add model comparison (bake off) notebook

azureml/medimageinsight/adapter-training.ipynb

@@ -24,6 +24,7 @@
     "### Required for This Notebook\n",
     "\n",
     "- **Model Endpoint(s)**: `MI2_MODEL_ENDPOINT`\n",
+    "- **Additional Dependencies**: None\n",
     "\n",
     "## Adapter Training Overview\n",
     "In this tutorial, we will guide you through the process of using the embeddings generated by MedImageInsight to classify unseen chest pathologies in chest X-rays. We will demonstrate how to train an adapter model to improve classification performance. The steps we'll cover are:\n",

azureml/medimageinsight/zero-shot-classification.ipynb

@@ -22,12 +22,7 @@
     "### Required for This Notebook\n",
     "\n",
     "- **Model Endpoint(s)**: `MI2_MODEL_ENDPOINT` \n",
-    "- **Additional Dependencies**: \n",
-    "  ```bash\n",
-    "  conda install -c pytorch faiss-cpu\n",
-    "  ```\n",
-    "\n",
-    "> **Note**: [FAISS](https://github.com/facebookresearch/faiss) provides efficient algorithms for searching large sets of vectors, making it perfect for building scalable image search systems.\n",
+    "- **Additional Dependencies**: None\n",
     "\n",
     "## Zero-Shot Classification Overview\n",
     "This tutorial will walk you through the steps of using the MedImageInsight embedding model to compute embeddings of an image collection and then classify these images using a set of predefined classes. Here are the steps we will perform:\n",