{"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":13836,"databundleVersionId":1718836,"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,"execution":{"iopub.status.busy":"2025-12-01T18:41:02.490260Z","iopub.execute_input":"2025-12-01T18:41:02.490557Z","iopub.status.idle":"2025-12-01T18:41:49.536913Z","shell.execute_reply.started":"2025-12-01T18:41:02.490532Z","shell.execute_reply":"2025-12-01T18:41:49.535093Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(\"=\" * 80)\nprint(\"CASSAVA LEAF DISEASE CLASSIFICATION\")\nprint(\"=\" * 80)\nprint(\"\\n[1/13] Importing libraries...\")\n\n# Core libraries\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.metrics import accuracy_score, confusion_matrix, classification_report\nimport cv2\nimport os\nimport time\nimport random\nimport gc\nfrom tqdm import tqdm\nimport warnings\nwarnings.filterwarnings('ignore')\n\n# PyTorch core\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torch.utils.data import Dataset, DataLoader\nfrom torch.utils.data.sampler import RandomSampler, SequentialSampler, WeightedRandomSampler\nfrom torch.optim.lr_scheduler import CosineAnnealingWarmRestarts\n\n# Augmentations\nimport albumentations as A\nfrom albumentations.pytorch import ToTensorV2\n\n# Install required packages (uncomment if needed)\n# !pip install timm\n# !pip install git+https://github.com/ildoonet/pytorch-gradual-warmup-lr.git\n\n# Advanced libraries\nimport timm  # PyTorch Image Models\nfrom timm.utils import AverageMeter\n\nprint(\"✓ All libraries imported successfully\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-01T18:42:01.970457Z","iopub.execute_input":"2025-12-01T18:42:01.971915Z","iopub.status.idle":"2025-12-01T18:42:20.373789Z","shell.execute_reply.started":"2025-12-01T18:42:01.971878Z","shell.execute_reply":"2025-12-01T18:42:20.372751Z"}},"outputs":[],"execution_count":null}]}