📚 Update the getting started notebook (#1800)

Update the getting started notebook

notebooks/000_getting_started/001_getting_started.ipynb

@@ -5,7 +5,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<center><img src=\"https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/images/logos/anomalib-wide-blue.png\" alt=\"Paris\" class=\"center\"></center>\n",
+    "<center><img src=\"https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/_static/images/logos/anomalib-wide-blue.png\" alt=\"Paris\" class=\"center\"></center>\n",
     "\n",
     "<center>💙 A library for benchmarking, developing and deploying deep learning anomaly detection algorithms</center>\n",
     "\n",
@@ -16,7 +16,7 @@
     "\n",
     "[Anomalib](https://github.com/openvinotoolkit/anomalib): Anomalib is a deep learning library that aims to collect state-of-the-art anomaly detection algorithms for benchmarking on both public and private datasets. Anomalib provides several ready-to-use implementations of anomaly detection algorithms described in the recent literature, as well as a set of tools that facilitate the development and implementation of custom models. The library has a strong focus on image-based anomaly detection, where the goal of the algorithm is to identify anomalous images, or anomalous pixel regions within images in a dataset.\n",
     "\n",
-    "The library supports [`MVTec AD`](https://www.mvtec.com/company/research/datasets/mvtec-ad) (CC BY-NC-SA 4.0) and [`BeanTech`](https://paperswithcode.com/dataset/btad) (CC-BY-SA) for **benchmarking** and `folder` for custom dataset **training/inference**. In this notebook, we will explore `anomalib` training a PADIM model on the `MVTec AD` bottle dataset and evaluating the model's performance. The sections in this notebook explores the steps in `tools/train.py` more in detail. Those who would like to reproduce the results via CLI could use `python tools/train.py --model padim`.\n"
+    "The library supports a number of image and video datasets for **benchmarking** and custom dataset support for **training/inference**. In this notebook, we will explore `anomalib` training a PADIM model on the `MVTec AD` bottle dataset and evaluating the model's performance.\n"
    ]
   },
   {
@@ -59,6 +59,15 @@
     "# %anomalib install -v"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> NOTE:\n",
+    ">\n",
+    "> Although v1.0.0 is on PyPI, it may not be stable and may have bugs. It is therefore recommended to install from source.\n"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -121,7 +130,10 @@
     "    root_directory = current_directory\n",
     "else:\n",
     "    # Otherwise, we'll need to clone the anomalib repo to the `current_directory`\n",
-    "    repo = Repo.clone_from(url=\"https://github.com/openvinotoolkit/anomalib.git\", to_path=current_directory)\n",
+    "    repo = Repo.clone_from(\n",
+    "        url=\"https://github.com/openvinotoolkit/anomalib.git\",\n",
+    "        to_path=current_directory,\n",
+    "    )\n",
     "    root_directory = current_directory / \"anomalib\"\n",
     "\n",
     "os.chdir(root_directory)"