Upload semanticallysegmentdeezglaciers.ipynb

semanticallysegmentdeezglaciers.ipynb

@@ -67,7 +67,10 @@
         "# 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"
+        "# 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"
       ]
     },
     {
@@ -364,7 +367,7 @@
         "    fp16=True,  # Whether to use 16-bit float precision instead of 32-bit\n",
         "    tf32=True,  # Whether to use tf32 precision instead of 32-bit\n",
         "    gradient_accumulation_steps=4,  # Number of updates steps to accumulate before performing a backward/update pass\n",
-        "    hub_model_id = \"aashraychegu/glacformer\"  # The model ID on the Hugging Face model hub\n",
+        "    hub_model_id = hf_model_name # The model ID on the Hugging Face model hub\n",
         ")\n",
         "\n",
         "# Define the trainer\n",
@@ -397,7 +400,7 @@
         "trainer.model.save_pretrained(\"glacformer\")\n",
         "\n",
         "# Create a repository object for the specified repository on Hugging Face's hub, cloning from the specified source\n",
-        "repo = huggingface_hub.Repository(\"glacformer\", clone_from=\"aashraychegu/glacformer\")\n",
+        "repo = huggingface_hub.Repository(\"glacformer\", clone_from=hf_model_name)\n",
         "\n",
         "# Pull the latest changes from the remote repository\n",
         "repo.git_pull()\n",
@@ -438,7 +441,7 @@
         "# Define a class for the model\n",
         "class glacformer():\n",
         "    def __init__(self, pipeline=pipeline(\"image-segmentation\",\n",
-        "                                         model=\"aashraychegu/glacformer\", image_processor=\"nvidia/MiT-b0\"), image_list = glob(\"secondleg/*/cropped_images/*.png\")) -> None:\n",
+        "                                         model=hf_model_name, image_processor=\"nvidia/MiT-b0\"), image_list = glob(\"secondleg/*/cropped_images/*.png\")) -> None:\n",
         "        self.pipeline = pipeline\n",
         "        self.image_list = image_list\n",
         "    def __getitem__(self, index, alpha = 100):\n",
@@ -492,7 +495,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.7"
+      "version": "3.10.11"
     },
     "widgets": {
       "application/vnd.jupyter.widget-state+json": {