diff --git a/E2E_Foundation_Model_Aaditya_Porwal/LICENSE b/E2E_Foundation_Model_Aaditya_Porwal/LICENSE
new file mode 100644
index 0000000..2b1f269
--- /dev/null
+++ b/E2E_Foundation_Model_Aaditya_Porwal/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 Aaditya Porwal
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
\ No newline at end of file
diff --git a/E2E_Foundation_Model_Aaditya_Porwal/NoteBooks/Classification/SSL_Classification_NTXent.ipynb b/E2E_Foundation_Model_Aaditya_Porwal/NoteBooks/Classification/SSL_Classification_NTXent.ipynb
new file mode 100644
index 0000000..9056c43
--- /dev/null
+++ b/E2E_Foundation_Model_Aaditya_Porwal/NoteBooks/Classification/SSL_Classification_NTXent.ipynb
@@ -0,0 +1 @@
+{"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":"gpu","dataSources":[{"sourceId":12447031,"sourceType":"datasetVersion","datasetId":7851613},{"sourceId":12540315,"sourceType":"datasetVersion","datasetId":7916922},{"sourceId":251729685,"sourceType":"kernelVersion"},{"sourceId":484220,"sourceType":"modelInstanceVersion","isSourceIdPinned":false,"modelInstanceId":386984,"modelId":406073}],"dockerImageVersionId":31089,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import Dataset, DataLoader, Subset\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import roc_auc_score\nimport torchvision.transforms as T\nimport matplotlib.pyplot as plt\n\nimport h5py\nimport numpy as np\nfrom tqdm.auto import tqdm\nimport os","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-23T05:25:22.671319Z","iopub.execute_input":"2025-07-23T05:25:22.671636Z","iopub.status.idle":"2025-07-23T05:25:22.676889Z","shell.execute_reply.started":"2025-07-23T05:25:22.671616Z","shell.execute_reply":"2025-07-23T05:25:22.676113Z"}},"outputs":[],"execution_count":11},{"cell_type":"code","source":"class Config:\n    # --- Paths ---\n    DATA_FILE_PATH = \"/kaggle/input/qc0-1million/train_jet_train_meta_100k.h5\"\n    PRETRAINED_ENCODER_PATH = \"/kaggle/input/lars-and-normalized/simclr_pytorch_model/final_encoder.pth\" \n    \n    # --- Data ---\n    IMAGE_SIZE = 125\n    EVAL_START_IDX = 70000\n    EVAL_END_IDX = 100000\n    TEST_SET_FRACTION = 0.5 # Use 50% of the 30k samples as a fixed test set\n    \n    # --- Augmentation (Matching pre-training) ---\n    ROTATION_DEGREES = 60.0\n    GAUSSIAN_BLUR_SIGMA = (0.1, 2.0)\n    \n    # --- Training ---\n    BATCH_SIZE = 128\n    EPOCHS = 50\n    LEARNING_RATE = 1e-3\n    NUM_CLASSES = 2\n    \n    # --- System ---\n    DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-23T05:24:16.437457Z","iopub.execute_input":"2025-07-23T05:24:16.437975Z","iopub.status.idle":"2025-07-23T05:24:16.499215Z","shell.execute_reply.started":"2025-07-23T05:24:16.437951Z","shell.execute_reply":"2025-07-23T05:24:16.498278Z"}},"outputs":[],"execution_count":2},{"cell_type":"code","source":"print(f\"Using device: {Config.DEVICE}\")\nif not os.path.exists(Config.PRETRAINED_ENCODER_PATH):\n    print(f\"ERROR: Pre-trained encoder not found at '{Config.PRETRAINED_ENCODER_PATH}'\")\n    print(\"Please make sure you have run the pre-training script and the path is correct.\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-23T05:24:16.501661Z","iopub.execute_input":"2025-07-23T05:24:16.502187Z","iopub.status.idle":"2025-07-23T05:24:16.547623Z","shell.execute_reply.started":"2025-07-23T05:24:16.502158Z","shell.execute_reply":"2025-07-23T05:24:16.547002Z"}},"outputs":[{"name":"stdout","text":"Using device: cuda\n","output_type":"stream"}],"execution_count":3},{"cell_type":"code","source":"class ResidualBlock(nn.Module):\n    def __init__(self, in_channels, out_channels, stride=1):\n        super().__init__()\n        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)\n        self.bn1 = nn.BatchNorm2d(out_channels)\n        self.relu = nn.ReLU(inplace=True)\n        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)\n        self.bn2 = nn.BatchNorm2d(out_channels)\n        \n        self.shortcut = nn.Sequential()\n        if stride != 1 or in_channels != out_channels:\n            self.shortcut = nn.Sequential(\n                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),\n                nn.BatchNorm2d(out_channels)\n            )\n\n    def forward(self, x):\n        out = self.relu(self.bn1(self.conv1(x)))\n        out = self.bn2(self.conv2(out))\n        out += self.shortcut(x)\n        out = self.relu(out)\n        return out","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-23T05:24:16.548359Z","iopub.execute_input":"2025-07-23T05:24:16.548638Z","iopub.status.idle":"2025-07-23T05:24:16.584546Z","shell.execute_reply.started":"2025-07-23T05:24:16.548609Z","shell.execute_reply":"2025-07-23T05:24:16.583651Z"}},"outputs":[],"execution_count":4},{"cell_type":"code","source":"class Encoder(nn.Module):\n    \"\"\"ResNet-15 Encoder\"\"\"\n    def __init__(self):\n        super().__init__()\n        self.in_channels = 64\n        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)\n        self.bn1 = nn.BatchNorm2d(64)\n        self.relu = nn.ReLU(inplace=True)\n        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n        \n        self.layer1 = self._make_layer(64, 2, stride=1)\n        self.layer2 = self._make_layer(128, 2, stride=2)\n        self.layer3 = self._make_layer(256, 2, stride=2)\n        \n        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n\n    def _make_layer(self, out_channels, num_blocks, stride):\n        strides = [stride] + [1]*(num_blocks-1)\n        layers = []\n        for s in strides:\n            layers.append(ResidualBlock(self.in_channels, out_channels, s))\n            self.in_channels = out_channels\n        return nn.Sequential(*layers)\n\n    def forward(self, x):\n        x = self.relu(self.bn1(self.conv1(x)))\n        x = self.maxpool(x)\n        x = self.layer1(x)\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.avgpool(x)\n        x = torch.flatten(x, 1)\n        return x","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-23T05:24:16.585596Z","iopub.execute_input":"2025-07-23T05:24:16.585877Z","iopub.status.idle":"2025-07-23T05:24:16.624815Z","shell.execute_reply.started":"2025-07-23T05:24:16.585855Z","shell.execute_reply":"2025-07-23T05:24:16.624119Z"}},"outputs":[],"execution_count":5},{"cell_type":"code","source":"class LinearClassifier(nn.Module):\n    \"\"\"A simple linear layer for classification.\"\"\"\n    def __init__(self, in_features, num_classes):\n        super().