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Jaguar Re-Identification: MegaDescriptor with Memory-Efficient Training\n\n**Competition:** Jaguar Re-Identification Challenge  \n**Objective:** Identify individual jaguars from wildlife photographs  \n**Approach:** Memory-efficient metric learning optimized for P100 GPU\n\n---\n\n## Table of Contents\n\n1. [Problem Statement and Methodology](#1-problem-statement-and-methodology)\n2. [Environment Configuration](#2-environment-configuration)\n3. [Data Analysis and Preprocessing](#3-data-analysis-and-preprocessing)\n4. [Model Architecture and Training](#4-model-architecture-and-training)\n5. [Inference and Submission Generation](#5-inference-and-submission-generation)\n\n---","metadata":{"papermill":{"duration":0.004843,"end_time":"2026-01-24T02:52:44.531656","exception":false,"start_time":"2026-01-24T02:52:44.526813","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"## 1. Problem Statement and Methodology\n\n### Technical Approach\n\n| Stage | Component | Purpose |\n|-------|-----------|--------|\n| Feature Extraction | MegaDescriptor-B-224 | Memory-efficient wildlife embeddings |\n| Metric Learning | ArcFace with Sub-center | Angular margin separation |\n| Background Suppression | Alpha Channel Masking | Remove spurious correlations |\n| Inference Enhancement | Test-Time Augmentation | Embedding robustness |\n| Post-Processing | k-Reciprocal Re-ranking | Gallery consistency refinement |\n\n---","metadata":{"papermill":{"duration":0.003654,"end_time":"2026-01-24T02:52:44.539223","exception":false,"start_time":"2026-01-24T02:52:44.535569","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"## 2. Environment Configuration","metadata":{"papermill":{"duration":0.003746,"end_time":"2026-01-24T02:52:44.546685","exception":false,"start_time":"2026-01-24T02:52:44.542939","status":"completed"},"tags":[]}},{"cell_type":"code","source":"import subprocess\nimport sys\nimport os\n\nos.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n\ndef install_package(package):\n    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", package])\n\ninstall_package(\"timm>=0.9.0\")\ninstall_package(\"albumentations>=1.3.0\")\ninstall_package(\"scikit-learn>=1.0.0\")\n\nprint(\"Package installation complete.\")","metadata":{"execution":{"iopub.execute_input":"2026-01-24T02:52:44.555754Z","iopub.status.busy":"2026-01-24T02:52:44.55516Z","iopub.status.idle":"2026-01-24T02:52:55.627045Z","shell.execute_reply":"2026-01-24T02:52:55.62631Z"},"papermill":{"duration":11.078413,"end_time":"2026-01-24T02:52:55.628772","exception":false,"start_time":"2026-01-24T02:52:44.550359","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import gc\nimport random\nimport warnings\nfrom pathlib import Path\nfrom dataclasses import dataclass\nfrom typing import Dict, List, Tuple, Optional\n\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import Dataset, DataLoader, WeightedRandomSampler\nfrom torch.cuda.amp import GradScaler, autocast\nfrom torch.utils.checkpoint import checkpoint\nfrom PIL import Image\nimport albumentations as A\nfrom albumentations.pytorch import ToTensorV2\nimport timm\nfrom tqdm.auto import tqdm\nfrom sklearn.preprocessing import LabelEncoder\n\nwarnings.filterwarnings(\"ignore\")\nos.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n\nprint(f\"PyTorch Version: {torch.__version__}\")\nprint(f\"CUDA Available: {torch.cuda.is_available()}\")\nif torch.cuda.is_available():\n    print(f\"GPU Device: {torch.cuda.get_device_name(0)}\")\n    print(f\"GPU Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB\")\n    torch.cuda.empty_cache()\n    gc.collect()","metadata":{"execution":{"iopub.execute_input":"2026-01-24T02:52:55.638173Z","iopub.status.busy":"2026-01-24T02:52:55.637919Z","iopub.status.idle":"2026-01-24T02:53:17.761506Z","shell.execute_reply":"2026-01-24T02:53:17.760616Z"},"papermill":{"duration":22.130015,"end_time":"2026-01-24T02:53:17.763053","exception":false,"start_time":"2026-01-24T02:52:55.633038","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"@dataclass\nclass Config:\n    seed: int = 42\n    data_root: Path = Path(\"/kaggle/input/jaguar-re-id\")\n    train_dir: Path = Path(\"/kaggle/input/jaguar-re-id/train/train\")\n    test_dir: Path = Path(\"/kaggle/input/jaguar-re-id/test/test\")\n    train_csv: Path = Path(\"/kaggle/input/jaguar-re-id/train.csv\")\n    test_csv: Path = Path(\"/kaggle/input/jaguar-re-id/test.csv\")\n    output_dir: Path = Path(\"/kaggle/working\")\n    \n    backbone: str = \"hf-hub:BVRA/MegaDescriptor-B-224\"\n    image_size: int = 224\n    embedding_dim: int = 768\n    num_classes: int = 31\n    \n    epochs: int = 25\n    batch_size: int = 8\n    num_workers: int = 2\n    learning_rate: float = 2e-4\n    weight_decay: float = 1e-4\n    warmup_epochs: int = 2\n    \n    arcface_scale: float = 30.0\n    arcface_margin: float = 0.5\n    num_subcenters: int = 2\n    \n    use_amp: bool = True\n    gradient_accumulation: int = 4\n    max_grad_norm: float = 1.0\n    gradient_checkpointing: bool = True\n    \n    use_alpha_mask: bool = True\n    tta_transforms: int = 4\n    rerank_k1: int = 15\n    rerank_k2: int = 4\n    rerank_lambda: float = 0.3\n\ncfg = Config()\n\ndef set_seed(seed: int):\n    random.seed(seed)\n    os.environ[\"PYTHONHASHSEED\"] = str(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n    torch.cuda.manual_seed(seed)\n    torch.cuda.manual_seed_all(seed)\n    torch.backends.cudnn.deterministic = True\n    torch.backends.cudnn.benchmark = False\n\nset_seed(cfg.seed)\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\nprint(\"\\nConfiguration Summary (Memory-Optimized):\")\nprint(f\"{'='*50}\")\nprint(f\"Backbone: {cfg.backbone}\")\nprint(f\"Image Size: {cfg.image_size}x{cfg.image_size}\")\nprint(f\"Embedding Dimension: {cfg.embedding_dim}\")\nprint(f\"Batch Size: {cfg.batch_size}\")\nprint(f\"Gradient Accumulation: {cfg.gradient_accumulation}\")\nprint(f\"Effective Batch Size: {cfg.batch_size * cfg.gradient_accumulation}\")\nprint(f\"Epochs: {cfg.epochs}\")\nprint(f\"Gradient Checkpointing: {cfg.gradient_checkpointing}\")\nprint(f\"Device: {device}\")\nprint(f\"{'='*50}\")","metadata":{"execution":{"iopub.execute_input":"2026-01-24T02:53:17.772728Z","iopub.status.busy":"2026-01-24T02:53:17.772277Z","iopub.status.idle":"2026-01-24T02:53:17.79072Z","shell.execute_reply":"2026-01-24T02:53:17.789846Z"},"papermill":{"duration":0.02462,"end_time":"2026-01-24T02:53:17.79201","exception":false,"start_time":"2026-01-24T02:53:17.76739","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## 3. Data Analysis and Preprocessing","metadata":{"papermill":{"duration":0.004017,"end_time":"2026-01-24T02:53:17.801107","exception":false,"start_time":"2026-01-24T02:53:17.79709","status":"completed"},"tags":[]}},{"cell_type":"code","source":"train_df = pd.read_csv(cfg.train_csv)\ntest_df = pd.read_csv(cfg.test_csv)\n\nprint(\"Dataset Statistics\")\nprint(f\"{'='*50}\")\nprint(f\"Training Samples: {len(train_df):,}\")\nprint(f\"Test Pairs: {len(test_df):,}\")\nprint(f\"Unique Individuals: {train_df['ground_truth'].nunique()}\")\nprint(f\"\\nTraining Data Sample:\")\nprint(train_df.head(10).to_string())\nprint(f\"\\nTest Data Sample:\")\nprint(test_df.head(10).to_string())","metadata":{"execution":{"iopub.execute_input":"2026-01-24T02:53:17.810713Z","iopub.status.busy":"2026-01-24T02:53:17.810147Z","iopub.status.idle":"2026-01-24T02:53:17.964479Z","shell.execute_reply":"2026-01-24T02:53:17.963705Z"},"papermill":{"duration":0.161077,"end_time":"2026-01-24T02:53:17.966183","exception":false,"start_time":"2026-01-24T02:53:17.805106","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class_distribution = train_df[\"ground_truth\"].value_counts().sort_values(ascending=False)\n\nprint(\"\\nClass Distribution Analysis\")\nprint(f\"{'='*50}\")\nprint(f\"{'Individual':<20} {'Count':>8} {'Percentage':>12}\")\nprint(f\"{'-'*50}\")\nfor name, count in class_distribution.items():\n    pct = count / len(train_df) * 100\n    print(f\"{name:<20} {count:>8} {pct:>11.2f}%\")\n\nprint(f\"{'-'*50}\")\nprint(f\"{'Maximum':<20} {class_distribution.max():>8}\")\nprint(f\"{'Minimum':<20} {class_distribution.min():>8}\")\nprint(f\"{'Imbalance Ratio':<20} {class_distribution.max()/class_distribution.min():>8.1f}x\")","metadata":{"execution":{"iopub.execute_input":"2026-01-24T02:53:17.976619Z","iopub.status.busy":"2026-01-24T02:53:17.976165Z","iopub.status.idle":"2026-01-24T02:53:17.990482Z","shell.execute_reply":"2026-01-24T02:53:17.989583Z"},"papermill":{"duration":0.020995,"end_time":"2026-01-24T02:53:17.991955","exception":false,"start_time":"2026-01-24T02:53:17.97096","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"label_encoder = LabelEncoder()\ntrain_df[\"label\"] = label_encoder.fit_transform(train_df[\"ground_truth\"])\n\nlabel_mapping = dict(zip(label_encoder.classes_, range(len(label_encoder.classes_))))\nprint(\"\\nLabel Encoding Mapping\")\nprint(f\"{'='*50}\")\nfor name, idx in sorted(label_mapping.items(), key=lambda x: x[1]):\n    print(f\"{idx:>3}: {name}\")","metadata":{"execution":{"iopub.execute_input":"2026-01-24T02:53:18.003385Z","iopub.status.busy":"2026-01-24T02:53:18.002876Z","iopub.status.idle":"2026-01-24T02:53:18.009158Z","shell.execute_reply":"2026-01-24T02:53:18.00847Z"},"papermill":{"duration":0.013591,"end_time":"2026-01-24T02:53:18.010771","exception":false,"start_time":"2026-01-24T02:53:17.99718","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"sample_image_path = cfg.train_dir / train_df.iloc[0][\"filename\"]\nsample_image = Image.open(sample_image_path)\n\nprint(\"\\nImage Properties Analysis\")\nprint(f\"{'='*50}\")\nprint(f\"Image Mode: {sample_image.mode}\")\nprint(f\"Image Size: {sample_image.size}\")\nprint(f\"Number of Channels: {len(sample_image.getbands())}\")\nprint(f\"Channel Names: {sample_image.getbands()}\")\n\nif sample_image.mode == \"RGBA\":\n    img_array = np.array(sample_image)\n    alpha_channel = img_array[:, :, 3]\n    foreground_pixels = np.sum(alpha_channel > 0)\n    total_pixels = alpha_channel.size\n    print(f\"\\nAlpha Channel Statistics:\")\n    print(f\"Foreground Coverage: {foreground_pixels/total_pixels*100:.1f}%\")\n    print(f\"Alpha Range: [{alpha_channel.min()}, {alpha_channel.max()}]\")","metadata":{"execution":{"iopub.execute_input":"2026-01-24T02:53:18.020839Z","iopub.status.busy":"2026-01-24T02:53:18.020625Z","iopub.status.idle":"2026-01-24T02:53:18.297819Z","shell.execute_reply":"2026-01-24T02:53:18.297042Z"},"papermill":{"duration":0.284209,"end_time":"2026-01-24T02:53:18.299528","exception":false,"start_time":"2026-01-24T02:53:18.015319","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def get_train_transforms(image_size: int) -> A.Compose:\n    return A.Compose([\n        A.LongestMaxSize(max_size=image_size),\n        A.PadIfNeeded(min_height=image_size, min_width=image_size, border_mode=0, value=(0, 0, 0)),\n        A.HorizontalFlip(p=0.5),\n        A.ShiftScaleRotate(shift_limit=0.1, scale_limit=0.15, rotate_limit=15, border_mode=0, p=0.7),\n        A.OneOf([A.GaussNoise(var_limit=(10.0, 50.0)), A.GaussianBlur(blur_limit=(3, 5)), A.MotionBlur(blur_limit=(3, 5))], p=0.3),\n        A.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1, p=0.5),\n        A.CoarseDropout(max_holes=6, max_height=image_size // 16, max_width=image_size // 16, fill_value=0, p=0.3),\n        A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n        ToTensorV2()\n    ])\n\ndef get_valid_transforms(image_size: int) -> A.Compose:\n    return A.Compose([\n        A.LongestMaxSize(max_size=image_size),\n        A.PadIfNeeded(min_height=image_size, min_width=image_size, border_mode=0, value=(0, 0, 0)),\n        A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n        ToTensorV2()\n    ])\n\ndef get_tta_transforms(image_size: int) -> List[A.Compose]:\n    base = [A.LongestMaxSize(max_size=image_size), A.PadIfNeeded(min_height=image_size, min_width=image_size, border_mode=0, value=(0, 0, 0)), A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ToTensorV2()]\n    return [\n        A.Compose(base),\n        A.Compose([A.HorizontalFlip(p=1.0)] + base),\n        A.Compose([A.Affine(scale=(1.1, 1.1))] + base),\n        A.Compose([A.Affine(scale=(0.9, 0.9))] + base),\n    ]\n\nprint(\"Transform pipelines initialized.