typo

Author: dnth

README.md

@@ -603,7 +603,7 @@ This notebooks in this section shows how you can load data from various sources
          </a>
       </td>
       <td rowspan="4">
-         <b> 🧠 TIMM Embeddings:</b> Compute your embeddings using <a href="https://github.com/huggingface/pytorch-image-models">TIMM (PyTorch Image Models)</a> and run fastdup over the them to analyze for issues.  model. Runs on CPU and GPU.
+         <b> 🧠 TIMM Embeddings:</b> Compute dataset embeddings using <a href="https://github.com/huggingface/pytorch-image-models">TIMM (PyTorch Image Models)</a> and run fastdup over the them to surface dataset issues. Runs on CPU and GPU.
       </td>
       <td align="center" width="80">
          <a href="https://nbviewer.org/github/visual-layer/fastdup/blob/main/examples/embeddings-timm.ipynb">

examples/embeddings-timm.ipynb

@@ -62,7 +62,7 @@
     "\n",
     "First, let's install the neccessary packages:\n",
     "\n",
-    "- fastdup - To analyze issues in the dataset.\n",
+    "- [fastdup](https://github.com/visual-layer/fastdup) - To analyze issues in the dataset.\n",
     "- [TIMM (PyTorch Image Models)](https://github.com/huggingface/pytorch-image-models) - To acquire pre-trained models."
    ]
   },
@@ -130,7 +130,11 @@
    "metadata": {},
    "source": [
     "## List TIMM Models\n",
-    "There are over a thousand models on TIMM. Let's list down models that match the keyword `dino`."
+    "There are currently 1212 computer vision models on TIMM. Pick a model of your choice to compute the embedding with.\n",
+    "\n",
+    "Now, pick a model of your choice. For demonstration, we will go with a relatively new model `vit_small_patch14_dinov2.lvd142m` from MetaAI. \n",
+    "\n",
+    "Let's list down models that match the keyword `dino`."
    ]
   },
   {
@@ -171,8 +175,6 @@
    "id": "633dce0c-47eb-4039-8cd4-a36874c49b8a",
    "metadata": {},
    "source": [
-    "Now, pick a model of your choice. For demonstration, we will go with a relatively new model `vit_small_patch14_dinov2.lvd142m` from MetaAI. \n",
-    "\n",
     "DINOv2 models produce high-performance visual features that can be directly employed with classifiers as simple as linear layers on a variety of computer vision tasks; these visual features are robust and perform well across domains without any requirement for fine-tuning. Read more about DINOv2 [here](https://github.com/facebookresearch/dinov2).\n",
     "\n",
     "It makes sense for us to use DINOv2 as a model to create an embedding of the dataset."
@@ -288,7 +290,7 @@
    "source": [
     "## Run fastdup\n",
     "\n",
-    "Now what's left is to load the embeddings into fastdup and run an analysis to surface dataset issues."
+    "Now let's load the embeddings into fastdup and run an analysis to surface dataset issues."
    ]
   },
   {
@@ -2467,7 +2469,11 @@
    "metadata": {},
    "source": [
     "## Wrap Up\n",
-    "In this tutorial, we showed how you can run fastdup using pre-computed feature vectors. Running over pre-computed feature vectors significantly reduces run time compared to running over raw image files.\n",
+    "In this tutorial, we showed how you can compute embeddings on your dataset using TIMM and run fastdup on top of it to surface dataset issues.\n",
+    "\n",
+    "Questions about this tutorial? Reach out to us on our [Slack channel](https://visuallayer.slack.com/)!\n",
+    "\n",
+    "\n",
     "\n",
     "Next, feel free to check out other tutorials -\n",
     "\n",