{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.12.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceType":"competition","sourceId":112509,"databundleVersionId":14254895}],"dockerImageVersionId":31328,"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":"2026-04-24T12:53:25.109029Z","iopub.execute_input":"2026-04-24T12:53:25.10929Z","iopub.status.idle":"2026-04-24T12:53:25.324799Z","shell.execute_reply.started":"2026-04-24T12:53:25.109266Z","shell.execute_reply":"2026-04-24T12:53:25.324073Z"}},"outputs":[{"name":"stdout","text":"/kaggle/input/competitions/csiro-biomass/sample_submission.csv\n/kaggle/input/competitions/csiro-biomass/train.csv\n/kaggle/input/competitions/csiro-biomass/test.csv\n/kaggle/input/competitions/csiro-biomass/test/ID1001187975.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID2099464826.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID2037861084.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1211362607.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1853508321.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID193102215.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID698608346.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1859251563.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1880764911.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID853954911.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1403107574.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1781353117.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID384648061.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1563418511.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID2125100696.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID482555369.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID638711343.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID779628955.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1876271942.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1692894460.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID746335827.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1136169672.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1471216911.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID846154859.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1294770420.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1183807388.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID423506847.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1889150649.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1140993511.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1413758094.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1545077474.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID95050718.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID528010569.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1645161155.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID786365141.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID896386823.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1025234388.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID663006174.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1509266870.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1496750796.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID471758347.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID740402124.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1624268863.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1098771283.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID710341728.