Upload semanticallysegmentdeezglaciers.ipynb

semanticallysegmentdeezglaciers.ipynb

@@ -64,15 +64,24 @@
         "# Set the allow_tf32 attribute of torch.backends.cuda.matmul to True to allow TensorFloat-32 (TF32) on Ampere devices\n",
         "torch.backends.cuda.matmul.allow_tf32 = True\n",
         "\n",
-        "# Call the notebook_login function to log in to Hugging Face's hub\n",
-        "notebook_login()\n",
-        "\n",
         "# This code imports necessary modules and functions for a machine learning task. It sets up for tensor computations, deep learning, data visualization, working with transformer models, datasets, image manipulations, and more. It also logs into Hugging Face's hub.\n",
         "\n",
         "# This sets the model's huggingface URL\n",
         "hf_model_name = \"glacierscopessegmentation/glacier_segmentation_transformer\"\n"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Call the notebook_login function to log in to Hugging Face's hub\n",
+        "notebook_login()\n",
+        "\n",
+        "# make sure to login, or use the huggingface-cli to login"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -166,7 +175,7 @@
       "outputs": [],
       "source": [
         "# Load a dataset from Hugging Face's hub using the specified repository name\n",
-        "ds = load_dataset(\"aashraychegu/glacier_scopes\")\n",
+        "ds = load_dataset(\"glacierscopessegmentation/secondleg\")\n",
         "\n",
         "# Split the \"train\" subset of the dataset into a training set and a test set, with 5% of the data going to the test set\n",
         "ds = ds[\"train\"].train_test_split(.05)\n",