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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 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-blindness-detection/test_images/e177610cf7a1.png\n/kaggle/input/competitions/aptos2019-blindness-detection/test_images/246efa653ba4.png\n/kaggle/input/competitions/aptos2019-blindness-detection/test_images/45f34a152021.png\n/kaggle/input/competitions/aptos2019-blindness-detection/test_images/427110b0a05e.png\n/kaggle/input/competitions/aptos2019-blindness-detection/test_images/cd45c48db97d.png\n/kaggle/input/competitions/aptos2019-blindness-detection/test_images/7eeed2e26c9f.png\n/kaggle/input/competitions/aptos2019-blindness-detection/test_images/fa09c97a2887.png\n/kaggle/input/competitions/aptos2019-blindness-detection/test_images/995eb7091756.png\n","output_type":"stream"}],"execution_count":2},{"cell_type":"code","source":"import os\nimport pandas as pd\n\nDATA_DIR = '/kaggle/input/competitions/aptos2019-blindness-detection'\ndf = pd.read_csv(f'{DATA_DIR}/train.csv')\nprint(df.head())\nprint(df['diagnosis'].value_counts())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-29T14:30:57.478402Z","iopub.execute_input":"2026-03-29T14:30:57.478614Z","iopub.status.idle":"2026-03-29T14:30:57.508512Z","shell.execute_reply.started":"2026-03-29T14:30:57.478595Z","shell.execute_reply":"2026-03-29T14:30:57.507718Z"}},"outputs":[{"name":"stdout","text":"        id_code  diagnosis\n0  000c1434d8d7          2\n1  001639a390f0          4\n2  0024cdab0c1e          1\n3  002c21358ce6          0\n4  005b95c28852          0\ndiagnosis\n0    1805\n2     999\n1     370\n4     295\n3     193\nName: count, dtype: int64\n","output_type":"stream"}],"execution_count":3},{"cell_type":"code","source":"import os\nimport pandas as pd\n\nDATA_DIR = '/kaggle/input/competitions/aptos2019-blindness-detection'\ndf = pd.read_csv(f'{DATA_DIR}/train.csv')\n\n\n\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision import transforms, models\nfrom PIL import Image\nfrom sklearn.model_selection import train_test_split\n\n# Dataset class\nclass DRDataset(Dataset):\n    def __init__(self, df, img_dir, transform=None):\n        self.df = df.reset_index(drop=True)\n        self.img_dir = img_dir\n        self.transform = transform\n\n    def __len__(self):\n        return len(self.df)\n\n    def __getitem__(self, idx):\n        img_path = f\"{self.img_dir}/{self.df['id_code'][idx]}.png\"\n        img = Image.open(img_path).convert('RGB')\n        if self.transform:\n            img = self.transform(img)\n        label = self.df['diagnosis'][idx]\n        return img, label\n\n# Transforms\ntrain_tfm = transforms.Compose([\n    transforms.Resize((224, 224)),\n    transforms.RandomHorizontalFlip(),\n    transforms.RandomRotation(10),\n    transforms.ToTensor(),\n    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n])\nval_tfm = transforms.Compose([\n    transforms.Resize((224, 224)),\n    transforms.ToTensor(),\n    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n])\n\n# Split\ntrain_df, val_df = train_test_split(df, test_size=0.2, stratify=df['diagnosis'], random_state=42)\n\nIMG_DIR = f'{DATA_DIR}/train_images'\ntrain_ds = DRDataset(train_df, IMG_DIR, train_tfm)\nval_ds   = DRDataset(val_df,   IMG_DIR, val_tfm)\ntrain_dl = DataLoader(train_ds, batch_size=32, shuffle=True,  num_workers=2)\nval_dl   = DataLoader(val_ds,   batch_size=32, shuffle=False, num_workers=2)\n\n# Model\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nmodel = models.mobilenet_v2(weights='DEFAULT')\nmodel.classifier[1] = nn.Linear(model.last_channel, 5)\nmodel = model.to(device)\n\n# Training setup\ncriterion = nn.CrossEntropyLoss()\noptimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\n\n# Train loop\nfor epoch in range(5):\n    model.train()\n    total_loss, correct = 0, 0\n    for imgs, labels in train_dl:\n        imgs, labels = imgs.to(device), labels.to(device)\n        optimizer.zero_grad()\n        out = model(imgs)\n        loss = criterion(out, labels)\n        loss.backward()\n        optimizer.step()\n        total_loss += loss.item()\n        correct += (out.argmax(1) == labels).sum().item()\n\n    # Validation\n    model.eval()\n    val_correct = 0\n    with torch.no_grad():\n        for imgs, labels in val_dl:\n            imgs, labels = imgs.to(device), labels.to(device)\n            out = model(imgs)\n            val_correct += (out.argmax(1) == labels).sum().item()\n\n    print(f\"Epoch {epoch+1} | Loss: {total_loss/len(train_dl):.4f} | Train Acc: {correct/len(train_ds):.4f} | Val Acc: {val_correct/len(val_ds):.4f}\")\n\n# Save model\ntorch.save(model.state_dict(), 'mobilenetv2_dr.pth')\nprint(\"Model saved!\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-29T14:30:57.510291Z","iopub.execute_input":"2026-03-29T14:30:57.510644Z","iopub.status.idle":"2026-03-29T14:50:29.039679Z","shell.execute_reply.started":"2026-03-29T14:30:57.51062Z","shell.execute_reply":"2026-03-29T14:50:29.038845Z"}},"outputs":[{"name":"stdout","text":"Downloading: \"https://download.pytorch.org/models/mobilenet_v2-7ebf99e0.pth\" to /root/.cache/torch/hub/checkpoints/mobilenet_v2-7ebf99e0.pth\n","output_type":"stream"},{"name":"stderr","text":"100%|██████████| 13.6M/13.6M [00:00<00:00, 141MB/s]\n","output_type":"stream"},{"name":"stdout","text":"Epoch 1 | Loss: 0.9433 | Train Acc: 0.6811 | Val Acc: 0.7490\nEpoch 2 | Loss: 0.5911 | Train Acc: 0.7777 | Val Acc: 0.7831\nEpoch 3 | Loss: 0.4907 | Train Acc: 0.8194 | Val Acc: 0.7926\nEpoch 4 | Loss: 0.4353 | Train Acc: 0.8337 | Val Acc: 0.7981\nEpoch 5 | Loss: 0.3883 | Train Acc: 0.8525 | Val Acc: 0.8240\nModel saved!\n","output_type":"stream"}],"execution_count":4},{"cell_type":"code","source":"from IPython.display import FileLink\nFileLink('mobilenetv2_dr.pth')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-29T14:50:29.041132Z","iopub.execute_input":"2026-03-29T14:50:29.041758Z","iopub.status.idle":"2026-03-29T14:50:29.047887Z","shell.execute_reply.started":"2026-03-29T14:50:29.041725Z","shell.execute_reply":"2026-03-29T14:50:29.047213Z"}},"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"/kaggle/working/mobilenetv2_dr.pth","text/html":"<a href='mobilenetv2_dr.pth' target='_blank'>mobilenetv2_dr.pth</a><br>"},"metadata":{}}],"execution_count":5},{"cell_type":"code","source":"from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score\nimport numpy as np\n\nmodel.eval()\nall_preds, all_labels, all_probs = [], [], []\n\nwith torch.no_grad():\n    for imgs, labels in val_dl:\n        imgs = imgs.to(device)\n        out = model(imgs)\n        probs = torch.softmax(out, dim=1)\n        preds = probs.argmax(1).cpu().numpy()\n        all_preds.extend(preds)\n        