\")","metadata":{"execution":{"iopub.execute_input":"2026-01-24T02:53:18.31034Z","iopub.status.busy":"2026-01-24T02:53:18.310083Z","iopub.status.idle":"2026-01-24T02:53:18.31987Z","shell.execute_reply":"2026-01-24T02:53:18.319152Z"},"papermill":{"duration":0.016583,"end_time":"2026-01-24T02:53:18.321103","exception":false,"start_time":"2026-01-24T02:53:18.30452","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class JaguarDataset(Dataset):\n    def __init__(self, df: pd.DataFrame, image_dir: Path, transform: A.Compose, use_alpha_mask: bool = True):\n        self.df = df.reset_index(drop=True)\n        self.image_dir = image_dir\n        self.transform = transform\n        self.use_alpha_mask = use_alpha_mask\n    \n    def __len__(self) -> int:\n        return len(self.df)\n    \n    def __getitem__(self, idx: int) -> Tuple[torch.Tensor, int]:\n        row = self.df.iloc[idx]\n        image_path = self.image_dir / row[\"filename\"]\n        image = Image.open(image_path)\n        \n        if self.use_alpha_mask and image.mode == \"RGBA\":\n            img_array = np.array(image)\n            rgb = img_array[:, :, :3]\n            alpha = img_array[:, :, 3:4] / 255.0\n            image = (rgb * alpha).astype(np.uint8)\n        else:\n            image = np.array(image.convert(\"RGB\"))\n        \n        transformed = self.transform(image=image)\n        return transformed[\"image\"], row[\"label\"]\n\nclass JaguarTestDataset(Dataset):\n    def __init__(self, image_list: List[str], image_dir: Path, transform: A.Compose, use_alpha_mask: bool = True):\n        self.image_list = image_list\n        self.image_dir = image_dir\n        self.transform = transform\n        self.use_alpha_mask = use_alpha_mask\n    \n    def __len__(self) -> int:\n        return len(self.image_list)\n    \n    def __getitem__(self, idx: int) -> Tuple[torch.Tensor, str]:\n        filename = self.image_list[idx]\n        image_path = self.image_dir / filename\n        image = Image.open(image_path)\n        \n        if self.use_alpha_mask and image.mode == \"RGBA\":\n            img_array = np.array(image)\n            rgb = img_array[:, :, :3]\n            alpha = img_array[:, :, 3:4] / 255.0\n            image = (rgb * alpha).astype(np.uint8)\n        else:\n            image = np.array(image.convert(\"RGB\"))\n        \n        transformed = self.transform(image=image)\n        return transformed[\"image\"], filename\n\nprint(\"Dataset classes defined.\")","metadata":{"execution":{"iopub.execute_input":"2026-01-24T02:53:18.331759Z","iopub.status.busy":"2026-01-24T02:53:18.331359Z","iopub.status.idle":"2026-01-24T02:53:18.34081Z","shell.execute_reply":"2026-01-24T02:53:18.340021Z"},"papermill":{"duration":0.016376,"end_time":"2026-01-24T02:53:18.342163","exception":false,"start_time":"2026-01-24T02:53:18.325787","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## 4. Model Architecture and Training","metadata":{"papermill":{"duration":0.004532,"end_time":"2026-01-24T02:53:18.351297","exception":false,"start_time":"2026-01-24T02:53:18.346765","status":"completed"},"tags":[]}},{"cell_type":"code","source":"class SubCenterArcFace(nn.Module):\n    def __init__(self, in_features: int, out_features: int, scale: float = 30.0, margin: float = 0.5, k: int = 2):\n        super().__init__()\n        self.in_features = in_features\n        self.out_features = out_features\n        self.scale = scale\n        self.margin = margin\n        self.k = k\n        self.weight = nn.Parameter(torch.FloatTensor(out_features * k, in_features))\n        nn.init.xavier_uniform_(self.weight)\n        self.cos_m = np.cos(margin)\n        self.sin_m = np.sin(margin)\n        self.th = np.cos(np.pi - margin)\n        self.mm = np.sin(np.pi - margin) * margin\n    \n    def forward(self, embeddings: torch.Tensor, labels: Optional[torch.Tensor] = None) -> torch.Tensor:\n        cosine = F.linear(F.normalize(embeddings), F.normalize(self.weight))\n        cosine = cosine.view(-1, self.out_features, self.k).max(dim=2)[0]\n        \n        if labels is None:\n            return cosine * self.scale\n        \n        sine = torch.sqrt(1.0 - torch.clamp(cosine * cosine, 0, 1))\n        phi = cosine * self.cos_m - sine * self.sin_m\n        phi = torch.where(cosine > self.th, phi, cosine - self.mm)\n        one_hot = torch.zeros_like(cosine)\n        one_hot.scatter_(1, labels.view(-1, 1), 1)\n        return ((one_hot * phi) + ((1.0 - one_hot) * cosine)) * self.scale\n\nprint(\"Sub-Center ArcFace module defined.\")","metadata":{"execution":{"iopub.execute_input":"2026-01-24T02:53:18.364469Z","iopub.status.busy":"2026-01-24T02:53:18.363903Z","iopub.status.idle":"2026-01-24T02:53:18.374272Z","shell.execute_reply":"2026-01-24T02:53:18.37315Z"},"papermill":{"duration":0.019765,"end_time":"2026-01-24T02:53:18.376124","exception":false,"start_time":"2026-01-24T02:53:18.356359","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class JaguarReIDModel(nn.Module):\n    def __init__(self, backbone_name: str, embedding_dim: int, num_classes: int, arcface_scale: float = 30.0, arcface_margin: float = 0.5, num_subcenters: int = 2, pretrained: bool = True, use_checkpointing: bool = True):\n        super().