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID2086966681.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1573329652.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID54128926.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID50027657.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1559189397.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID290369222.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1590632667.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID552040066.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID488873801.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID363069566.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1839139621.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1131079710.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID2010625680.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID152157478.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1357758282.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1498398599.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID679913293.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID697718693.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID4464212.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1275072698.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1579942839.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID799079114.jpg\n/kaggle/input/competitions/csiro-biomass/train/ID1415329644.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pandas as pd\n\ntrain_long = pd.read_csv('/kaggle/input/competitions/csiro-biomass/train.csv')\nprint(train_long.shape)\ntrain_long.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T12:53:25.326192Z","iopub.execute_input":"2026-04-24T12:53:25.326422Z","iopub.status.idle":"2026-04-24T12:53:25.34393Z","shell.execute_reply.started":"2026-04-24T12:53:25.326399Z","shell.execute_reply":"2026-04-24T12:53:25.343208Z"}},"outputs":[{"name":"stdout","text":"(1785, 9)\n","output_type":"stream"},{"execution_count":164,"output_type":"execute_result","data":{"text/plain":" sample_id image_path Sampling_Date State \\\n0 ID1011485656__Dry_Clover_g train/ID1011485656.jpg 2015/9/4 Tas \n1 ID1011485656__Dry_Dead_g train/ID1011485656.jpg 2015/9/4 Tas \n2 ID1011485656__Dry_Green_g train/ID1011485656.jpg 2015/9/4 Tas \n3 ID1011485656__Dry_Total_g train/ID1011485656.jpg 2015/9/4 Tas \n4 ID1011485656__GDM_g train/ID1011485656.jpg 2015/9/4 Tas \n\n Species Pre_GSHH_NDVI Height_Ave_cm target_name target \n0 Ryegrass_Clover 0.62 4.6667 Dry_Clover_g 0.0000 \n1 Ryegrass_Clover 0.62 4.6667 Dry_Dead_g 31.9984 \n2 Ryegrass_Clover 0.62 4.6667 Dry_Green_g 16.2751 \n3 Ryegrass_Clover 0.62 4.6667 Dry_Total_g 48.2735 \n4 Ryegrass_Clover 0.62 4.6667 GDM_g 16.2750 ","text/html":"
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sample_idimage_pathSampling_DateStateSpeciesPre_GSHH_NDVIHeight_Ave_cmtarget_nametarget
0ID1011485656__Dry_Clover_gtrain/ID1011485656.jpg2015/9/4TasRyegrass_Clover0.624.6667Dry_Clover_g0.0000
1ID1011485656__Dry_Dead_gtrain/ID1011485656.jpg2015/9/4TasRyegrass_Clover0.624.6667Dry_Dead_g31.9984
2ID1011485656__Dry_Green_gtrain/ID1011485656.jpg2015/9/4TasRyegrass_Clover0.624.6667Dry_Green_g16.2751
3ID1011485656__Dry_Total_gtrain/ID1011485656.jpg2015/9/4TasRyegrass_Clover0.624.6667Dry_Total_g48.2735
4ID1011485656__GDM_gtrain/ID1011485656.jpg2015/9/4TasRyegrass_Clover0.624.6667GDM_g16.2750
\n