__init__()\n        self.fc = nn.Linear(in_features, num_classes)\n\n    def forward(self, x):\n        return self.fc(x)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-23T05:24:16.625591Z","iopub.execute_input":"2025-07-23T05:24:16.625848Z","iopub.status.idle":"2025-07-23T05:24:16.641063Z","shell.execute_reply.started":"2025-07-23T05:24:16.625806Z","shell.execute_reply":"2025-07-23T05:24:16.640460Z"}},"outputs":[],"execution_count":6},{"cell_type":"code","source":"class InMemoryJetDataset(Dataset):\n    def __init__(self, h5_path, start_idx, end_idx):\n        print(\"Loading evaluation data into memory...\")\n        with h5py.File(h5_path, 'r') as f:\n            self.images = f['train_jet'][start_idx:end_idx]\n            self.labels = f['train_meta'][start_idx:end_idx, 2]\n        print(\"Data loaded.\")\n        self.length = len(self.images)\n    def __len__(self):\n        return self.length\n    def __getitem__(self, idx):\n        image = self.images[idx]\n        label = self.labels[idx]\n        image_tensor = torch.from_numpy(image).permute(2, 0, 1).float()\n        label_tensor = torch.tensor(int(label), dtype=torch.long)\n        return image_tensor, label_tensor","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-23T05:24:16.641812Z","iopub.execute_input":"2025-07-23T05:24:16.642069Z","iopub.status.idle":"2025-07-23T05:24:16.654076Z","shell.execute_reply.started":"2025-07-23T05:24:16.642042Z","shell.execute_reply":"2025-07-23T05:24:16.653351Z"}},"outputs":[],"execution_count":7},{"cell_type":"code","source":"class EarlyStopping:\n    \"\"\"Stops training when the AUC score does not improve after a given patience.\"\"\"\n    def __init__(self, patience=10, min_delta=0.001, verbose=True):\n        self.patience = patience\n        self.min_delta = min_delta\n        self.verbose = verbose\n        self.counter = 0\n        self.best_auc = 0.0\n        self.early_stop = False\n\n    def __call__(self, current_auc):\n        if current_auc > self.best_auc + self.min_delta:\n            if self.verbose:\n                print(f\"  -> Test AUC improved ({self.best_auc:.4f} --> {current_auc:.4f}).\")\n            self.best_auc = current_auc\n            self.counter = 0\n        else:\n            self.counter += 1\n            if self.verbose:\n                print(f\"  -> EarlyStopping counter: {self.counter} out of {self.patience}\")\n            if self.counter >= self.patience:\n                print(\"  -> Early stopping triggered.\")\n                self.early_stop = True","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-23T05:24:16.654897Z","iopub.execute_input":"2025-07-23T05:24:16.655098Z","iopub.status.idle":"2025-07-23T05:24:16.669854Z","shell.execute_reply.started":"2025-07-23T05:24:16.655074Z","shell.execute_reply":"2025-07-23T05:24:16.669068Z"}},"outputs":[],"execution_count":8},{"cell_type":"code","source":"def run_linear_probe(label_percentage, tuning_pool_indices, tuning_pool_labels, full_eval_dataset, test_dataset):\n    \"\"\"\n    Trains on a % of the tuning_pool_indices and evaluates on the fixed test_dataset.\n    Returns the best test AUC score and the history of AUC scores.\n    \"\"\"\n    print(f\"\\n--- Running Linear Probe for {label_percentage*100:.0f}% Labels ---\")\n    \n    # --- Data Setup ---\n    if label_percentage == 1.0:\n        train_indices = tuning_pool_indices\n    else:\n        train_indices, _ = train_test_split(\n            tuning_pool_indices, train_size=label_percentage,\n            stratify=tuning_pool_labels, random_state=42\n        )\n    train_subset = Subset(full_eval_dataset, train_indices)\n    print(f\"Training on {len(train_subset)} samples, testing on {len(test_dataset)} samples.\")\n    train_loader = DataLoader(train_subset, batch_size=Config.BATCH_SIZE, shuffle=True, num_workers=0)\n    test_loader = DataLoader(test_dataset, batch_size=Config.BATCH_SIZE, shuffle=False, num_workers=0)\n\n    # --- Model Setup ---\n    encoder = Encoder().to(Config.DEVICE)\n    encoder.load_state_dict(torch.load(Config.PRETRAINED_ENCODER_PATH, map_location=Config.DEVICE))\n    encoder.eval() \n    for param in encoder.parameters():\n        param.requires_grad = False\n    with torch.no_grad():\n        dummy_input = torch.randn(1, 3, Config.IMAGE_SIZE, Config.IMAGE_SIZE).to(Config.DEVICE)\n        feature_size = encoder(dummy_input / 255.0).shape[1]\n    classifier = LinearClassifier(feature_size, Config.NUM_CLASSES).to(Config.DEVICE)\n    \n    # --- Training ---\n    optimizer = optim.Adam(classifier.parameters(), lr=Config.LEARNING_RATE)\n    criterion = nn.CrossEntropyLoss()\n    early_stopper = EarlyStopping(patience=10, min_delta=0.0001)\n    \n    best_auc = 0.0\n    auc_history = []\n    for epoch in range(Config.EPOCHS):\n        classifier.train()\n        for images, labels in train_loader:\n            images, labels = images.to(Config.DEVICE), labels.to(Config.DEVICE)\n            images = images / 255.0\n            images = train_transform(images)\n            optimizer.zero_grad()\n            with torch.no_grad():\n                features = encoder(images)\n            outputs = classifier(features)\n            loss = criterion(outputs, labels)\n            loss.backward()\n            optimizer.step()\n        \n        # --- Evaluation ---\n        classifier.eval()\n        all_labels = []\n        all_outputs = []\n        with torch.no_grad():\n            for images, labels in test_loader:\n                images = images.to(Config.DEVICE)\n                images = images / 255.0\n                images = test_transform(images)\n                features = encoder(images)\n                outputs = classifier(features)\n                # Store raw outputs for class 1 and true labels\n                all_labels.extend(labels.cpu().numpy())\n                all_outputs.extend(outputs.cpu().numpy()[:, 1]) # Probability/logit for the positive class\n        \n        # Calculate AUC score for the epoch\n        auc_score = roc_auc_score(all_labels, all_outputs)\n        auc_history.append(auc_score)\n        \n        print(f\"Epoch [{epoch+1}/{Config.EPOCHS}], Test AUC: {auc_score:.4f}\")\n        if auc_score > best_auc:\n            best_auc = auc_score\n        \n        early_stopper(auc_score)\n        if early_stopper.early_stop:\n            break\n            \n    print(f\"Best Test AUC for {label_percentage*100:.0f}%: {best_auc:.4f}\")\n    return