__init__()\n        self.use_checkpointing = use_checkpointing\n        self.backbone = timm.create_model(backbone_name, pretrained=pretrained, num_classes=0)\n        backbone_dim = self.backbone.num_features\n        \n        if hasattr(self.backbone, 'set_grad_checkpointing'):\n            self.backbone.set_grad_checkpointing(enable=use_checkpointing)\n        \n        self.neck = nn.Sequential(\n            nn.Linear(backbone_dim, embedding_dim),\n            nn.BatchNorm1d(embedding_dim),\n            nn.PReLU(),\n            nn.Dropout(0.1),\n            nn.Linear(embedding_dim, embedding_dim),\n            nn.BatchNorm1d(embedding_dim)\n        )\n        self.head = SubCenterArcFace(embedding_dim, num_classes, arcface_scale, arcface_margin, num_subcenters)\n        self._init_weights()\n    \n    def _init_weights(self):\n        for m in self.neck.modules():\n            if isinstance(m, nn.Linear):\n                nn.init.kaiming_normal_(m.weight, mode=\"fan_out\", nonlinearity=\"relu\")\n                if m.bias is not None:\n                    nn.init.constant_(m.bias, 0)\n            elif isinstance(m, nn.BatchNorm1d):\n                nn.init.constant_(m.weight, 1)\n                nn.init.constant_(m.bias, 0)\n    \n    def extract_features(self, x: torch.Tensor) -> torch.Tensor:\n        with torch.no_grad():\n            features = self.backbone(x)\n            embeddings = self.neck(features)\n            return F.normalize(embeddings, p=2, dim=1)\n    \n    def forward(self, x: torch.Tensor, labels: Optional[torch.Tensor] = None) -> torch.Tensor:\n        features = self.backbone(x)\n        embeddings = self.neck(features)\n        embeddings = F.normalize(embeddings, p=2, dim=1)\n        if labels is not None:\n            return self.head(embeddings, labels)\n        return embeddings\n\nprint(\"Model architecture defined with gradient checkpointing support.\")","metadata":{"execution":{"iopub.execute_input":"2026-01-24T02:53:18.388989Z","iopub.status.busy":"2026-01-24T02:53:18.38874Z","iopub.status.idle":"2026-01-24T02:53:18.397592Z","shell.execute_reply":"2026-01-24T02:53:18.396784Z"},"papermill":{"duration":0.016523,"end_time":"2026-01-24T02:53:18.398952","exception":false,"start_time":"2026-01-24T02:53:18.382429","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class FocalLoss(nn.Module):\n    def __init__(self, gamma: float = 2.0):\n        super().__init__()\n        self.gamma = gamma\n    \n    def forward(self, inputs: torch.Tensor, targets: torch.Tensor) -> torch.Tensor:\n        ce_loss = F.cross_entropy(inputs, targets, reduction=\"none\")\n        pt = torch.exp(-ce_loss)\n        return (((1 - pt) ** self.gamma) * ce_loss).mean()\n\ndef get_cosine_schedule_with_warmup(optimizer, num_warmup_steps: int, num_training_steps: int, min_lr_ratio: float = 0.01):\n    def lr_lambda(current_step: int) -> float:\n        if current_step < num_warmup_steps:\n            return float(current_step) / float(max(1, num_warmup_steps))\n        progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))\n        return max(min_lr_ratio, 0.5 * (1.0 + np.cos(np.pi * progress)))\n    return torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda)\n\ndef create_class_balanced_sampler(labels: np.ndarray) -> WeightedRandomSampler:\n    class_counts = np.bincount(labels)\n    class_weights = 1.0 / class_counts\n    sample_weights = torch.from_numpy(class_weights[labels]).float()\n    return WeightedRandomSampler(weights=sample_weights, num_samples=len(labels), replacement=True)\n\nprint(\"Loss, scheduler, and sampler defined.\")","metadata":{"execution":{"iopub.execute_input":"2026-01-24T02:53:18.410212Z","iopub.status.busy":"2026-01-24T02:53:18.409663Z","iopub.status.idle":"2026-01-24T02:53:18.417224Z","shell.execute_reply":"2026-01-24T02:53:18.416439Z"},"papermill":{"duration":0.014945,"end_time":"2026-01-24T02:53:18.418727","exception":false,"start_time":"2026-01-24T02:53:18.403782","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def train_one_epoch(model, train_loader, optimizer, scheduler, criterion, device, epoch, cfg):\n    model.train()\n    scaler = GradScaler() if cfg.use_amp else None\n    running_loss, correct, total = 0.0, 0, 0\n    progress_bar = tqdm(train_loader, desc=f\"Epoch {epoch+1}/{cfg.epochs}\", leave=True)\n    optimizer.zero_grad(set_to_none=True)\n    \n    for batch_idx, (images, labels) in enumerate(progress_bar):\n        images = images.to(device, non_blocking=True)\n        labels = labels.to(device, non_blocking=True)\n        \n        if cfg.use_amp:\n            with autocast():\n                logits = model(images, labels)\n                loss = criterion(logits, labels) / cfg.gradient_accumulation\n            scaler.scale(loss).backward()\n            \n            if (batch_idx + 1) % cfg.gradient_accumulation == 0:\n                scaler.unscale_(optimizer)\n                torch.nn.utils.clip_grad_norm_(model.parameters(), cfg.max_grad_norm)\n                scaler.step(optimizer)\n                scaler.update()\n                optimizer.zero_grad(set_to_none=True)\n                scheduler.step()\n        else:\n            logits = model(images, labels)\n            loss = criterion(logits, labels) / cfg.gradient_accumulation\n            loss.backward()\n            \n            if (batch_idx + 1) % cfg.gradient_accumulation == 0:\n                torch.nn.utils.clip_grad_norm_(model.parameters(), cfg.max_grad_norm)\n                optimizer.step()\n                optimizer.zero_grad(set_to_none=True)\n                scheduler.step()\n        \n        running_loss += loss.item() * cfg.gradient_accumulation\n        _, predicted = logits.max(1)\n        total += labels.size(0)\n        correct += predicted.eq(labels).sum().item()\n        \n        progress_bar.set_postfix({\"loss\": f\"{running_loss/(batch_idx+1):.4f}\", \"acc\": f\"{100.*correct/total:.2f}%\", \"lr\": f\"{scheduler.get_last_lr()[0]:.2e}\"})\n        \n        if (batch_idx + 1) % 50 == 0:\n            torch.cuda.empty_cache()\n    \n    return {\"loss\": running_loss / len(train_loader), \"accuracy\": 100. * correct / total}\n\nprint(\"Training function defined with memory optimizations.