"},"metadata":{}}],"execution_count":164},{"cell_type":"code","source":"\nprint(train_long.columns.tolist())\nprint(train_long['target_name'].value_counts())\nprint(train_long.head(10))\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T12:53:25.344845Z","iopub.execute_input":"2026-04-24T12:53:25.345096Z","iopub.status.idle":"2026-04-24T12:53:25.357386Z","shell.execute_reply.started":"2026-04-24T12:53:25.345066Z","shell.execute_reply":"2026-04-24T12:53:25.356766Z"}},"outputs":[{"name":"stdout","text":"['sample_id', 'image_path', 'Sampling_Date', 'State', 'Species', 'Pre_GSHH_NDVI', 'Height_Ave_cm', 'target_name', 'target']\ntarget_name\nDry_Clover_g 357\nDry_Dead_g 357\nDry_Green_g 357\nDry_Total_g 357\nGDM_g 357\nName: count, dtype: int64\n sample_id image_path Sampling_Date State \\\n0 ID1011485656__Dry_Clover_g train/ID1011485656.jpg 2015/9/4 Tas \n1 ID1011485656__Dry_Dead_g train/ID1011485656.jpg 2015/9/4 Tas \n2 ID1011485656__Dry_Green_g train/ID1011485656.jpg 2015/9/4 Tas \n3 ID1011485656__Dry_Total_g train/ID1011485656.jpg 2015/9/4 Tas \n4 ID1011485656__GDM_g train/ID1011485656.jpg 2015/9/4 Tas \n5 ID1012260530__Dry_Clover_g train/ID1012260530.jpg 2015/4/1 NSW \n6 ID1012260530__Dry_Dead_g train/ID1012260530.jpg 2015/4/1 NSW \n7 ID1012260530__Dry_Green_g train/ID1012260530.jpg 2015/4/1 NSW \n8 ID1012260530__Dry_Total_g train/ID1012260530.jpg 2015/4/1 NSW \n9 ID1012260530__GDM_g train/ID1012260530.jpg 2015/4/1 NSW \n\n Species Pre_GSHH_NDVI Height_Ave_cm target_name target \n0 Ryegrass_Clover 0.62 4.6667 Dry_Clover_g 0.0000 \n1 Ryegrass_Clover 0.62 4.6667 Dry_Dead_g 31.9984 \n2 Ryegrass_Clover 0.62 4.6667 Dry_Green_g 16.2751 \n3 Ryegrass_Clover 0.62 4.6667 Dry_Total_g 48.2735 \n4 Ryegrass_Clover 0.62 4.6667 GDM_g 16.2750 \n5 Lucerne 0.55 16.0000 Dry_Clover_g 0.0000 \n6 Lucerne 0.55 16.0000 Dry_Dead_g 0.0000 \n7 Lucerne 0.55 16.0000 Dry_Green_g 7.6000 \n8 Lucerne 0.55 16.0000 Dry_Total_g 7.6000 \n9 Lucerne 0.55 16.0000 GDM_g 7.6000 \n","output_type":"stream"}],"execution_count":165},{"cell_type":"code","source":"\nprint(train_long['image_path'].head(10).tolist())\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T12:53:25.358894Z","iopub.execute_input":"2026-04-24T12:53:25.359091Z","iopub.status.idle":"2026-04-24T12:53:25.37247Z","shell.execute_reply.started":"2026-04-24T12:53:25.35907Z","shell.execute_reply":"2026-04-24T12:53:25.371394Z"}},"outputs":[{"name":"stdout","text":"['train/ID1011485656.jpg', 'train/ID1011485656.jpg', 'train/ID1011485656.jpg', 'train/ID1011485656.jpg', 'train/ID1011485656.jpg', 'train/ID1012260530.jpg', 'train/ID1012260530.jpg', 'train/ID1012260530.jpg', 'train/ID1012260530.jpg', 'train/ID1012260530.jpg']\n","output_type":"stream"}],"execution_count":166},{"cell_type":"code","source":"\nmeta_cols = [\n 'image_path',\n 'Sampling_Date',\n 'State',\n 'Species',\n 'Pre_GSHH_NDVI',\n 'Height_Ave_cm'\n]\n\ndf_wide = train_long.pivot_table(\n index=meta_cols,\n columns='target_name',\n values='target',\n aggfunc='first'\n).reset_index()\n\ndf_wide.columns.name = None\n\nprint(df_wide.shape)\ndf_wide.head()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T12:53:25.373484Z","iopub.execute_input":"2026-04-24T12:53:25.373769Z","iopub.status.idle":"2026-04-24T12:53:25.405676Z","shell.execute_reply.started":"2026-04-24T12:53:25.373746Z","shell.execute_reply":"2026-04-24T12:53:25.405042Z"}},"outputs":[{"name":"stdout","text":"(357, 11)\n","output_type":"stream"},{"execution_count":167,"output_type":"execute_result","data":{"text/plain":" image_path Sampling_Date State Species \\\n0 train/ID1011485656.jpg 2015/9/4 Tas Ryegrass_Clover \n1 train/ID1012260530.jpg 2015/4/1 NSW Lucerne \n2 train/ID1025234388.jpg 2015/9/1 WA SubcloverDalkeith \n3 train/ID1028611175.jpg 2015/5/18 Tas Ryegrass \n4 train/ID1035947949.jpg 2015/9/11 Tas Ryegrass \n\n Pre_GSHH_NDVI Height_Ave_cm Dry_Clover_g Dry_Dead_g Dry_Green_g \\\n0 0.62 4.6667 0.0000 31.9984 16.2751 \n1 0.55 16.0000 0.0000 0.0000 7.6000 \n2 0.38 1.0000 6.0500 0.0000 0.0000 \n3 0.66 5.0000 0.0000 30.9703 24.2376 \n4 0.54 3.5000 0.4343 23.2239 10.5261 \n\n Dry_Total_g GDM_g \n0 48.2735 16.2750 \n1 7.6000 7.6000 \n2 6.0500 6.0500 \n3 55.2079 24.2376 \n4 34.1844 10.9605 ","text/html":"