best_auc, auc_history","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-23T05:24:16.671604Z","iopub.execute_input":"2025-07-23T05:24:16.671859Z","iopub.status.idle":"2025-07-23T05:24:16.687044Z","shell.execute_reply.started":"2025-07-23T05:24:16.671842Z","shell.execute_reply":"2025-07-23T05:24:16.686328Z"}},"outputs":[],"execution_count":9},{"cell_type":"code","source":"if __name__ == '__main__':\n    # --- GPU-based Augmentation Pipeline ---\n    train_transform = nn.Sequential(\n        T.RandomHorizontalFlip(),\n        T.RandomVerticalFlip(),\n        T.RandomRotation(degrees=Config.ROTATION_DEGREES),\n        T.GaussianBlur(kernel_size=5, sigma=Config.GAUSSIAN_BLUR_SIGMA)\n    ).to(Config.DEVICE)\n    test_transform = nn.Identity().to(Config.DEVICE)\n    \n    # --- Load Data Once ---\n    full_eval_dataset = InMemoryJetDataset(\n        Config.DATA_FILE_PATH, \n        Config.EVAL_START_IDX, \n        Config.EVAL_END_IDX\n    )\n    \n    # --- Create Fixed Data Splits (Official Protocol) ---\n    all_indices = list(range(len(full_eval_dataset)))\n    all_labels = full_eval_dataset.labels\n    tuning_pool_indices, test_indices, tuning_pool_labels, _ = train_test_split(\n        all_indices, all_labels,\n        test_size=Config.TEST_SET_FRACTION, stratify=all_labels, random_state=42\n    )\n    test_dataset = Subset(full_eval_dataset, test_indices)\n    \n    # --- Run Experiments ---\n    label_percentages = [0.01, 0.10, 0.25, 0.50, 1.0]\n    results_auc = {}\n    history_auc = {}\n    \n    for percent in label_percentages:\n        best_auc, auc_history = run_linear_probe(percent, tuning_pool_indices, tuning_pool_labels, full_eval_dataset, test_dataset)\n        results_auc[f\"{int(percent*100)}%\"] = best_auc\n        history_auc[f\"{int(percent*100)}%\"] = auc_history\n        \n    # --- Plotting Results ---\n    print(\"\\n--- Final Results ---\")\n    for percent, acc in results_auc.items():\n        print(f\"Best AUC with {percent} labels: {acc:.4f}\")\n        \n    plt.style.use('seaborn-v0_8-whitegrid')\n    \n    # Plot 1: Bar chart of the best AUC scores\n    fig1, ax1 = plt.subplots(figsize=(10, 6))\n    percentages = list(results_auc.keys())\n    accuracies = list(results_auc.values())\n    bars = ax1.bar(percentages, accuracies, color=['#ff9999','#66b3ff','#99ff99','#ffcc99', '#c2c2f0'])\n    ax1.set_ylabel('ROC AUC Score', fontsize=14)\n    ax1.set_xlabel('Percentage of Labeled Data Used (from Tuning Pool)', fontsize=14)\n    ax1.set_title('SimCLR Linear Probing Performance (AUC)', fontsize=16, weight='bold')\n    ax1.set_ylim(0, 1.0)\n    for bar in bars:\n        yval = bar.get_height()\n        ax1.text(bar.get_x() + bar.get_width()/2.0, yval + 0.02, f'{yval:.4f}', ha='center', va='bottom', fontsize=12)\n    plt.xticks(fontsize=12); plt.yticks(fontsize=12); plt.tight_layout()\n    plt.show()\n\n    # Plot 2: Line chart of AUC vs. Epochs for each percentage\n    fig2, ax2 = plt.subplots(figsize=(12, 8))\n    for percent_str, history in history_auc.items():\n        ax2.plot(range(1, len(history) + 1), history, marker='o', linestyle='-', label=f'{percent_str} Labels')\n    ax2.set_ylabel('Test ROC AUC', fontsize=14)\n    ax2.set_xlabel('Epoch', fontsize=14)\n    ax2.set_title('AUC Score vs. Epochs for Different Label Percentages', fontsize=16, weight='bold')\n    ax2.set_ylim(bottom=0.5, top=max(max(h) for h in history_auc.values()) * 1.05 if any(history_auc.values()) else 1.0)\n    ax2.grid(True, which='both', linestyle='--', linewidth=0.5)\n    ax2.legend(fontsize=12)\n    plt.xticks(fontsize=12); plt.yticks(fontsize=12); plt.tight_layout()\n    plt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-23T05:25:26.364785Z","iopub.execute_input":"2025-07-23T05:25:26.365449Z","iopub.status.idle":"2025-07-23T05:44:11.763247Z","shell.execute_reply.started":"2025-07-23T05:25:26.365426Z","shell.execute_reply":"2025-07-23T05:44:11.762475Z"}},"outputs":[{"name":"stdout","text":"Loading evaluation data into memory...\nData loaded.\n\n--- Running Linear Probe for 1% Labels ---\nTraining on 150 samples, testing on 15000 samples.\nEpoch [1/50], Test AUC: 0.5673\n  -> Test AUC improved (0.0000 --> 0.5673).\nEpoch [2/50], Test AUC: 0.5812\n  -> Test AUC improved (0.5673 --> 0.5812).\nEpoch [3/50], Test AUC: 0.5931\n  -> Test AUC improved (0.5812 --> 0.5931).\nEpoch [4/50], Test AUC: 0.6020\n  -> Test AUC improved (0.5931 --> 0.6020).\nEpoch [5/50], Test AUC: 0.6082\n  -> Test AUC improved (0.6020 --> 0.6082).\nEpoch [6/50], Test AUC: 0.6135\n  -> Test AUC improved (0.6082 --> 0.6135).\nEpoch [7/50], Test AUC: 0.6168\n  -> Test AUC improved (0.6135 --> 0.6168).\nEpoch [8/50], Test AUC: 0.6193\n  -> Test AUC improved (0.6168 --> 0.6193).\nEpoch [9/50], Test AUC: 0.6217\n  -> Test AUC improved (0.6193 --> 0.6217).\nEpoch [10/50], Test AUC: 0.6231\n  -> Test AUC improved (0.6217 --> 0.6231).\nEpoch [11/50], Test AUC: 0.6246\n  -> Test AUC improved (0.6231 --> 0.6246).\nEpoch [12/50], Test AUC: 0.6261\n  -> Test AUC improved (0.6246 --> 0.6261).\nEpoch [13/50], Test AUC: 0.6278\n  -> Test AUC improved (0.6261 --> 0.6278).\nEpoch [14/50], Test AUC: 0.6297\n  -> Test AUC improved (0.6278 --> 0.6297).\nEpoch [15/50], Test AUC: 0.6310\n  -> Test AUC improved (0.6297 --> 0.6310).\nEpoch [16/50], Test AUC: 0.6322\n  -> Test AUC improved (0.6310 --> 0.6322).\nEpoch [17/50], Test AUC: 0.6333\n  -> Test AUC improved (0.6322 --> 0.6333).\nEpoch [18/50], Test AUC: 0.6347\n  -> Test AUC improved (0.6333 --> 0.6347).\nEpoch [19/50], Test AUC: 0.6358\n  -> Test AUC improved (0.6347 --> 0.6358).\nEpoch [20/50], Test AUC: 0.6365\n  -> Test AUC improved (0.6358 --> 0.6365).\nEpoch [21/50], Test AUC: 0.6370\n  -> Test AUC improved (0.6365 --> 0.6370).\nEpoch [22/50], Test AUC: 0.6373\n  -> Test AUC improved (0.6370 --> 0.6373).\nEpoch [23/50], Test AUC: 0.6373\n  -> EarlyStopping counter: 1 out of 10\nEpoch [24/50], Test AUC: 0.6372\n  -> EarlyStopping counter: 2 out of 10\nEpoch [25/50], Test AUC: 0.6374\n  -> Test AUC improved (0.6373 --> 0.6374).\nEpoch [26/50], Test AUC: 0.6375\n  -> EarlyStopping counter: 1 out of 10\nEpoch [27/50], Test AUC: 0.6372\n  -> EarlyStopping counter: 2 out of 10\nEpoch [28/50], Test AUC: 0.6368\n  -> EarlyStopping counter: 3 out of 10\nEpoch [29/50], Test AUC: 0.6365\n  -> EarlyStopping counter: 4 out of 10\nEpoch [30/50], Test AUC: 0.6370\n  -> EarlyStopping counter: 5 out of 10\nEpoch [31/50], Test AUC: 0.6375\n  -> EarlyStopping counter: 6 out of 10\nEpoch [32/50], Test AUC: 0.6378\n  -> Test AUC improved (0.6374 --> 0.6378).