\")","metadata":{"execution":{"iopub.execute_input":"2026-01-24T02:53:18.429679Z","iopub.status.busy":"2026-01-24T02:53:18.42938Z","iopub.status.idle":"2026-01-24T02:53:18.438419Z","shell.execute_reply":"2026-01-24T02:53:18.437593Z"},"papermill":{"duration":0.016022,"end_time":"2026-01-24T02:53:18.439779","exception":false,"start_time":"2026-01-24T02:53:18.423757","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(\"\\nInitializing Model\")\nprint(f\"{'='*50}\")\n\ntorch.cuda.empty_cache()\ngc.collect()\n\nmodel = JaguarReIDModel(\n    backbone_name=cfg.backbone, \n    embedding_dim=cfg.embedding_dim, \n    num_classes=cfg.num_classes, \n    arcface_scale=cfg.arcface_scale, \n    arcface_margin=cfg.arcface_margin, \n    num_subcenters=cfg.num_subcenters, \n    pretrained=True,\n    use_checkpointing=cfg.gradient_checkpointing\n).to(device)\n\ntotal_params = sum(p.numel() for p in model.parameters())\ntrainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n\nprint(f\"Total Parameters: {total_params:,}\")\nprint(f\"Trainable Parameters: {trainable_params:,}\")\nprint(f\"Model Size: {total_params * 4 / 1e6:.1f} MB (FP32)\")\n\nif torch.cuda.is_available():\n    print(f\"GPU Memory Allocated: {torch.cuda.memory_allocated() / 1e9:.2f} GB\")\n    print(f\"GPU Memory Reserved: {torch.cuda.memory_reserved() / 1e9:.2f} GB\")","metadata":{"execution":{"iopub.execute_input":"2026-01-24T02:53:18.451009Z","iopub.status.busy":"2026-01-24T02:53:18.450758Z","iopub.status.idle":"2026-01-24T02:53:27.354208Z","shell.execute_reply":"2026-01-24T02:53:27.353386Z"},"papermill":{"duration":8.911193,"end_time":"2026-01-24T02:53:27.355863","exception":false,"start_time":"2026-01-24T02:53:18.44467","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(\"\\nPreparing Data Loaders\")\nprint(f\"{'='*50}\")\n\ntrain_transform = get_train_transforms(cfg.image_size)\ntrain_dataset = JaguarDataset(df=train_df, image_dir=cfg.train_dir, transform=train_transform, use_alpha_mask=cfg.use_alpha_mask)\ntrain_sampler = create_class_balanced_sampler(train_df[\"label\"].values)\ntrain_loader = DataLoader(train_dataset, batch_size=cfg.batch_size, sampler=train_sampler, num_workers=cfg.num_workers, pin_memory=True, drop_last=True)\n\nprint(f\"Training Samples: {len(train_dataset):,}\")\nprint(f\"Batches per Epoch: {len(train_loader):,}\")\nprint(f\"Batch Size: {cfg.batch_size}\")\nprint(f\"Effective Batch Size: {cfg.batch_size * cfg.gradient_accumulation}\")\nprint(f\"Class-Balanced Sampling: Enabled\")\nprint(f\"Alpha Mask Usage: {cfg.use_alpha_mask}\")","metadata":{"execution":{"iopub.execute_input":"2026-01-24T02:53:27.368098Z","iopub.status.busy":"2026-01-24T02:53:27.367445Z","iopub.status.idle":"2026-01-24T02:53:27.381586Z","shell.execute_reply":"2026-01-24T02:53:27.380854Z"},"papermill":{"duration":0.021492,"end_time":"2026-01-24T02:53:27.382978","exception":false,"start_time":"2026-01-24T02:53:27.361486","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(\"\\nConfiguring Optimizer and Scheduler\")\nprint(f\"{'='*50}\")\n\noptimizer = torch.optim.AdamW(model.parameters(), lr=cfg.learning_rate, weight_decay=cfg.weight_decay)\nnum_training_steps = len(train_loader) * cfg.epochs // cfg.gradient_accumulation\nnum_warmup_steps = len(train_loader) * cfg.warmup_epochs // cfg.gradient_accumulation\nscheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps)\ncriterion = FocalLoss(gamma=2.0)\n\nprint(f\"Optimizer: AdamW\")\nprint(f\"Learning Rate: {cfg.learning_rate}\")\nprint(f\"Weight Decay: {cfg.weight_decay}\")\nprint(f\"Total Training Steps: {num_training_steps:,}\")\nprint(f\"Warmup Steps: {num_warmup_steps:,}\")\nprint(f\"Loss Function: Focal Loss (gamma=2.0)\")","metadata":{"execution":{"iopub.execute_input":"2026-01-24T02:53:27.394535Z","iopub.status.busy":"2026-01-24T02:53:27.394312Z","iopub.status.idle":"2026-01-24T02:53:27.400934Z","shell.execute_reply":"2026-01-24T02:53:27.400068Z"},"papermill":{"duration":0.013857,"end_time":"2026-01-24T02:53:27.402252","exception":false,"start_time":"2026-01-24T02:53:27.388395","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(\"\\n\" + \"=\"*60)\nprint(\"TRAINING PHASE\")\nprint(\"=\"*60 + \"\\n\")\n\ntraining_history = []\nbest_loss = float(\"inf\")\n\nfor epoch in range(cfg.epochs):\n    torch.cuda.empty_cache()\n    gc.collect()\n    \n    metrics = train_one_epoch(model=model, train_loader=train_loader, optimizer=optimizer, scheduler=scheduler, criterion=criterion, device=device, epoch=epoch, cfg=cfg)\n    training_history.append(metrics)\n    \n    if metrics[\"loss\"] < best_loss:\n        best_loss = metrics[\"loss\"]\n        torch.save({\"epoch\": epoch, \"model_state_dict\": model.state_dict(), \"loss\": best_loss}, cfg.output_dir / \"best_model.pth\")\n    \n    print(f\"\\nEpoch {epoch+1} Summary: Loss={metrics['loss']:.4f}, Accuracy={metrics['accuracy']:.2f}%\")\n    if torch.cuda.is_available():\n        print(f\"GPU Memory: {torch.cuda.memory_allocated() / 1e9:.2f} GB allocated, {torch.cuda.memory_reserved() / 1e9:.2f} GB reserved\\n\")\n\nprint(\"\\n\" + \"=\"*60)\nprint(\"TRAINING COMPLETE\")\nprint(\"=\"*60)\nprint(f\"\\nBest Loss: {best_loss:.4f}\")\nprint(f\"Model saved to: {cfg.output_dir / 'best_model.pth'}\")","metadata":{"execution":{"iopub.execute_input":"2026-01-24T02:53:27.413988Z","iopub.status.busy":"2026-01-24T02:53:27.413465Z","iopub.status.idle":"2026-01-24T06:00:10.439786Z","shell.execute_reply":"2026-01-24T06:00:10.438754Z"},"papermill":{"duration":11203.034144,"end_time":"2026-01-24T06:00:10.441577","exception":false,"start_time":"2026-01-24T02:53:27.407433","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(\"\\nTraining History\")\nprint(f\"{'='*50}\")\nprint(f\"{'Epoch':<8} {'Loss':<12} {'Accuracy':<12}\")\nprint(f\"{'-'*50}\")\nfor