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image_pathSampling_DateStateSpeciesPre_GSHH_NDVIHeight_Ave_cmDry_Clover_gDry_Dead_gDry_Green_gDry_Total_gGDM_g
0train/ID1011485656.jpg2015/9/4TasRyegrass_Clover0.624.66670.000031.998416.275148.273516.2750
1train/ID1012260530.jpg2015/4/1NSWLucerne0.5516.00000.00000.00007.60007.60007.6000
2train/ID1025234388.jpg2015/9/1WASubcloverDalkeith0.381.00006.05000.00000.00006.05006.0500
3train/ID1028611175.jpg2015/5/18TasRyegrass0.665.00000.000030.970324.237655.207924.2376
4train/ID1035947949.jpg2015/9/11TasRyegrass0.543.50000.434323.223910.526134.184410.9605
\n
"},"metadata":{}}],"execution_count":167},{"cell_type":"code","source":"\nTARGET_COLS = [\n 'Dry_Green_g',\n 'Dry_Dead_g',\n 'Dry_Clover_g',\n 'GDM_g',\n 'Dry_Total_g'\n]\n\nprint(df_wide[TARGET_COLS].head())\nprint(df_wide[TARGET_COLS].isnull().sum())\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T12:53:25.406441Z","iopub.execute_input":"2026-04-24T12:53:25.406705Z","iopub.status.idle":"2026-04-24T12:53:25.414385Z","shell.execute_reply.started":"2026-04-24T12:53:25.406676Z","shell.execute_reply":"2026-04-24T12:53:25.413704Z"}},"outputs":[{"name":"stdout","text":" Dry_Green_g Dry_Dead_g Dry_Clover_g GDM_g Dry_Total_g\n0 16.2751 31.9984 0.0000 16.2750 48.2735\n1 7.6000 0.0000 0.0000 7.6000 7.6000\n2 0.0000 0.0000 6.0500 6.0500 6.0500\n3 24.2376 30.9703 0.0000 24.2376 55.2079\n4 10.5261 23.2239 0.4343 10.9605 34.1844\nDry_Green_g 0\nDry_Dead_g 0\nDry_Clover_g 0\nGDM_g 0\nDry_Total_g 0\ndtype: int64\n","output_type":"stream"}],"execution_count":168},{"cell_type":"code","source":"\nprint(train_long['image_path'].head(10).tolist())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T12:53:25.415194Z","iopub.execute_input":"2026-04-24T12:53:25.415443Z","iopub.status.idle":"2026-04-24T12:53:25.43629Z","shell.execute_reply.started":"2026-04-24T12:53:25.415414Z","shell.execute_reply":"2026-04-24T12:53:25.435647Z"}},"outputs":[{"name":"stdout","text":"['train/ID1011485656.jpg', 'train/ID1011485656.jpg', 'train/ID1011485656.jpg', 'train/ID1011485656.jpg', 'train/ID1011485656.jpg', 'train/ID1012260530.jpg', 'train/ID1012260530.jpg', 'train/ID1012260530.jpg', 'train/ID1012260530.jpg', 'train/ID1012260530.jpg']\n","output_type":"stream"}],"execution_count":169},{"cell_type":"code","source":"\nBASE_IMAGE_DIR = '/kaggle/input/competitions/csiro-biomass'\ndf_wide['image_path'] = df_wide['image_path'].apply(lambda x: os.path.join(BASE_IMAGE_DIR, x))\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T12:53:25.437113Z","iopub.execute_input":"2026-04-24T12:53:25.437302Z","iopub.status.idle":"2026-04-24T12:53:25.454347Z","shell.execute_reply.started":"2026-04-24T12:53:25.437281Z","shell.execute_reply":"2026-04-24T12:53:25.453609Z"}},"outputs":[],"execution_count":170},{"cell_type":"code","source":"\nfrom PIL import Image\n\nsample_path = df_wide.iloc[0]['image_path']\nprint(sample_path)\n\nimg = Image.open(sample_path)\nprint(img.size, img.mode)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T12:53:25.455129Z","iopub.execute_input":"2026-04-24T12:53:25.45533Z","iopub.status.idle":"2026-04-24T12:53:25.476364Z","shell.execute_reply.started":"2026-04-24T12:53:25.455307Z","shell.execute_reply":"2026-04-24T12:53:25.475261Z"}},"outputs":[{"name":"stdout","text":"/kaggle/input/competitions/csiro-biomass/train/ID1011485656.jpg\n(2000, 