\nEpoch [33/50], Test AUC: 0.6377\n  -> EarlyStopping counter: 1 out of 10\nEpoch [34/50], Test AUC: 0.6374\n  -> EarlyStopping counter: 2 out of 10\nEpoch [35/50], Test AUC: 0.6369\n  -> EarlyStopping counter: 3 out of 10\nEpoch [36/50], Test AUC: 0.6362\n  -> EarlyStopping counter: 4 out of 10\nEpoch [37/50], Test AUC: 0.6355\n  -> EarlyStopping counter: 5 out of 10\nEpoch [38/50], Test AUC: 0.6347\n  -> EarlyStopping counter: 6 out of 10\nEpoch [39/50], Test AUC: 0.6338\n  -> EarlyStopping counter: 7 out of 10\nEpoch [40/50], Test AUC: 0.6330\n  -> EarlyStopping counter: 8 out of 10\nEpoch [41/50], Test AUC: 0.6327\n  -> EarlyStopping counter: 9 out of 10\nEpoch [42/50], Test AUC: 0.6328\n  -> EarlyStopping counter: 10 out of 10\n  -> Early stopping triggered.\nBest Test AUC for 1%: 0.6378\n\n--- Running Linear Probe for 10% Labels ---\nTraining on 1500 samples, testing on 15000 samples.\nEpoch [1/50], Test AUC: 0.6322\n  -> Test AUC improved (0.0000 --> 0.6322).\nEpoch [2/50], Test AUC: 0.6800\n  -> Test AUC improved (0.6322 --> 0.6800).\nEpoch [3/50], Test AUC: 0.6928\n  -> Test AUC improved (0.6800 --> 0.6928).\nEpoch [4/50], Test AUC: 0.6977\n  -> Test AUC improved (0.6928 --> 0.6977).\nEpoch [5/50], Test AUC: 0.7019\n  -> Test AUC improved (0.6977 --> 0.7019).\nEpoch [6/50], Test AUC: 0.7048\n  -> Test AUC improved (0.7019 --> 0.7048).\nEpoch [7/50], Test AUC: 0.7065\n  -> Test AUC improved (0.7048 --> 0.7065).\nEpoch [8/50], Test AUC: 0.7090\n  -> Test AUC improved (0.7065 --> 0.7090).\nEpoch [9/50], Test AUC: 0.7110\n  -> Test AUC improved (0.7090 --> 0.7110).\nEpoch [10/50], Test AUC: 0.7122\n  -> Test AUC improved (0.7110 --> 0.7122).\nEpoch [11/50], Test AUC: 0.7141\n  -> Test AUC improved (0.7122 --> 0.7141).\nEpoch [12/50], Test AUC: 0.7153\n  -> Test AUC improved (0.7141 --> 0.7153).\nEpoch [13/50], Test AUC: 0.7175\n  -> Test AUC improved (0.7153 --> 0.7175).\nEpoch [14/50], Test AUC: 0.7180\n  -> Test AUC improved (0.7175 --> 0.7180).\nEpoch [15/50], Test AUC: 0.7192\n  -> Test AUC improved (0.7180 --> 0.7192).\nEpoch [16/50], Test AUC: 0.7199\n  -> Test AUC improved (0.7192 --> 0.7199).\nEpoch [17/50], Test AUC: 0.7202\n  -> Test AUC improved (0.7199 --> 0.7202).\nEpoch [18/50], Test AUC: 0.7211\n  -> Test AUC improved (0.7202 --> 0.7211).\nEpoch [19/50], Test AUC: 0.7217\n  -> Test AUC improved (0.7211 --> 0.7217).\nEpoch [20/50], Test AUC: 0.7217\n  -> EarlyStopping counter: 1 out of 10\nEpoch [21/50], Test AUC: 0.7223\n  -> Test AUC improved (0.7217 --> 0.7223).\nEpoch [22/50], Test AUC: 0.7230\n  -> Test AUC improved (0.7223 --> 0.7230).\nEpoch [23/50], Test AUC: 0.7234\n  -> Test AUC improved (0.7230 --> 0.7234).\nEpoch [24/50], Test AUC: 0.7233\n  -> EarlyStopping counter: 1 out of 10\nEpoch [25/50], Test AUC: 0.7237\n  -> Test AUC improved (0.7234 --> 0.7237).\nEpoch [26/50], Test AUC: 0.7239\n  -> Test AUC improved (0.7237 --> 0.7239).\nEpoch [27/50], Test AUC: 0.7238\n  -> EarlyStopping counter: 1 out of 10\nEpoch [28/50], Test AUC: 0.7239\n  -> EarlyStopping counter: 2 out of 10\nEpoch [29/50], Test AUC: 0.7239\n  -> EarlyStopping counter: 3 out of 10\nEpoch [30/50], Test AUC: 0.7240\n  -> EarlyStopping counter: 4 out of 10\nEpoch [31/50], Test AUC: 0.7247\n  -> Test AUC improved (0.7239 --> 0.7247).\nEpoch [32/50], Test AUC: 0.7249\n  -> Test AUC improved (0.7247 --> 0.7249).\nEpoch [33/50], Test AUC: 0.7251\n  -> Test AUC improved (0.7249 --> 0.7251).\nEpoch [34/50], Test AUC: 0.7257\n  -> Test AUC improved (0.7251 --> 0.7257).\nEpoch [35/50], Test AUC: 0.7258\n  -> Test AUC improved (0.7257 --> 0.7258).\nEpoch [36/50], Test AUC: 0.7259\n  -> Test AUC improved (0.7258 --> 0.7259).\nEpoch [37/50], Test AUC: 0.7262\n  -> Test AUC improved (0.7259 --> 0.7262).\nEpoch [38/50], Test AUC: 0.7260\n  -> EarlyStopping counter: 1 out of 10\nEpoch [39/50], Test AUC: 0.7256\n  -> EarlyStopping counter: 2 out of 10\nEpoch [40/50], Test AUC: 0.7253\n  -> EarlyStopping counter: 3 out of 10\nEpoch [41/50], Test AUC: 0.7255\n  -> EarlyStopping counter: 4 out of 10\nEpoch [42/50], Test AUC: 0.7256\n  -> EarlyStopping counter: 5 out of 10\nEpoch [43/50], Test AUC: 0.7256\n  -> EarlyStopping counter: 6 out of 10\nEpoch [44/50], Test AUC: 0.7257\n  -> EarlyStopping counter: 7 out of 10\nEpoch [45/50], Test AUC: 0.7257\n  -> EarlyStopping counter: 8 out of 10\nEpoch [46/50], Test AUC: 0.7259\n  -> EarlyStopping counter: 9 out of 10\nEpoch [47/50], Test AUC: 0.7260\n  -> EarlyStopping counter: 10 out of 10\n  -> Early stopping triggered.\nBest Test AUC for 10%: 0.7262\n\n--- Running Linear Probe for 25% Labels ---\nTraining on 3750 samples, testing on 15000 samples.\nEpoch [1/50], Test AUC: 0.6887\n  -> Test AUC improved (0.0000 --> 0.6887).\nEpoch [2/50], Test AUC: 0.7069\n  -> Test AUC improved (0.6887 --> 0.7069).\nEpoch [3/50], Test AUC: 0.7139\n  -> Test AUC improved (0.7069 --> 0.7139).\nEpoch [4/50], Test AUC: 0.7208\n  -> Test AUC improved (0.7139 --> 0.7208).\nEpoch [5/50], Test AUC: 0.7255\n  -> Test AUC improved (0.7208 --> 0.7255).\nEpoch [6/50], Test AUC: 0.7275\n  -> Test AUC improved (0.7255 --> 0.7275).\nEpoch [7/50], Test AUC: 0.7287\n  -> Test AUC improved (0.7275 --> 0.7287).\nEpoch [8/50], Test AUC: 0.7296\n  -> Test AUC improved (0.7287 --> 0.7296).\nEpoch [9/50], Test AUC: 0.7310\n  -> Test AUC improved (0.7296 --> 0.7310).\nEpoch [10/50], Test AUC: 0.7314\n  -> Test AUC improved (0.7310 --> 0.7314).\nEpoch [11/50], Test AUC: 0.7318\n  -> Test AUC improved (0.7314 --> 0.7318).\nEpoch [12/50], Test AUC: 0.7321\n  -> Test AUC improved (0.7318 --> 0.7321).\nEpoch [13/50], Test AUC: 0.7323\n  -> Test AUC improved (0.7321 --> 0.7323).\nEpoch [14/50], Test AUC: 0.7324\n  -> Test AUC improved (0.7323 --> 0.7324).\nEpoch [15/50], Test AUC: 0.7326\n  -> Test AUC improved (0.7324 --> 0.7326).\nEpoch [16/50], Test AUC: 0.7328\n  -> Test AUC improved (0.7326 --> 0.7328).\nEpoch [17/50], Test AUC: 0.7328\n  -> EarlyStopping counter: 1 out of 10\nEpoch [18/50], Test AUC: 0.7328\n  -> EarlyStopping counter: 2 out of 10\nEpoch [19/50], Test AUC: 0.7325\n  -> EarlyStopping counter: 3 out of 10\nEpoch [20/50], Test AUC: 0.7326\n  -> EarlyStopping counter: 4 out of 10\nEpoch [21/50], Test AUC: 0.7326\n  -> EarlyStopping counter: 5 out of 10\nEpoch [22/50], Test AUC: 0.7325\n  -> EarlyStopping counter: 6 out of 10\nEpoch [23/50], Test AUC: 0.7324\n  -> EarlyStopping counter: 7 out of 10\nEpoch [24/50], Test AUC: 0.7318\n  -> EarlyStopping counter: 8 out of 10\nEpoch [25/50], Test AUC: 0.7325\n  -> EarlyStopping counter: 9 out of 10\nEpoch [26/50], Test AUC: 0.7320\n  -> EarlyStopping counter: 10 out of 10\n  -> Early stopping triggered.