i, h in enumerate(training_history):\n    print(f\"{i+1:<8} {h['loss']:<12.4f} {h['accuracy']:<12.2f}%\")","metadata":{"execution":{"iopub.execute_input":"2026-01-24T06:00:10.459602Z","iopub.status.busy":"2026-01-24T06:00:10.459305Z","iopub.status.idle":"2026-01-24T06:00:10.464757Z","shell.execute_reply":"2026-01-24T06:00:10.464056Z"},"papermill":{"duration":0.016109,"end_time":"2026-01-24T06:00:10.466055","exception":false,"start_time":"2026-01-24T06:00:10.449946","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## 5. Inference and Submission Generation","metadata":{"papermill":{"duration":0.007783,"end_time":"2026-01-24T06:00:10.481854","exception":false,"start_time":"2026-01-24T06:00:10.474071","status":"completed"},"tags":[]}},{"cell_type":"code","source":"print(\"\\nLoading Best Model for Inference\")\nprint(f\"{'='*50}\")\n\ntorch.cuda.empty_cache()\ngc.collect()\n\ncheckpoint = torch.load(cfg.output_dir / \"best_model.pth\")\nmodel.load_state_dict(checkpoint[\"model_state_dict\"])\nmodel.eval()\n\nprint(f\"Loaded model from epoch {checkpoint['epoch']+1}\")\nprint(f\"Training loss at checkpoint: {checkpoint['loss']:.4f}\")","metadata":{"execution":{"iopub.execute_input":"2026-01-24T06:00:10.498912Z","iopub.status.busy":"2026-01-24T06:00:10.498364Z","iopub.status.idle":"2026-01-24T06:00:21.077879Z","shell.execute_reply":"2026-01-24T06:00:21.077121Z"},"papermill":{"duration":10.589876,"end_time":"2026-01-24T06:00:21.079654","exception":false,"start_time":"2026-01-24T06:00:10.489778","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"test_images = sorted(list(set(test_df[\"query_image\"].tolist() + test_df[\"gallery_image\"].tolist())))\n\nprint(f\"\\nTest Set Statistics\")\nprint(f\"{'='*50}\")\nprint(f\"Unique Test Images: {len(test_images)}\")\nprint(f\"Total Pairs to Score: {len(test_df):,}\")","metadata":{"execution":{"iopub.execute_input":"2026-01-24T06:00:21.098437Z","iopub.status.busy":"2026-01-24T06:00:21.098181Z","iopub.status.idle":"2026-01-24T06:00:21.110458Z","shell.execute_reply":"2026-01-24T06:00:21.10974Z"},"papermill":{"duration":0.02293,"end_time":"2026-01-24T06:00:21.11185","exception":false,"start_time":"2026-01-24T06:00:21.08892","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"@torch.no_grad()\ndef extract_embeddings(model, image_list, image_dir, transform, device, batch_size=16, use_alpha_mask=True):\n    model.eval()\n    dataset = JaguarTestDataset(image_list=image_list, image_dir=image_dir, transform=transform, use_alpha_mask=use_alpha_mask)\n    loader = DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=2, pin_memory=True)\n    embeddings_dict = {}\n    \n    for images, filenames in tqdm(loader, desc=\"Extracting embeddings\"):\n        images = images.to(device, non_blocking=True)\n        embeddings = model.extract_features(images).cpu().numpy()\n        for filename, emb in zip(filenames, embeddings):\n            embeddings_dict[filename] = emb\n        torch.cuda.empty_cache()\n    \n    return embeddings_dict\n\n@torch.no_grad()\ndef extract_embeddings_with_tta(model, image_list, image_dir, tta_transforms, device, batch_size=16, use_alpha_mask=True):\n    model.eval()\n    all_embeddings = {name: [] for name in image_list}\n    \n    for t_idx, transform in enumerate(tta_transforms):\n        dataset = JaguarTestDataset(image_list=image_list, image_dir=image_dir, transform=transform, use_alpha_mask=use_alpha_mask)\n        loader = DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=2, pin_memory=True)\n        \n        for images, filenames in tqdm(loader, desc=f\"TTA {t_idx+1}/{len(tta_transforms)}\", leave=False):\n            images = images.to(device, non_blocking=True)\n            embeddings = model.extract_features(images).cpu().numpy()\n            for filename, emb in zip(filenames, embeddings):\n                all_embeddings[filename].append(emb)\n            torch.cuda.empty_cache()\n    \n    final_embeddings = {}\n    for name, emb_list in all_embeddings.items():\n        avg_emb = np.mean(emb_list, axis=0)\n        final_embeddings[name] = avg_emb / np.linalg.norm(avg_emb)\n    \n    return final_embeddings\n\nprint(\"Embedding extraction functions defined.\")","metadata":{"execution":{"iopub.execute_input":"2026-01-24T06:00:21.129605Z","iopub.status.busy":"2026-01-24T06:00:21.12934Z","iopub.status.idle":"2026-01-24T06:00:21.138139Z","shell.execute_reply":"2026-01-24T06:00:21.13737Z"},"papermill":{"duration":0.019478,"end_time":"2026-01-24T06:00:21.139613","exception":false,"start_time":"2026-01-24T06:00:21.120135","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def compute_k_reciprocal_rerank(query_embeddings, gallery_embeddings, k1=15, k2=4, lambda_value=0.3):\n    query_num = query_embeddings.shape[0]\n    all_num = query_num\n    original_dist = 1 - np.dot(query_embeddings, gallery_embeddings.T)\n    gallery_dist = 1 - np.dot(gallery_embeddings, gallery_embeddings.T)\n    initial_rank = np.argsort(gallery_dist, axis=1)\n    V = np.zeros((all_num, all_num), dtype=np.float32)\n    \n    for i in range(all_num):\n        forward_k_neigh_index = initial_rank[i, :k1+1]\n        backward_k_neigh_index = initial_rank[forward_k_neigh_index, :k1+1]\n        fi = np.where(backward_k_neigh_index == i)[0]\n        k_reciprocal_index = forward_k_neigh_index[fi]\n        k_reciprocal_expansion_index = k_reciprocal_index\n        \n        for j in range(len(k_reciprocal_index)):\n            candidate = k_reciprocal_index[j]\n            candidate_forward_k_neigh_index = initial_rank[candidate, :int(np.around(k1/2))+1]\n            candidate_backward_k_neigh_index = initial_rank[candidate_forward_k_neigh_index, :int(np.around(k1/2))+1]\n            fi_candidate = np.where(candidate_backward_k_neigh_index == candidate)[0]\n            candidate_k_reciprocal_index = candidate_forward_k_neigh_index[fi_candidate]\n            if len(np.intersect1d(candidate_k_reciprocal_index, k_reciprocal_index)) > 2/3*len(candidate_k_reciprocal_index):\n                k_reciprocal_expansion_index = np.append(k_reciprocal_expansion_index, candidate_k_reciprocal_index)\n        \n        k_reciprocal_expansion_index = np.unique(k_reciprocal_expansion_index)\n        weight = np.exp(-gallery_dist[i, k_reciprocal_expansion_index])\n        V[i, k_reciprocal_expansion_index] = weight / np.sum(weight)\n    \n    if k2 != 1:\n        V_qe = np.zeros_like(V, dtype=np.float32)\n        for i in range(all_num):\n            V_qe[i, :] = np.mean(V[initial_rank[i, :k2], :], axis=0)\n        V = V_qe\n    \n    jaccard_dist = 1 - np.dot(V[:query_num], V.T)\n    return lambda_value * jaccard_dist + (1 - lambda_value) * original_dist\n\nprint(\"k-Reciprocal re-ranking function defined.