1000) RGB\n","output_type":"stream"}],"execution_count":171},{"cell_type":"code","source":"\n!pip install timm albumentations --quiet\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T12:53:25.479254Z","iopub.execute_input":"2026-04-24T12:53:25.479536Z","iopub.status.idle":"2026-04-24T12:53:28.564927Z","shell.execute_reply.started":"2026-04-24T12:53:25.479515Z","shell.execute_reply":"2026-04-24T12:53:28.564151Z"}},"outputs":[],"execution_count":172},{"cell_type":"code","source":"\nimport random\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import Dataset, DataLoader\n\nfrom sklearn.model_selection import train_test_split\n\nimport timm\n\nimport albumentations as A\nfrom albumentations.pytorch import ToTensorV2\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T12:53:28.565873Z","iopub.execute_input":"2026-04-24T12:53:28.566057Z","iopub.status.idle":"2026-04-24T12:53:28.570779Z","shell.execute_reply.started":"2026-04-24T12:53:28.566034Z","shell.execute_reply":"2026-04-24T12:53:28.56994Z"}},"outputs":[],"execution_count":173},{"cell_type":"code","source":"\ndef seed_everything(seed=42):\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n torch.cuda.manual_seed_all(seed)\n\nseed_everything(42)\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nprint(device)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T12:53:28.571739Z","iopub.execute_input":"2026-04-24T12:53:28.571957Z","iopub.status.idle":"2026-04-24T12:53:28.591446Z","shell.execute_reply.started":"2026-04-24T12:53:28.571934Z","shell.execute_reply":"2026-04-24T12:53:28.590734Z"}},"outputs":[{"name":"stdout","text":"cpu\n","output_type":"stream"}],"execution_count":174},{"cell_type":"code","source":"\nTARGET_WEIGHTS = np.array([0.1, 0.1, 0.1, 0.2, 0.5], dtype=np.float32)\n\nIMG_SIZE = 224\nBATCH_SIZE = 32\nLR = 1e-4\nEPOCHS = 5\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T12:53:28.592385Z","iopub.execute_input":"2026-04-24T12:53:28.592669Z","iopub.status.idle":"2026-04-24T12:53:28.6095Z","shell.execute_reply.started":"2026-04-24T12:53:28.592622Z","shell.execute_reply":"2026-04-24T12:53:28.608805Z"}},"outputs":[],"execution_count":175},{"cell_type":"code","source":"\ntrain_df, val_df = train_test_split(\n df_wide,\n test_size=0.2,\n random_state=42\n)\n\nprint('train:', len(train_df))\nprint('val:', len(val_df))\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T12:53:28.610377Z","iopub.execute_input":"2026-04-24T12:53:28.610588Z","iopub.status.idle":"2026-04-24T12:53:28.63202Z","shell.execute_reply.started":"2026-04-24T12:53:28.610567Z","shell.execute_reply":"2026-04-24T12:53:28.630979Z"}},"outputs":[{"name":"stdout","text":"train: 285\nval: 72\n","output_type":"stream"}],"execution_count":176},{"cell_type":"code","source":"train_transforms = A.Compose([\n A.Resize(IMG_SIZE, IMG_SIZE),\n A.HorizontalFlip(p=0.5),\n A.VerticalFlip(p=0.5),\n A.RandomRotate90(p=0.5),\n A.RandomBrightnessContrast(p=0.3),\n A.Normalize(mean=(0.485, 0.456, 0.406),\n std=(0.229, 0.224, 0.225)),\n ToTensorV2(),\n])\n\nval_transforms = A.Compose([\n A.Resize(IMG_SIZE, IMG_SIZE),\n A.Normalize(mean=(0.485, 0.456, 0.406),\n std=(0.229, 0.224, 0.225)),\n ToTensorV2(),\n])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T12:53:28.632959Z","iopub.execute_input":"2026-04-24T12:53:28.633191Z","iopub.status.idle":"2026-04-24T12:53:28.653588Z","shell.execute_reply.started":"2026-04-24T12:53:28.633159Z","shell.execute_reply":"2026-04-24T12:53:28.652706Z"}},"outputs":[],"execution_count":177},{"cell_type":"code","source":"class