\nBest Test AUC for 25%: 0.7328\n\n--- Running Linear Probe for 50% Labels ---\nTraining on 7500 samples, testing on 15000 samples.\nEpoch [1/50], Test AUC: 0.7010\n  -> Test AUC improved (0.0000 --> 0.7010).\nEpoch [2/50], Test AUC: 0.7163\n  -> Test AUC improved (0.7010 --> 0.7163).\nEpoch [3/50], Test AUC: 0.7238\n  -> Test AUC improved (0.7163 --> 0.7238).\nEpoch [4/50], Test AUC: 0.7277\n  -> Test AUC improved (0.7238 --> 0.7277).\nEpoch [5/50], Test AUC: 0.7295\n  -> Test AUC improved (0.7277 --> 0.7295).\nEpoch [6/50], Test AUC: 0.7308\n  -> Test AUC improved (0.7295 --> 0.7308).\nEpoch [7/50], Test AUC: 0.7314\n  -> Test AUC improved (0.7308 --> 0.7314).\nEpoch [8/50], Test AUC: 0.7319\n  -> Test AUC improved (0.7314 --> 0.7319).\nEpoch [9/50], Test AUC: 0.7324\n  -> Test AUC improved (0.7319 --> 0.7324).\nEpoch [10/50], Test AUC: 0.7327\n  -> Test AUC improved (0.7324 --> 0.7327).\nEpoch [11/50], Test AUC: 0.7327\n  -> EarlyStopping counter: 1 out of 10\nEpoch [12/50], Test AUC: 0.7332\n  -> Test AUC improved (0.7327 --> 0.7332).\nEpoch [13/50], Test AUC: 0.7336\n  -> Test AUC improved (0.7332 --> 0.7336).\nEpoch [14/50], Test AUC: 0.7338\n  -> Test AUC improved (0.7336 --> 0.7338).\nEpoch [15/50], Test AUC: 0.7335\n  -> EarlyStopping counter: 1 out of 10\nEpoch [16/50], Test AUC: 0.7336\n  -> EarlyStopping counter: 2 out of 10\nEpoch [17/50], Test AUC: 0.7340\n  -> Test AUC improved (0.7338 --> 0.7340).\nEpoch [18/50], Test AUC: 0.7337\n  -> EarlyStopping counter: 1 out of 10\nEpoch [19/50], Test AUC: 0.7344\n  -> Test AUC improved (0.7340 --> 0.7344).\nEpoch [20/50], Test AUC: 0.7345\n  -> EarlyStopping counter: 1 out of 10\nEpoch [21/50], Test AUC: 0.7342\n  -> EarlyStopping counter: 2 out of 10\nEpoch [22/50], Test AUC: 0.7345\n  -> EarlyStopping counter: 3 out of 10\nEpoch [23/50], Test AUC: 0.7349\n  -> Test AUC improved (0.7344 --> 0.7349).\nEpoch [24/50], Test AUC: 0.7344\n  -> EarlyStopping counter: 1 out of 10\nEpoch [25/50], Test AUC: 0.7348\n  -> EarlyStopping counter: 2 out of 10\nEpoch [26/50], Test AUC: 0.7347\n  -> EarlyStopping counter: 3 out of 10\nEpoch [27/50], Test AUC: 0.7347\n  -> EarlyStopping counter: 4 out of 10\nEpoch [28/50], Test AUC: 0.7346\n  -> EarlyStopping counter: 5 out of 10\nEpoch [29/50], Test AUC: 0.7345\n  -> EarlyStopping counter: 6 out of 10\nEpoch [30/50], Test AUC: 0.7351\n  -> Test AUC improved (0.7349 --> 0.7351).\nEpoch [31/50], Test AUC: 0.7353\n  -> Test AUC improved (0.7351 --> 0.7353).\nEpoch [32/50], Test AUC: 0.7352\n  -> EarlyStopping counter: 1 out of 10\nEpoch [33/50], Test AUC: 0.7351\n  -> EarlyStopping counter: 2 out of 10\nEpoch [34/50], Test AUC: 0.7350\n  -> EarlyStopping counter: 3 out of 10\nEpoch [35/50], Test AUC: 0.7349\n  -> EarlyStopping counter: 4 out of 10\nEpoch [36/50], Test AUC: 0.7353\n  -> EarlyStopping counter: 5 out of 10\nEpoch [37/50], Test AUC: 0.7356\n  -> Test AUC improved (0.7353 --> 0.7356).\nEpoch [38/50], Test AUC: 0.7357\n  -> Test AUC improved (0.7356 --> 0.7357).\nEpoch [39/50], Test AUC: 0.7355\n  -> EarlyStopping counter: 1 out of 10\nEpoch [40/50], Test AUC: 0.7356\n  -> EarlyStopping counter: 2 out of 10\nEpoch [41/50], Test AUC: 0.7354\n  -> EarlyStopping counter: 3 out of 10\nEpoch [42/50], Test AUC: 0.7356\n  -> EarlyStopping counter: 4 out of 10\nEpoch [43/50], Test AUC: 0.7356\n  -> EarlyStopping counter: 5 out of 10\nEpoch [44/50], Test AUC: 0.7355\n  -> EarlyStopping counter: 6 out of 10\nEpoch [45/50], Test AUC: 0.7357\n  -> EarlyStopping counter: 7 out of 10\nEpoch [46/50], Test AUC: 0.7356\n  -> EarlyStopping counter: 8 out of 10\nEpoch [47/50], Test AUC: 0.7359\n  -> Test AUC improved (0.7357 --> 0.7359).\nEpoch [48/50], Test AUC: 0.7356\n  -> EarlyStopping counter: 1 out of 10\nEpoch [49/50], Test AUC: 0.7359\n  -> EarlyStopping counter: 2 out of 10\nEpoch [50/50], Test AUC: 0.7361\n  -> Test AUC improved (0.7359 --> 0.7361).\nBest Test AUC for 50%: 0.7361\n\n--- Running Linear Probe for 100% Labels ---\nTraining on 15000 samples, testing on 15000 samples.\nEpoch [1/50], Test AUC: 0.7207\n  -> Test AUC improved (0.0000 --> 0.7207).\nEpoch [2/50], Test AUC: 0.7301\n  -> Test AUC improved (0.7207 --> 0.7301).\nEpoch [3/50], Test AUC: 0.7323\n  -> Test AUC improved (0.7301 --> 0.7323).\nEpoch [4/50], Test AUC: 0.7322\n  -> EarlyStopping counter: 1 out of 10\nEpoch [5/50], Test AUC: 0.7337\n  -> Test AUC improved (0.7323 --> 0.7337).\nEpoch [6/50], Test AUC: 0.7327\n  -> EarlyStopping counter: 1 out of 10\nEpoch [7/50], Test AUC: 0.7339\n  -> Test AUC improved (0.7337 --> 0.7339).\nEpoch [8/50], Test AUC: 0.7335\n  -> EarlyStopping counter: 1 out of 10\nEpoch [9/50], Test AUC: 0.7340\n  -> EarlyStopping counter: 2 out of 10\nEpoch [10/50], Test AUC: 0.7345\n  -> Test AUC improved (0.7339 --> 0.7345).\nEpoch [11/50], Test AUC: 0.7346\n  -> Test AUC improved (0.7345 --> 0.7346).\nEpoch [12/50], Test AUC: 0.7348\n  -> Test AUC improved (0.7346 --> 0.7348).\nEpoch [13/50], Test AUC: 0.7350\n  -> Test AUC improved (0.7348 --> 0.7350).\nEpoch [14/50], Test AUC: 0.7347\n  -> EarlyStopping counter: 1 out of 10\nEpoch [15/50], Test AUC: 0.7353\n  -> Test AUC improved (0.7350 --> 0.7353).\nEpoch [16/50], Test AUC: 0.7349\n  -> EarlyStopping counter: 1 out of 10\nEpoch [17/50], Test AUC: 0.7357\n  -> Test AUC improved (0.7353 --> 0.7357).\nEpoch [18/50], Test AUC: 0.7341\n  -> EarlyStopping counter: 1 out of 10\nEpoch [19/50], Test AUC: 0.7345\n  -> EarlyStopping counter: 2 out of 10\nEpoch [20/50], Test AUC: 0.7362\n  -> Test AUC improved (0.7357 --> 0.7362).\nEpoch [21/50], Test AUC: 0.7352\n  -> EarlyStopping counter: 1 out of 10\nEpoch [22/50], Test AUC: 0.7358\n  -> EarlyStopping counter: 2 out of 10\nEpoch [23/50], Test AUC: 0.7343\n  -> EarlyStopping counter: 3 out of 10\nEpoch [24/50], Test AUC: 0.7352\n  -> EarlyStopping counter: 4 out of 10\nEpoch [25/50], Test AUC: 0.7355\n  -> EarlyStopping counter: 5 out of 10\nEpoch [26/50], Test AUC: 0.7366\n  -> Test AUC improved (0.7362 --> 0.7366).\nEpoch [27/50], Test AUC: 0.7368\n  -> Test AUC improved (0.7366 --> 0.7368).\nEpoch [28/50], Test AUC: 0.7367\n  -> EarlyStopping counter: 1 out of 10\nEpoch [29/50], Test AUC: 0.7369\n  -> EarlyStopping counter: 2 out of 10\nEpoch [30/50], Test AUC: 0.7370\n  -> Test AUC improved (0.7368 --> 0.7370).