
     "# The input for creating the embedding is a folder with images\n",
     "# The output is saved in fastdup_work_dir.\n",
-    "import fastdup\n",
     "fd = fastdup.create(input_dir=\"images/\", work_dir=\"fastdup_work_dir/\")"
    ]
   },