BiomassDataset(Dataset):\n def __init__(self, df, transforms=None):\n self.df = df.reset_index(drop=True)\n self.transforms = transforms\n\n def __len__(self):\n return len(self.df)\n\n def __getitem__(self, idx):\n row = self.df.iloc[idx]\n\n image = np.array(Image.open(row['image_path']).convert('RGB'))\n\n if self.transforms is not None:\n image = self.transforms(image=image)['image']\n\n targets = torch.tensor(\n np.log1p(row[TARGET_COLS].values.astype(np.float32)),\n dtype=torch.float32\n )\n\n return image, targets","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T12:53:28.654505Z","iopub.execute_input":"2026-04-24T12:53:28.654783Z","iopub.status.idle":"2026-04-24T12:53:28.672342Z","shell.execute_reply.started":"2026-04-24T12:53:28.654755Z","shell.execute_reply":"2026-04-24T12:53:28.671104Z"}},"outputs":[],"execution_count":178},{"cell_type":"code","source":"train_dataset = BiomassDataset(train_df, transforms=train_transforms)\nval_dataset = BiomassDataset(val_df, transforms=val_transforms)\n\ntrain_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2)\nval_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=2)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T12:53:28.673347Z","iopub.execute_input":"2026-04-24T12:53:28.673594Z","iopub.status.idle":"2026-04-24T12:53:28.710231Z","shell.execute_reply.started":"2026-04-24T12:53:28.673567Z","shell.execute_reply":"2026-04-24T12:53:28.709396Z"}},"outputs":[],"execution_count":179},{"cell_type":"code","source":"images, targets = next(iter(train_loader))\nprint(images.shape)\nprint(targets.shape)\nprint(targets[:2])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T12:53:28.71097Z","iopub.execute_input":"2026-04-24T12:53:28.71113Z","iopub.status.idle":"2026-04-24T12:53:31.425075Z","shell.execute_reply.started":"2026-04-24T12:53:28.711111Z","shell.execute_reply":"2026-04-24T12:53:31.424077Z"}},"outputs":[{"name":"stdout","text":"torch.Size([32, 3, 224, 224])\ntorch.Size([32, 5])\ntensor([[3.5134, 2.9252, 0.0000, 3.5134, 3.9357],\n [3.0477, 1.3524, 1.3524, 3.1753, 3.2884]])\n","output_type":"stream"}],"execution_count":180},{"cell_type":"code","source":"model = timm.create_model(\n 'resnet18',\n pretrained=True,\n num_classes=5\n)\n\nmodel = model.to(device)\nprint(type(model))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T12:53:31.426196Z","iopub.execute_input":"2026-04-24T12:53:31.426426Z","iopub.status.idle":"2026-04-24T12:53:31.777781Z","shell.execute_reply.started":"2026-04-24T12:53:31.426401Z","shell.execute_reply":"2026-04-24T12:53:31.776901Z"}},"outputs":[{"name":"stdout","text":"\n","output_type":"stream"}],"execution_count":181},{"cell_type":"code","source":"class WeightedSmoothL1Loss(nn.Module):\n def __init__(self, weights):\n super().__init__()\n self.weights = torch.tensor(weights, dtype=torch.float32)\n self.loss_fn = nn.SmoothL1Loss(reduction='none')\n\n def forward(self, preds, targets):\n weights = self.weights.to(preds.device)\n loss = self.loss_fn(preds, targets)\n loss = loss * weights\n return loss.mean()\n\n\n\ncriterion = WeightedSmoothL1Loss(TARGET_WEIGHTS)\noptimizer = torch.optim.AdamW(model.parameters(), lr=LR, weight_decay=1e-4)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T12:53:31.77862Z","iopub.execute_input":"2026-04-24T12:53:31.778859Z","iopub.status.idle":"2026-04-24T12:53:31.798052Z","shell.execute_reply.started":"2026-04-24T12:53:31.778837Z","shell.execute_reply":"2026-04-24T12:53:31.797179Z"}},"outputs":[],"execution_count":182},{"cell_type":"code","source":"def weighted_r2_score(y_true, y_pred, weights):\n y_true = np.asarray(y_true)\n y_pred = np.asarray(y_pred)\n weights = np.asarray(weights)\n\n y_true_flat = y_true.reshape(-1)\n y_pred_flat = y_pred.reshape(-1)\n repeated_weights = np.tile(weights, y_true.shape[0])\n\n y_mean = np.sum(repeated_weights * y_true_flat) / np.sum(repeated_weights)\n ss_res = np.sum(repeated_weights * (y_true_flat - y_pred_flat) ** 2)\n ss_tot = np.sum(repeated_weights * (y_true_flat - y_mean) ** 2)\n\n return 1.0 - ss_res / ss_tot","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T12:53:31.799489Z","iopub.execute_input":"2026-04-24T12:53:31.799867Z","iopub.status.idle":"2026-04-24T12:53:31.804989Z","shell.execute_reply.started":"2026-04-24T12:53:31.79983Z","shell.execute_reply":"2026-04-24T12:53:31.804373Z"}},"outputs":[],"execution_count":183},{"cell_type":"code","source":"def train_one_epoch(model, loader, optimizer, criterion, device):\n model.train()\n running_loss = 0.0\n\n for images, targets in loader:\n images = images.to(device)\n targets = targets.to(device)\n\n optimizer.zero_grad()\n preds = model(images)\n loss = criterion(preds, targets)\n loss.backward()\n optimizer.step()\n\n running_loss += loss.item() * images.size(0)\n\n return running_loss / len(loader.dataset)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T12:53:31.805765Z","iopub.execute_input":"2026-04-24T12:53:31.805972Z","iopub.status.idle":"2026-04-24T12:53:31.825965Z","shell.execute_reply.started":"2026-04-24T12:53:31.805952Z","shell.execute_reply":"2026-04-24T12:53:31.824985Z"}},"outputs":[],"execution_count":184},{"cell_type":"code","source":"\ndef validate(model, loader, criterion, device, weights):\n model.eval()\n running_loss = 0.0\n all_targets = []\n all_preds = []\n\n with torch.no_grad():\n for images, targets in loader:\n images = images.to(device)\n targets = targets.to(device)\n\n preds = model(images)\n loss = criterion(preds, targets)\n\n running_loss += loss.item() * images.size(0)\n\n preds_real = torch.expm1(preds)\n targets_real = torch.expm1(targets)\n\n all_targets.append(targets_real.cpu().numpy())\n all_preds.append(preds_real.cpu().numpy())\n\n val_loss = running_loss / len(loader.dataset)\n all_targets = np.concatenate(all_targets, axis=0)\n all_preds = np.concatenate(all_preds, axis=0)\n\n val_r2 = weighted_r2_score(all_targets, all_preds, weights)\n\n return val_loss, val_r2\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T12:53:31.82698Z","iopub.execute_input":"2026-04-24T12:53:31.82725Z","iopub.status.idle":"2026-04-24T12:53:31.837762Z","shell.execute_reply.started":"2026-04-24T12:53:31.827223Z","shell.execute_reply":"2026-04-24T12:53:31.836962Z"}},"outputs":[],"execution_count":185},{"cell_type":"code","source":"history = []\nbest_r2 = -999\n\nfor epoch in range(EPOCHS):\n train_loss = train_one_epoch(model, train_loader, optimizer, criterion, device)\n val_loss, val_r2 = validate(model, val_loader, criterion, device, TARGET_WEIGHTS)\n\n history.append({\n 'epoch': epoch + 1,\n 'train_loss': train_loss,\n 'val_loss': val_loss,\n 'val_weighted_r2': val_r2\n })\n\n print(f'Epoch {epoch+1}/{EPOCHS}')\n print(f'train_loss = {train_loss:.5f}')\n print(f'val_loss = {val_loss:.5f}')\n print(f'val_wR2 = {val_r2:.5f}')\n print('------------------------------')\n\n if val_r2 > best_r2:\n best_r2 = val_r2\n torch.save(model.state_dict(), 'best_resnet18_baseline.pth')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T12:53:31.839037Z","iopub.execute_input":"2026-04-24T12:53:31.839352Z","iopub.status.idle":"2026-04-24T12:56:22.004533Z","shell.execute_reply.started":"2026-04-24T12:53:31.839319Z","shell.execute_reply":"2026-04-24T12:56:22.003731Z"}},"outputs":[{"name":"stdout","text":"Epoch 1/5\ntrain_loss = 