\nEpoch [31/50], Test AUC: 0.7364\n  -> EarlyStopping counter: 1 out of 10\nEpoch [32/50], Test AUC: 0.7362\n  -> EarlyStopping counter: 2 out of 10\nEpoch [33/50], Test AUC: 0.7367\n  -> EarlyStopping counter: 3 out of 10\nEpoch [34/50], Test AUC: 0.7373\n  -> Test AUC improved (0.7370 --> 0.7373).\nEpoch [35/50], Test AUC: 0.7371\n  -> EarlyStopping counter: 1 out of 10\nEpoch [36/50], Test AUC: 0.7369\n  -> EarlyStopping counter: 2 out of 10\nEpoch [37/50], Test AUC: 0.7371\n  -> EarlyStopping counter: 3 out of 10\nEpoch [38/50], Test AUC: 0.7374\n  -> Test AUC improved (0.7373 --> 0.7374).\nEpoch [39/50], Test AUC: 0.7373\n  -> EarlyStopping counter: 1 out of 10\nEpoch [40/50], Test AUC: 0.7375\n  -> Test AUC improved (0.7374 --> 0.7375).\nEpoch [41/50], Test AUC: 0.7374\n  -> EarlyStopping counter: 1 out of 10\nEpoch [42/50], Test AUC: 0.7362\n  -> EarlyStopping counter: 2 out of 10\nEpoch [43/50], Test AUC: 0.7364\n  -> EarlyStopping counter: 3 out of 10\nEpoch [44/50], Test AUC: 0.7354\n  -> EarlyStopping counter: 4 out of 10\nEpoch [45/50], Test AUC: 0.7365\n  -> EarlyStopping counter: 5 out of 10\nEpoch [46/50], Test AUC: 0.7365\n  -> EarlyStopping counter: 6 out of 10\nEpoch [47/50], Test AUC: 0.7367\n  -> EarlyStopping counter: 7 out of 10\nEpoch [48/50], Test AUC: 0.7366\n  -> EarlyStopping counter: 8 out of 10\nEpoch [49/50], Test AUC: 0.7369\n  -> EarlyStopping counter: 9 out of 10\nEpoch [50/50], Test AUC: 0.7364\n  -> EarlyStopping counter: 10 out of 10\n  -> Early stopping triggered.\nBest Test AUC for 100%: 0.7375\n\n--- Final Results ---\nBest AUC with 1% labels: 0.6378\nBest AUC with 10% labels: 0.7262\nBest AUC with 25% labels: 0.7328\nBest AUC with 50% labels: 0.7361\nBest AUC with 100% labels: 0.7375\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x600 with 1 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\n"},"metadata":{}}],"execution_count":12},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}
\ No newline at end of file
diff --git a/E2E_Foundation_Model_Aaditya_Porwal/NoteBooks/Classification/SSL_Classification_VICReg.ipynb b/E2E_Foundation_Model_Aaditya_Porwal/NoteBooks/Classification/SSL_Classification_VICReg.ipynb
new file mode 100644
index 0000000..3d36e79
--- /dev/null
+++ b/E2E_Foundation_Model_Aaditya_Porwal/NoteBooks/Classification/SSL_Classification_VICReg.ipynb
@@ -0,0 +1 @@
+{"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":"gpu","dataSources":[{"sourceId":12447031,"sourceType":"datasetVersion","datasetId":7851613},{"sourceId":12540315,"sourceType":"datasetVersion","datasetId":7916922},{"sourceId":12567791,"sourceType":"datasetVersion","datasetId":7936657},{"sourceId":12574716,"sourceType":"datasetVersion","datasetId":7941414},{"sourceId":252433079,"sourceType":"kernelVersion"},{"sourceId":484220,"sourceType":"modelInstanceVersion","isSourceIdPinned":false,"modelInstanceId":386984,"modelId":406073}],"dockerImageVersionId":31089,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import Dataset, DataLoader, Subset\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import roc_auc_score\nimport torchvision.transforms as T\nimport matplotlib.pyplot as plt\n\nimport h5py\nimport numpy as np\nfrom tqdm.auto import tqdm\nimport os","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-25T14:41:54.244949Z","iopub.execute_input":"2025-07-25T14:41:54.245243Z","iopub.status.idle":"2025-07-25T14:42:02.958073Z","shell.execute_reply.started":"2025-07-25T14:41:54.245223Z","shell.execute_reply":"2025-07-25T14:42:02.957275Z"}},"outputs":[],"execution_count":2},{"cell_type":"code","source":"class Config:\n    # --- Paths ---\n    DATA_FILE_PATH = \"/kaggle/input/qc0-1million/train_jet_train_meta_100k.h5\"\n    PRETRAINED_ENCODER_PATH = \"/kaggle/input/pytorchresnet15/simclr_pytorch_model/best_encoder.pth\"\n    \n    # --- Data ---\n    IMAGE_SIZE = 125\n    EVAL_START_IDX = 70000\n    EVAL_END_IDX = 100000\n    TEST_SET_FRACTION = 0.5 # Use 50% of the 30k samples as a fixed test set\n    \n    # --- Augmentation (Matching pre-training) ---\n    ROTATION_DEGREES = 60.0\n    GAUSSIAN_BLUR_SIGMA = (0.1, 2.0)\n    \n    # --- Training ---\n    BATCH_SIZE = 128\n    EPOCHS = 50\n    LEARNING_RATE = 1e-3\n    NUM_CLASSES = 2\n    \n    # --- System ---\n    DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-25T14:42:02.959510Z","iopub.execute_input":"2025-07-25T14:42:02.959893Z","iopub.status.idle":"2025-07-25T14:42:03.032095Z","shell.execute_reply.started":"2025-07-25T14:42:02.959873Z","shell.execute_reply":"2025-07-25T14:42:03.031126Z"}},"outputs":[],"execution_count":3},{"cell_type":"code","source":"print(f\"Using device: {Config.DEVICE}\")\nif not os.path.exists(Config.PRETRAINED_ENCODER_PATH):\n    print(f\"ERROR: Pre-trained encoder not found at '{Config.PRETRAINED_ENCODER_PATH}'\")\n    print(\"Please make sure you have run the pre-training script and the path is correct.\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-25T14:42:03.033252Z","iopub.execute_input":"2025-07-25T14:42:03.033643Z","iopub.status.idle":"2025-07-25T14:42:03.059056Z","shell.execute_reply.started":"2025-07-25T14:42:03.033606Z","shell.execute_reply":"2025-07-25T14:42:03.058208Z"}},"outputs":[{"name":"stdout","text":"Using device: cuda\n","output_type":"stream"}],"execution_count":4},{"cell_type":"code","source":"class ResidualBlock(nn.Module):\n    def __init__(self, in_channels, out_channels, stride=1):\n        super().__init__()\n        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)\n        self.bn1 = nn.BatchNorm2d(out_channels)\n        self.relu = nn.ReLU(inplace=True)\n        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)\n        self.bn2 = nn.BatchNorm2d(out_channels)\n        \n        self.shortcut = nn.Sequential()\n        if stride != 1 or in_channels != out_channels:\n            self.shortcut = nn.Sequential(\n                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),\n                nn.BatchNorm2d(out_channels)\n            )\n\n    def forward(self, x):\n        out = self.relu(self.bn1(self.conv1(x)))\n        out = self.bn2(self.conv2(out))\n        out += self.shortcut(x)\n        out = self.relu(out)\n        return out","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-25T14:42:03.059899Z","iopub.execute_input":"2025-07-25T14:42:03.060487Z","iopub.status.idle":"2025-07-25T14:42:03.076833Z","shell.execute_reply.started":"2025-07-25T14:42:03.060437Z","shell.execute_reply":"2025-07-25T14:42:03.076079Z"}},"outputs":[],"execution_count":5},{"cell_type":"code","source":"class Encoder(nn.Module):\n    \"\"\"ResNet-15 Encoder\"\"\"\n    def __init__(self):\n        super().