small fix

posts/2025-02-10-object-detection-random-baseline.ipynb

@@ -25,6 +25,7 @@
     "Given a standard dataset with a fixed set of models, it is easier to compare the performance of different models. But what if we are working on a new model which has performance far from the best set of models but as a first step, we simply want to check if the model is learning anything at all. In such cases, it is useful to compare the performance of the model with a random baseline.\n",
     "\n",
     "# Proposed Idea\n",
+    "\n",
     "* To formalize the problem, let's say for an arbitrary image, model predicts $k$ bounding boxes with sizes $(h_1, w_1), (h_2, w_2), \\ldots, (h_k, w_k)$.\n",
     "* A simple random baseline would be to generate $k$ random bounding boxes for that image with sizes $(h_1, w_1), (h_2, w_2), \\ldots, (h_k, w_k)$. In other words, we can simply move the predicted bounding boxes to random locations ensuring that the bounding boxes are within the image."
    ]
@@ -209,6 +210,7 @@
    "metadata": {},
    "source": [
     "As per our assumption, we would simply need to randomly move the existing bounding boxes keeping their sizes constant with the following constraints:\n",
+    "\n",
     "* The bounding box should be within the image boundaries."
    ]
   },