0.51999\nval_loss = 0.51554\nval_wR2 = -1.64890\n------------------------------\nEpoch 2/5\ntrain_loss = 0.49857\nval_loss = 0.49429\nval_wR2 = -1.63489\n------------------------------\nEpoch 3/5\ntrain_loss = 0.47728\nval_loss = 0.47094\nval_wR2 = -1.61827\n------------------------------\nEpoch 4/5\ntrain_loss = 0.45410\nval_loss = 0.44217\nval_wR2 = -1.59446\n------------------------------\nEpoch 5/5\ntrain_loss = 0.42935\nval_loss = 0.40797\nval_wR2 = -1.56032\n------------------------------\n","output_type":"stream"}],"execution_count":186},{"cell_type":"code","source":"\nhistory_df = pd.DataFrame(history)\nhistory_df.to_csv('resnet18_baseline_history.csv', index=False)\nprint(history_df)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T12:56:22.005558Z","iopub.execute_input":"2026-04-24T12:56:22.005862Z","iopub.status.idle":"2026-04-24T12:56:22.01424Z","shell.execute_reply.started":"2026-04-24T12:56:22.005828Z","shell.execute_reply":"2026-04-24T12:56:22.013479Z"}},"outputs":[{"name":"stdout","text":" epoch train_loss val_loss val_weighted_r2\n0 1 0.519988 0.515537 -1.648904\n1 2 0.498565 0.494286 -1.634895\n2 3 0.477281 0.470937 -1.618271\n3 4 0.454104 0.442166 -1.594463\n4 5 0.429346 0.407972 -1.560322\n","output_type":"stream"}],"execution_count":187},{"cell_type":"code","source":"\nprint(df_wide[TARGET_COLS].describe())\n\nprint(df_wide[TARGET_COLS].min())\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T12:56:22.017087Z","iopub.execute_input":"2026-04-24T12:56:22.017319Z","iopub.status.idle":"2026-04-24T12:56:22.043255Z","shell.execute_reply.started":"2026-04-24T12:56:22.017289Z","shell.execute_reply":"2026-04-24T12:56:22.042229Z"}},"outputs":[{"name":"stdout","text":" Dry_Green_g Dry_Dead_g Dry_Clover_g GDM_g Dry_Total_g\ncount 357.000000 357.000000 357.000000 357.000000 357.000000\nmean 26.624722 12.044548 6.649692 33.274414 45.318097\nstd 25.401232 12.402007 12.117761 24.935822 27.984015\nmin 0.000000 0.000000 0.000000 1.040000 1.040000\n25% 8.800000 3.200000 0.000000 16.026100 25.271500\n50% 20.800000 7.980900 1.423500 27.108200 40.300000\n75% 35.083400 17.637800 7.242900 43.675700 57.880000\nmax 157.983600 83.840700 71.786500 157.983600 185.700000\nDry_Green_g 0.00\nDry_Dead_g 0.00\nDry_Clover_g 0.00\nGDM_g 1.04\nDry_Total_g 1.04\ndtype: float64\n","output_type":"stream"}],"execution_count":188},{"cell_type":"code","source":"mean_targets = train_df[TARGET_COLS].mean().values\nval_true = val_df[TARGET_COLS].values\nval_pred_mean = np.tile(mean_targets, (len(val_df), 1))\n\nbaseline_r2 = weighted_r2_score(val_true, val_pred_mean, TARGET_WEIGHTS)\nprint(\"Mean baseline weighted R2:\", baseline_r2)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T12:56:22.044114Z","iopub.execute_input":"2026-04-24T12:56:22.044357Z","iopub.status.idle":"2026-04-24T12:56:22.051256Z","shell.execute_reply.started":"2026-04-24T12:56:22.044335Z","shell.execute_reply":"2026-04-24T12:56:22.050482Z"}},"outputs":[{"name":"stdout","text":"Mean baseline weighted R2: 0.2591984593284523\n","output_type":"stream"}],"execution_count":189},{"cell_type":"code","source":"\nprint(\"unique image_path:\", df_wide['image_path'].nunique())\nprint(\"rows in df_wide:\", len(df_wide))\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-24T12:56:22.052565Z","iopub.execute_input":"2026-04-24T12:56:22.052778Z","iopub.status.idle":"2026-04-24T12:56:22.070473Z","shell.execute_reply.started":"2026-04-24T12:56:22.052759Z","shell.execute_reply":"2026-04-24T12:56:22.069784Z"}},"outputs":[{"name":"stdout","text":"unique image_path: 357\nrows in df_wide: 357\n","output_type":"stream"}],"execution_count":190}]}