all_labels.extend(labels.numpy())\n        all_probs.extend(probs.cpu().numpy())\n\nall_preds  = np.array(all_preds)\nall_labels = np.array(all_labels)\nall_probs  = np.array(all_probs)\n\nacc  = accuracy_score(all_labels, all_preds)\nprec = precision_score(all_labels, all_preds, average='macro', zero_division=0)\nrec  = recall_score(all_labels, all_preds, average='macro', zero_division=0)\nf1   = f1_score(all_labels, all_preds, average='macro', zero_division=0)\nauc  = roc_auc_score(all_labels, all_probs, multi_class='ovr', average='macro')\n\nprint(f\"Accuracy  : {acc:.4f}\")\nprint(f\"Precision : {prec:.4f}\")\nprint(f\"Recall    : {rec:.4f}\")\nprint(f\"F1 Score  : {f1:.4f}\")\nprint(f\"AUC       : {auc:.4f}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-29T14:50:29.049604Z","iopub.execute_input":"2026-03-29T14:50:29.05034Z","iopub.status.idle":"2026-03-29T14:51:15.073109Z","shell.execute_reply.started":"2026-03-29T14:50:29.050314Z","shell.execute_reply":"2026-03-29T14:51:15.072001Z"}},"outputs":[{"name":"stdout","text":"Accuracy  : 0.8240\nPrecision : 0.7186\nRecall    : 0.6245\nF1 Score  : 0.6536\nAUC       : 0.9391\n","output_type":"stream"}],"execution_count":6},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nfrom sklearn.metrics import roc_curve, auc\nfrom sklearn.preprocessing import label_binarize\n\n# Binarize labels\nclasses = [0, 1, 2, 3, 4]\ny_bin = label_binarize(all_labels, classes=classes)\n\ngrade_names = [\"No DR\", \"Mild\", \"Moderate\", \"Severe\", \"Proliferative DR\"]\ncolors = [\"#2ecc71\", \"#3498db\", \"#e67e22\", \"#e74c3c\", \"#9b59b6\"]\n\nplt.figure(figsize=(10, 7))\n\nfor i in range(5):\n    fpr, tpr, _ = roc_curve(y_bin[:, i], all_probs[:, i])\n    roc_auc = auc(fpr, tpr)\n    plt.plot(fpr, tpr, color=colors[i], lw=2,\n             label=f\"{grade_names[i]} (AUC = {roc_auc:.4f})\")\n\nplt.plot([0, 1], [0, 1], 'k--', lw=1.5, label=\"Random Classifier\")\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.05])\nplt.xlabel(\"False Positive Rate\", fontsize=13)\nplt.ylabel(\"True Positive Rate\", fontsize=13)\nplt.title(\"ROC Curve — MobileNetV2 (DR Grading)\", fontsize=15)\nplt.legend(loc=\"lower right\", fontsize=11)\nplt.grid(alpha=0.3)\nplt.tight_layout()\nplt.savefig(\"mobilenet_roc_curve.png\", dpi=150)\nplt.show()\nprint(\"Saved!\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-29T15:11:49.166966Z","iopub.execute_input":"2026-03-29T15:11:49.167906Z","iopub.status.idle":"2026-03-29T15:11:49.871743Z","shell.execute_reply.started":"2026-03-29T15:11:49.167868Z","shell.execute_reply":"2026-03-29T15:11:49.870761Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x700 with 1 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"Saved!\n","output_type":"stream"}],"execution_count":7},{"cell_type":"code","source":"from IPython.display import FileLink\nFileLink('mobilenet_roc_curve.png')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-29T15:13:24.229358Z","iopub.execute_input":"2026-03-29T15:13:24.229926Z","iopub.status.idle":"2026-03-29T15:13:24.235326Z","shell.execute_reply.started":"2026-03-29T15:13:24.229893Z","shell.execute_reply":"2026-03-29T15:13:24.234786Z"}},"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"/kaggle/working/mobilenet_roc_curve.png","text/html":"<a href='mobilenet_roc_curve.png' target='_blank'>mobilenet_roc_curve.png</a><br>"},"metadata":{}}],"execution_count":8}]}