diff --git a/example-get-started-cv/code/notebooks/TrainSegModel.ipynb b/example-get-started-cv/code/notebooks/TrainSegModel.ipynb
index 01657aa4..dba479c2 100644
--- a/example-get-started-cv/code/notebooks/TrainSegModel.ipynb
+++ b/example-get-started-cv/code/notebooks/TrainSegModel.ipynb
@@ -39,7 +39,8 @@
     "from fastai.metrics import DiceMulti\n",
     "from fastai.vision.all import (Resize, SegmentationDataLoaders, aug_transforms,\n",
     "                               imagenet_stats, models, unet_learner)\n",
-    "from ruamel.yaml import YAML"
+    "from ruamel.yaml import YAML\n",
+    "from PIL import Image"
    ]
   },
   {
@@ -100,7 +101,7 @@
    "source": [
     "bs = 8\n",
     "valid_pct = 0.20\n",
-    "img_size = 512\n",
+    "img_size = 256\n",
     "\n",
     "data_loader = SegmentationDataLoaders.from_label_func(\n",
     "        path=train_data_dir,\n",
@@ -142,6 +143,23 @@
     "### Train multiple models with different learning rates using `DVCLiveCallback`"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def dice(mask_pred, mask_true, classes=[0, 1], eps=1e-6):\n",
+    "    dice_list = []\n",
+    "    for c in classes:\n",
+    "        y_true = mask_true == c\n",
+    "        y_pred = mask_pred == c\n",
+    "        intersection = 2.0 * np.sum(y_true * y_pred)\n",
+    "        dice = intersection / (np.sum(y_true) + np.sum(y_pred) + eps)\n",
+    "        dice_list.append(dice)\n",
+    "    return np.mean(dice_list)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -154,7 +172,8 @@
     "    live = Live(dir=os.path.join('results', 'train'), \n",
     "            report=\"md\", \n",
     "            save_dvc_exp=True)\n",
-    "    live.log_param(\"base_lr\", base_lr)\n",
+    "    live.summary[\"base_lr\"] =base_lr\n",
+    "    live.make_summary()\n",
     "    learn = unet_learner(data_loader, \n",
     "                        arch=getattr(models, train_arch), \n",
     "                        metrics=DiceMulti)\n",
@@ -164,7 +183,25 @@
     "        }\n",
     "    learn.fine_tune(\n",
     "        **fine_tune_args,\n",
-    "        cbs=[DVCLiveCallback(live=live)])"
+    "        cbs=[DVCLiveCallback(live=live)])\n",
+    "    \n",
+    "    test_img_fpaths = get_files(Path(\"data\") / \"test_data\", extensions=\".jpg\")\n",
+    "    test_dl = learn.dls.test_dl(test_img_fpaths)\n",
+    "    preds, _ = learn.get_preds(dl=test_dl)\n",
+    "    masks_pred = np.array(preds[:, 1, :] > 0.5, dtype=int)\n",
+    "    test_mask_fpaths = [\n",
+    "        get_mask_path(fpath, Path(\"data\") / \"test_data\") for fpath in test_img_fpaths\n",
+    "    ]\n",
+    "    masks_true = [Image.open(mask_path) for mask_path in test_mask_fpaths]\n",
+    "    masks_true = [\n",
+    "        np.array(img.resize((img_size, img_size)), dtype=int) for img in masks_true\n",
+    "    ]\n",
+    "    with Live(\"results/evaluate\", report=\"md\") as live:\n",
+    "        dice_multi = 0.0\n",
+    "        for ii in range(len(masks_true)):\n",
+    "            mask_pred, mask_true = masks_pred[ii], masks_true[ii]\n",
+    "            dice_multi += dice(mask_true, mask_pred) / len(masks_true)\n",
+    "        live.summary[\"dice_multi\"] = dice_multi"
    ]
   },
   {
@@ -183,8 +220,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "%%bash\n",
     "# Apply best performing experiment to the workspace\n",
-    "!EXP=$(dvc exp show --csv --sort-by dice_multi | tail -n 1 | cut -d , -f 1) && dvc exp apply $EXP"
+    "BEST_EXP_NAME=$(dvc exp show --csv --sort-by dice_multi | tail -n 1 | cut -d , -f 1)\n",
+    "echo  \"Applying $BEST_EXP_NAME\"\n",
+    "dvc exp apply  \"$BEST_EXP_NAME\""
    ]
   },
   {