{"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":"nvidiaTeslaT4","dataSources":[{"sourceId":113558,"databundleVersionId":14456136,"isSourceIdPinned":false,"sourceType":"competition"}],"dockerImageVersionId":31193,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"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":"2025-12-02T09:11:34.113071Z","iopub.execute_input":"2025-12-02T09:11:34.113321Z","iopub.status.idle":"2025-12-02T09:11:39.807601Z","shell.execute_reply.started":"2025-12-02T09:11:34.113302Z","shell.execute_reply":"2025-12-02T09:11:39.806634Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\n\n# You need to implement or import the rle_encode function\n# from a competition utility script.\n# (Example code for rle_encode can be found in the official starter notebooks)\ndef rle_encode(mask):\n    # Function provided by competition hosts for consistency\n    # ... implementation details here ...\n    pass\n\n# 'predictions' is a dictionary you generate from your model's output\n# where keys are case_ids and values are 'authentic' or RLE strings\npredictions = {\n    '1': 'authentic',\n    '2': '[123 4 56 7]',\n    # ... for all test images ...\n}\n\nsubmission_rows = []\nfor case_id, annotation in predictions.items():\n    submission_rows.append({'case_id': int(case_id), 'annotation': annotation})\n\nsubmission = pd.DataFrame(submission_rows)\n\n# Save the file named 'submission.csv' for Kaggle to pick it up\nsubmission.to_csv('submission.csv', index=False)\nprint(\"✅ Submission saved to submission.csv\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-12-02T09:11:39.808712Z","iopub.execute_input":"2025-12-02T09:11:39.809076Z","iopub.status.idle":"2025-12-02T09:11:39.823166Z","shell.execute_reply.started":"2025-12-02T09:11:39.809057Z","shell.execute_reply":"2025-12-02T09:11:39.822545Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}