\")","metadata":{"execution":{"iopub.execute_input":"2026-01-24T06:00:21.157661Z","iopub.status.busy":"2026-01-24T06:00:21.157415Z","iopub.status.idle":"2026-01-24T06:00:21.165468Z","shell.execute_reply":"2026-01-24T06:00:21.164837Z"},"papermill":{"duration":0.018965,"end_time":"2026-01-24T06:00:21.167038","exception":false,"start_time":"2026-01-24T06:00:21.148073","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def compute_similarities(test_df, embeddings_dict):\n    similarities = []\n    for _, row in tqdm(test_df.iterrows(), total=len(test_df), desc=\"Computing similarities\"):\n        query_emb = embeddings_dict[row[\"query_image\"]]\n        gallery_emb = embeddings_dict[row[\"gallery_image\"]]\n        sim = np.clip((np.dot(query_emb, gallery_emb) + 1) / 2, 0, 1)\n        similarities.append(sim)\n    return np.array(similarities)\n\ndef compute_reranked_similarities(test_df, embeddings_dict, test_images, k1=15, k2=4, lambda_value=0.3):\n    image_to_idx = {name: idx for idx, name in enumerate(test_images)}\n    embeddings_matrix = np.stack([embeddings_dict[name] for name in test_images])\n    print(\"Computing k-reciprocal re-ranking...\")\n    reranked_dist = compute_k_reciprocal_rerank(embeddings_matrix, embeddings_matrix, k1, k2, lambda_value)\n    similarities = []\n    for _, row in tqdm(test_df.iterrows(), total=len(test_df), desc=\"Extracting pair scores\"):\n        query_idx = image_to_idx[row[\"query_image\"]]\n        gallery_idx = image_to_idx[row[\"gallery_image\"]]\n        dist = reranked_dist[query_idx, gallery_idx]\n        sim = np.clip((1 - dist + 1) / 2, 0, 1)\n        similarities.append(sim)\n    return np.array(similarities)\n\nprint(\"Similarity computation functions defined.\")","metadata":{"execution":{"iopub.execute_input":"2026-01-24T06:00:21.184638Z","iopub.status.busy":"2026-01-24T06:00:21.184369Z","iopub.status.idle":"2026-01-24T06:00:21.191179Z","shell.execute_reply":"2026-01-24T06:00:21.190494Z"},"papermill":{"duration":0.017353,"end_time":"2026-01-24T06:00:21.192517","exception":false,"start_time":"2026-01-24T06:00:21.175164","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(\"\\n\" + \"=\"*60)\nprint(\"INFERENCE PHASE\")\nprint(\"=\"*60 + \"\\n\")\n\nvalid_transform = get_valid_transforms(cfg.image_size)\n\nprint(\"Extracting baseline embeddings...\")\nembeddings_baseline = extract_embeddings(model=model, image_list=test_images, image_dir=cfg.test_dir, transform=valid_transform, device=device, batch_size=16, use_alpha_mask=cfg.use_alpha_mask)\nprint(f\"Extracted {len(embeddings_baseline)} embeddings.\")","metadata":{"execution":{"iopub.execute_input":"2026-01-24T06:00:21.210643Z","iopub.status.busy":"2026-01-24T06:00:21.210382Z","iopub.status.idle":"2026-01-24T06:02:01.709751Z","shell.execute_reply":"2026-01-24T06:02:01.708815Z"},"papermill":{"duration":100.510459,"end_time":"2026-01-24T06:02:01.711294","exception":false,"start_time":"2026-01-24T06:00:21.200835","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(\"\\nExtracting TTA embeddings...\")\ntta_transforms = get_tta_transforms(cfg.image_size)\nembeddings_tta = extract_embeddings_with_tta(model=model, image_list=test_images, image_dir=cfg.test_dir, tta_transforms=tta_transforms, device=device, batch_size=16, use_alpha_mask=cfg.use_alpha_mask)\nprint(f\"Extracted {len(embeddings_tta)} TTA embeddings.\")","metadata":{"execution":{"iopub.execute_input":"2026-01-24T06:02:01.730903Z","iopub.status.busy":"2026-01-24T06:02:01.730613Z","iopub.status.idle":"2026-01-24T06:08:26.241832Z","shell.execute_reply":"2026-01-24T06:08:26.240779Z"},"papermill":{"duration":384.523557,"end_time":"2026-01-24T06:08:26.24395","exception":false,"start_time":"2026-01-24T06:02:01.720393","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(\"\\n\" + \"=\"*60)\nprint(\"GENERATING SUBMISSIONS\")\nprint(\"=\"*60 + \"\\n\")\n\nprint(\"Submission 1: Baseline\")\nprint(\"-\" * 40)\nsimilarities_baseline = compute_similarities(test_df, embeddings_baseline)\nsubmission_baseline = pd.DataFrame({\"row_id\": test_df[\"row_id\"], \"similarity\": similarities_baseline})\nsubmission_baseline.to_csv(cfg.output_dir / \"submission_baseline.csv\", index=False)\nprint(f\"Stats: Mean={similarities_baseline.mean():.4f}, Std={similarities_baseline.std():.4f}\")\nprint(f\"Saved: submission_baseline.csv\\n\")","metadata":{"execution":{"iopub.execute_input":"2026-01-24T06:08:26.264307Z","iopub.status.busy":"2026-01-24T06:08:26.26363Z","iopub.status.idle":"2026-01-24T06:08:36.766521Z","shell.execute_reply":"2026-01-24T06:08:36.76571Z"},"papermill":{"duration":10.514867,"end_time":"2026-01-24T06:08:36.768068","exception":false,"start_time":"2026-01-24T06:08:26.253201","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(\"Submission 2: TTA\")\nprint(\"-\" * 40)\nsimilarities_tta = compute_similarities(test_df, embeddings_tta)\nsubmission_tta = pd.DataFrame({\"row_id\": test_df[\"row_id\"], \"similarity\": similarities_tta})\nsubmission_tta.to_csv(cfg.output_dir / \"submission_tta.csv\", index=False)\nprint(f\"Stats: Mean={similarities_tta.mean():.4f}, Std={similarities_tta.std():.4f}\")\nprint(f\"Saved: submission_tta.csv\\n\")","metadata":{"execution":{"iopub.execute_input":"2026-01-24T06:08:36.787878Z","iopub.status.busy":"2026-01-24T06:08:36.78763Z","iopub.status.idle":"2026-01-24T06:08:47.35368Z","shell.execute_reply":"2026-01-24T06:08:47.351608Z"},"papermill":{"duration":10.58349,"end_time":"2026-01-24T06:08:47.361268","exception":false,"start_time":"2026-01-24T06:08:36.777778","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(\"Submission 3: Re-ranking\")\nprint(\"-\" * 40)\nsimilarities_rerank = compute_reranked_similarities(test_df=test_df, embeddings_dict=embeddings_tta, test_images=test_images, k1=cfg.rerank_k1, k2=cfg.rerank_k2, lambda_value=cfg.rerank_lambda)\nsubmission_rerank = pd.DataFrame({\"row_id\": test_df[\"row_id\"], \"similarity\": similarities_rerank})\nsubmission_rerank.to_csv(cfg.output_dir / \"submission_rerank.csv\", index=False)\nprint(f\"Stats: Mean={similarities_rerank.mean():.4f}, Std={similarities_rerank.std():.4f}\")\nprint(f\"Saved: submission_rerank.csv\\n\")","metadata":{"execution":{"iopub.execute_input":"2026-01-24T06:08:47.384674Z","iopub.status.busy":"2026-01-24T06:08:47.384342Z","iopub.status.idle":"2026-01-24T06:08:54.356237Z","shell.execute_reply":"2026-01-24T06:08:54.355337Z"},"papermill":{"duration":6.98616,"end_time":"2026-01-24T06:08:54.357936","exception":false,"start_time":"2026-01-24T06:08:47.371776","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(\"Submission 4: Calibrated\")\nprint(\"-\" * 40)\npercentiles = np.percentile(similarities_tta, [5, 95])\nsimilarities_calibrated = np.clip((similarities_tta - percentiles[0]) / (percentiles[1] - percentiles[0]), 0, 1)\nsubmission_calibrated = pd.DataFrame({\"row_id\": test_df[\"row_id\"], \"similarity\": similarities_calibrated})\nsubmission_calibrated.to_csv(cfg.output_dir / \"submission_calibrated.csv\", index=False)\nprint(f\"Stats: Mean={similarities_calibrated.mean():.4f}, Std={similarities_calibrated.std():.4f}\")\nprint(f\"Saved: submission_calibrated.csv\\n\")","metadata":{"execution":{"iopub.execute_input":"2026-01-24T06:08:54.378862Z","iopub.status.busy":"2026-01-24T06:08:54.378609Z","iopub.status.idle":"2026-01-24T06:08:54.634685Z","shell.execute_reply":"2026-01-24T06:08:54.633959Z"},"papermill":{"duration":0.268064,"end_time":"2026-01-24T06:08:54.636176","exception":false,"start_time":"2026-01-24T06:08:54.368112","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(\"Submission 5: Ensemble\")\nprint(\"-\" * 40)\nsimilarities_ensemble = 0.4 * similarities_tta + 0.35 * similarities_rerank + 0.25 * similarities_calibrated\nsubmission_ensemble = pd.DataFrame({\"row_id\": test_df[\"row_id\"], \"similarity\": similarities_ensemble})\nsubmission_ensemble.to_csv(cfg.output_dir / \"submission_ensemble.csv\", index=False)\nprint(f\"Stats: Mean={similarities_ensemble.mean():.4f}, Std={similarities_ensemble.std():.4f}\")\nprint(f\"Saved: submission_ensemble.csv\\n\")","metadata":{"execution":{"iopub.execute_input":"2026-01-24T06:08:54.65765Z","iopub.status.busy":"2026-01-24T06:08:54.657374Z","iopub.status.idle":"2026-01-24T06:08:54.906875Z","shell.execute_reply":"2026-01-24T06:08:54.905948Z"},"papermill":{"duration":0.261517,"end_time":"2026-01-24T06:08:54.908425","exception":false,"start_time":"2026-01-24T06:08:54.646908","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(\"\\n\" + \"=\"*60)\nprint(\"SUBMISSION SUMMARY\")\nprint(\"=\"*60)\n\nsubmissions = [\n    (\"submission_baseline.csv\", \"Cosine similarity\"),\n    (\"submission_tta.csv\", \"Test-Time Augmentation\"),\n    (\"submission_rerank.csv\", \"k-Reciprocal re-ranking\"),\n    (\"submission_calibrated.csv\", \"Percentile calibration\"),\n    (\"submission_ensemble.csv\", \"Weighted ensemble\"),\n]\n\nprint(f\"\\n{'File':<30} {'Description':<40}\")\nprint(\"-\" * 70)\nfor filename, desc in submissions:\n    print(f\"{filename:<30} {desc:<40}\")\n\nprint(\"\\n\" + \"=\"*60)\nprint(\"PIPELINE COMPLETE\")\nprint(\"=\"*60)","metadata":{"execution":{"iopub.execute_input":"2026-01-24T06:08:54.929671Z","iopub.status.busy":"2026-01-24T06:08:54.929406Z","iopub.status.idle":"2026-01-24T06:08:54.934913Z","shell.execute_reply":"2026-01-24T06:08:54.934243Z"},"papermill":{"duration":0.017314,"end_time":"2026-01-24T06:08:54.936296","exception":false,"start_time":"2026-01-24T06:08:54.918982","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(\"\\nSample Predictions (First 10 Pairs)\")\nprint(\"=\"*90)\n\nsample_results = pd.DataFrame({\n    \"row_id\": test_df[\"row_id\"].head(10),\n    \"query\": test_df[\"query_image\"].head(10),\n    \"gallery\": test_df[\"gallery_image\"].head(10),\n    \"baseline\": similarities_baseline[:10],\n    \"tta\": similarities_tta[:10],\n    \"ensemble\": similarities_ensemble[:10]\n})\n\nprint(sample_results.to_string(index=False))","metadata":{"execution":{"iopub.execute_input":"2026-01-24T06:08:54.956701Z","iopub.status.busy":"2026-01-24T06:08:54.956459Z","iopub.status.idle":"2026-01-24T06:08:54.966244Z","shell.execute_reply":"2026-01-24T06:08:54.965677Z"},"papermill":{"duration":0.021368,"end_time":"2026-01-24T06:08:54.96745","exception":false,"start_time":"2026-01-24T06:08:54.946082","status":"completed"},"tags":[]},"outputs":[],"execution_count":null},{"cell_type":"code","source":"del model\ndel embeddings_baseline\ndel embeddings_tta\ngc.collect()\ntorch.cuda.empty_cache()\n\nprint(\"Memory cleared. Pipeline execution complete.\")","metadata":{"execution":{"iopub.execute_input":"2026-01-24T06:08:54.987656Z","iopub.status.busy":"2026-01-24T06:08:54.987423Z","iopub.status.idle":"2026-01-24T06:09:45.419064Z","shell.execute_reply":"2026-01-24T06:09:45.418263Z"},"papermill":{"duration":50.471867,"end_time":"2026-01-24T06:09:45.449075","exception":false,"start_time":"2026-01-24T06:08:54.977208","status":"completed"},"tags":[]},"outputs":[],"execution_count":null}]}