{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":117682,"databundleVersionId":14443416,"sourceType":"competition"}],"dockerImageVersionId":31192,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# STEP 1: Setup & Installs\n!pip install -q monai[all] tifffile connected-components-3d\n\nimport os\nimport numpy as np\nimport tifffile\nimport torch\nimport monai\nfrom monai.transforms import *\nfrom monai.networks.nets import UNet\nfrom monai.losses import DiceLoss\nfrom monai.metrics import DiceMetric\nfrom monai.inferers import sliding_window_inference\nfrom monai.data import DataLoader, Dataset, list_data_collate\nfrom pathlib import Path\n\nprint(\"MONAI ready\")\nprint(torch.cuda.is_available())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-13T19:39:27.617434Z","iopub.execute_input":"2025-12-13T19:39:27.617772Z","iopub.status.idle":"2025-12-13T19:43:50.069735Z","shell.execute_reply.started":"2025-12-13T19:39:27.617747Z","shell.execute_reply":"2025-12-13T19:43:50.068572Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# STEP 2: Paths & Config\ntrain_volumes = sorted(Path('/kaggle/input/vesuvius-challenge-surface-detection/train').rglob('*volume.tif'))\ntrain_masks = sorted(Path('/kaggle/input/vesuvius-challenge-surface-detection/train').rglob('*mask.tif'))\n\ntest_volumes = sorted(Path('/kaggle/input/vesuvius-challenge-surface-detection/test').rglob('*volume.tif'))\n\nprint(f\"Train volumes: {len(train_volumes)}\")\nprint(f\"Test volumes: {len(test_volumes)}\")","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}