__init__()\n        self.in_channels = 64\n        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)\n        self.bn1 = nn.BatchNorm2d(64)\n        self.relu = nn.ReLU(inplace=True)\n        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n        \n        self.layer1 = self._make_layer(64, 2, stride=1)\n        self.layer2 = self._make_layer(128, 2, stride=2)\n        self.layer3 = self._make_layer(256, 2, stride=2)\n        \n        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n\n    def _make_layer(self, out_channels, num_blocks, stride):\n        strides = [stride] + [1]*(num_blocks-1)\n        layers = []\n        for s in strides:\n            layers.append(ResidualBlock(self.in_channels, out_channels, s))\n            self.in_channels = out_channels\n        return nn.Sequential(*layers)\n\n    def forward(self, x):\n        x = self.relu(self.bn1(self.conv1(x)))\n        x = self.maxpool(x)\n        x = self.layer1(x)\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.avgpool(x)\n        x = torch.flatten(x, 1)\n        return x","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-25T14:42:03.078561Z","iopub.execute_input":"2025-07-25T14:42:03.078761Z","iopub.status.idle":"2025-07-25T14:42:03.094368Z","shell.execute_reply.started":"2025-07-25T14:42:03.078746Z","shell.execute_reply":"2025-07-25T14:42:03.093507Z"}},"outputs":[],"execution_count":6},{"cell_type":"code","source":"class LinearClassifier(nn.Module):\n    \"\"\"A simple linear layer for classification.\"\"\"\n    def __init__(self, in_features, num_classes):\n        super().__init__()\n        self.fc = nn.Linear(in_features, num_classes)\n\n    def forward(self, x):\n        return self.fc(x)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-25T14:42:03.095216Z","iopub.execute_input":"2025-07-25T14:42:03.095875Z","iopub.status.idle":"2025-07-25T14:42:03.115122Z","shell.execute_reply.started":"2025-07-25T14:42:03.095848Z","shell.execute_reply":"2025-07-25T14:42:03.114493Z"}},"outputs":[],"execution_count":7},{"cell_type":"code","source":"class InMemoryJetDataset(Dataset):\n    def __init__(self, h5_path, start_idx, end_idx):\n        print(\"Loading evaluation data into memory...\")\n        with h5py.File(h5_path, 'r') as f:\n            self.images = f['train_jet'][start_idx:end_idx]\n            self.labels = f['train_meta'][start_idx:end_idx, 2]\n        print(\"Data loaded.\")\n        self.length = len(self.images)\n    def __len__(self):\n        return self.length\n    def __getitem__(self, idx):\n        image = self.images[idx]\n        label = self.labels[idx]\n        image_tensor = torch.from_numpy(image).permute(2, 0, 1).float()\n        label_tensor = torch.tensor(int(label), dtype=torch.long)\n        return image_tensor, label_tensor","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-25T14:42:03.116086Z","iopub.execute_input":"2025-07-25T14:42:03.116522Z","iopub.status.idle":"2025-07-25T14:42:03.131267Z","shell.execute_reply.started":"2025-07-25T14:42:03.116489Z","shell.execute_reply":"2025-07-25T14:42:03.130511Z"}},"outputs":[],"execution_count":8},{"cell_type":"code","source":"class EarlyStopping:\n    \"\"\"Stops training when the AUC score does not improve after a given patience.\"\"\"\n    def __init__(self, patience=10, min_delta=0.001, verbose=True):\n        self.patience = patience\n        self.min_delta = min_delta\n        self.verbose = verbose\n        self.counter = 0\n        self.best_auc = 0.0\n        self.early_stop = False\n\n    def __call__(self, current_auc):\n        if current_auc > self.best_auc + self.min_delta:\n            if self.verbose:\n                print(f\"  -> Test AUC improved ({self.best_auc:.4f} --> {current_auc:.4f}).\")\n            self.best_auc = current_auc\n            self.counter = 0\n        else:\n            self.counter += 1\n            if self.verbose:\n                print(f\"  -> EarlyStopping counter: {self.counter} out of {self.patience}\")\n            if self.counter >= self.patience:\n                print(\"  -> Early stopping triggered.\")\n                self.early_stop = True","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-25T14:42:03.132058Z","iopub.execute_input":"2025-07-25T14:42:03.132336Z","iopub.status.idle":"2025-07-25T14:42:03.149744Z","shell.execute_reply.started":"2025-07-25T14:42:03.132320Z","shell.execute_reply":"2025-07-25T14:42:03.149140Z"}},"outputs":[],"execution_count":9},{"cell_type":"code","source":"def run_linear_probe(label_percentage, tuning_pool_indices, tuning_pool_labels, full_eval_dataset, test_dataset):\n    \"\"\"\n    Trains on a % of the tuning_pool_indices and evaluates on the fixed test_dataset.\n    Returns the best test AUC score and the history of AUC scores.\n    \"\"\"\n    print(f\"\\n--- Running Linear Probe for {label_percentage*100:.0f}% Labels ---\")\n    \n    # --- Data Setup ---\n    if label_percentage == 1.0:\n        train_indices = tuning_pool_indices\n    else:\n        train_indices, _ = train_test_split(\n            tuning_pool_indices, train_size=label_percentage,\n            stratify=tuning_pool_labels, random_state=42\n        )\n    train_subset = Subset(full_eval_dataset, train_indices)\n    print(f\"Training on {len(train_subset)} samples, testing on {len(test_dataset)} samples.\")\n    train_loader = DataLoader(train_subset, batch_size=Config.BATCH_SIZE, shuffle=True, num_workers=0)\n    test_loader = DataLoader(test_dataset, batch_size=Config.BATCH_SIZE, shuffle=False, num_workers=0)\n\n    # --- Model Setup ---\n    encoder = Encoder().to(Config.DEVICE)\n    encoder.load_state_dict(torch.load(Config.PRETRAINED_ENCODER_PATH, map_location=Config.DEVICE))\n    encoder.eval() \n    for param in encoder.parameters():\n        param.requires_grad = False\n    with torch.no_grad():\n        dummy_input = torch.randn(1, 3, Config.IMAGE_SIZE, Config.IMAGE_SIZE).to(Config.DEVICE)\n        feature_size = encoder(dummy_input / 255.0).shape[1]\n    classifier = LinearClassifier(feature_size, Config.NUM_CLASSES).to(Config.DEVICE)\n    \n    # --- Training ---\n    optimizer = optim.Adam(classifier.parameters(), lr=Config.LEARNING_RATE)\n    criterion = nn.CrossEntropyLoss()\n    early_stopper = EarlyStopping(patience=10, min_delta=0.0001)\n    \n    best_auc = 0.0\n    auc_history = []\n    for epoch in range(Config.EPOCHS):\n        