diff --git a/docs/Tutorials/Preprocessing.ipynb b/docs/Tutorials/Preprocessing.ipynb
index bf42aff..143bea5 100644
--- a/docs/Tutorials/Preprocessing.ipynb
+++ b/docs/Tutorials/Preprocessing.ipynb
@@ -13,7 +13,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This notebook assumes you have the DC2 data downloaded  \n",
+    "## This notebook assumes you have the DC2 data downloaded  \n",
     "\n",
     "You will have to change directory paths"
    ]
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -74,12 +74,36 @@
     "import json"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0.1+g11d1363\n",
+      "1.23.4\n",
+      "1.2.1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print the versions to test the imports and so we know what works\n",
+    "print(scarlet.__version__)\n",
+    "print(np.__version__)\n",
+    "print(sep.__version__)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
     "tags": []
    },
    "source": [
+    "### Run Scarlet to produce segmentation maps\n",
+    "\n",
     "First, let's test scarlet using one DC2 image. The DC2 image data is divided into \"tracts\" and \"patches\" on the sky. You can get the data here https://data.lsstdesc.org/.\n",
     "\n",
     "You will need to change the directory paths below"
@@ -162,11 +186,16 @@
     "plt.imshow(datsm[1],norm=colors.LogNorm())"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now we can run detection and segmentation on the cutout image\n",
+    "#### Now we can run detection and segmentation on the cutout image\n",
     "\n",
     "To produce labels for an image, use ```deepdisc.preprocessing.detection.run_scarlet```.  The main inputs you will need are an image, a list of filters, and a psf (either an image or a gaussian standard deviation).\n",
     "Check the API documentation for details. \n",
@@ -253,6 +282,34 @@
     "generate_training_data_example(dirpath, tract='3828',patch='1,2',sp=4,plot_scene=False, plot_likelihood=False, write_results=True, outdir=outdir)\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -663,7 +720,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's run a few more times to get a set of images"
+    "#### Let's run a few more times to get a set of images"
    ]
   },
   {