classifier.train()\n        for images, labels in train_loader:\n            images, labels = images.to(Config.DEVICE), labels.to(Config.DEVICE)\n            images = images / 255.0\n            images = train_transform(images)\n            optimizer.zero_grad()\n            with torch.no_grad():\n                features = encoder(images)\n            outputs = classifier(features)\n            loss = criterion(outputs, labels)\n            loss.backward()\n            optimizer.step()\n        \n        # --- Evaluation ---\n        classifier.eval()\n        all_labels = []\n        all_outputs = []\n        with torch.no_grad():\n            for images, labels in test_loader:\n                images = images.to(Config.DEVICE)\n                images = images / 255.0\n                images = test_transform(images)\n                features = encoder(images)\n                outputs = classifier(features)\n                # Store raw outputs for class 1 and true labels\n                all_labels.extend(labels.cpu().numpy())\n                all_outputs.extend(outputs.cpu().numpy()[:, 1]) # Probability/logit for the positive class\n        \n        # Calculate AUC score for the epoch\n        auc_score = roc_auc_score(all_labels, all_outputs)\n        auc_history.append(auc_score)\n        \n        print(f\"Epoch [{epoch+1}/{Config.EPOCHS}], Test AUC: {auc_score:.4f}\")\n        if auc_score > best_auc:\n            best_auc = auc_score\n        \n        early_stopper(auc_score)\n        if early_stopper.early_stop:\n            break\n            \n    print(f\"Best Test AUC for {label_percentage*100:.0f}%: {best_auc:.4f}\")\n    return best_auc, auc_history","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-25T14:42:03.150517Z","iopub.execute_input":"2025-07-25T14:42:03.150813Z","iopub.status.idle":"2025-07-25T14:42:03.165724Z","shell.execute_reply.started":"2025-07-25T14:42:03.150789Z","shell.execute_reply":"2025-07-25T14:42:03.164990Z"}},"outputs":[],"execution_count":10},{"cell_type":"code","source":"if __name__ == '__main__':\n    # --- GPU-based Augmentation Pipeline ---\n    train_transform = nn.Sequential(\n        T.RandomHorizontalFlip(),\n        T.RandomVerticalFlip(),\n        T.RandomRotation(degrees=Config.ROTATION_DEGREES),\n        T.GaussianBlur(kernel_size=5, sigma=Config.GAUSSIAN_BLUR_SIGMA)\n    ).to(Config.DEVICE)\n    test_transform = nn.Identity().to(Config.DEVICE)\n    \n    # --- Load Data Once ---\n    full_eval_dataset = InMemoryJetDataset(\n        Config.DATA_FILE_PATH, \n        Config.EVAL_START_IDX, \n        Config.EVAL_END_IDX\n    )\n    \n    # --- Create Fixed Data Splits (Official Protocol) ---\n    all_indices = list(range(len(full_eval_dataset)))\n    all_labels = full_eval_dataset.labels\n    tuning_pool_indices, test_indices, tuning_pool_labels, _ = train_test_split(\n        all_indices, all_labels,\n        test_size=Config.TEST_SET_FRACTION, stratify=all_labels, random_state=42\n    )\n    test_dataset = Subset(full_eval_dataset, test_indices)\n    \n    # --- Run Experiments ---\n    label_percentages = [0.01, 0.10, 0.25, 0.50, 1.0]\n    results_auc = {}\n    history_auc = {}\n    \n    for percent in label_percentages:\n        best_auc, auc_history = run_linear_probe(percent, tuning_pool_indices, tuning_pool_labels, full_eval_dataset, test_dataset)\n        results_auc[f\"{int(percent*100)}%\"] = best_auc\n        history_auc[f\"{int(percent*100)}%\"] = auc_history\n        \n    # --- Plotting Results ---\n    print(\"\\n--- Final Results ---\")\n    for percent, acc in results_auc.items():\n        print(f\"Best AUC with {percent} labels: {acc:.4f}\")\n        \n    plt.style.use('seaborn-v0_8-whitegrid')\n    \n    # Plot 1: Bar chart of the best AUC scores\n    fig1, ax1 = plt.subplots(figsize=(10, 6))\n    percentages = list(results_auc.keys())\n    accuracies = list(results_auc.values())\n    bars = ax1.bar(percentages, accuracies, color=['#ff9999','#66b3ff','#99ff99','#ffcc99', '#c2c2f0'])\n    ax1.set_ylabel('ROC AUC Score', fontsize=14)\n    ax1.set_xlabel('Percentage of Labeled Data Used (from Tuning Pool)', fontsize=14)\n    ax1.set_title('SimCLR Linear Probing Performance (AUC)', fontsize=16, weight='bold')\n    ax1.set_ylim(0, 1.0)\n    for bar in bars:\n        yval = bar.get_height()\n        ax1.text(bar.get_x() + bar.get_width()/2.0, yval + 0.02, f'{yval:.4f}', ha='center', va='bottom', fontsize=12)\n    plt.xticks(fontsize=12); plt.yticks(fontsize=12); plt.tight_layout()\n    plt.show()\n\n    # Plot 2: Line chart of AUC vs. Epochs for each percentage\n    fig2, ax2 = plt.subplots(figsize=(12, 8))\n    for percent_str, history in history_auc.items():\n        ax2.plot(range(1, len(history) + 1), history, marker='o', linestyle='-', label=f'{percent_str} Labels')\n    ax2.set_ylabel('Test ROC AUC', fontsize=14)\n    ax2.set_xlabel('Epoch', fontsize=14)\n    ax2.set_title('AUC Score vs. Epochs for Different Label Percentages', fontsize=16, weight='bold')\n    ax2.set_ylim(bottom=0.5, top=max(max(h) for h in history_auc.values()) * 1.05 if any(history_auc.values()) else 1.0)\n    ax2.grid(True, which='both', linestyle='--', linewidth=0.5)\n    ax2.legend(fontsize=12)\n    plt.xticks(fontsize=12); plt.yticks(fontsize=12); plt.tight_layout()\n    plt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-25T14:57:16.958037Z","iopub.execute_input":"2025-07-25T14:57:16.958704Z","iopub.status.idle":"2025-07-25T15:10:15.975679Z","shell.execute_reply.started":"2025-07-25T14:57:16.958672Z","shell.execute_reply":"2025-07-25T15:10:15.974923Z"}},"outputs":[{"name":"stdout","text":"Loading evaluation data into memory...\nData loaded.\n\n--- Running Linear Probe for 1% Labels ---\nTraining on 150 samples, testing on 15000 samples.\nEpoch [1/50], Test AUC: 0.5817\n  -> Test AUC improved (0.0000 --> 0.5817).\nEpoch [2/50], Test AUC: 0.5944\n  -> Test AUC improved (0.5817 --> 0.5944).\nEpoch [3/50], Test AUC: 0.6054\n  -> Test AUC improved (0.5944 --> 0.6054).\nEpoch [4/50], Test AUC: 0.6117\n  -> Test AUC improved (0.6054 --> 0.6117).\nEpoch [5/50], Test AUC: 0.6154\n  -> Test AUC improved (0.6117 --> 0.6154).\nEpoch [6/50], Test AUC: 0.6178\n  -> Test AUC improved (0.6154 --> 0.6178).\nEpoch [7/50], Test AUC: 0.6200\n  -> Test AUC improved (0.6178 --> 0.6200).\nEpoch [8/50], Test AUC: 0.6217\n  -> Test AUC improved (0.6200 --> 0.6217).\nEpoch [9/50], Test AUC: 0.6223\n  -> Test AUC improved (0.6217 --> 0.6223).\nEpoch [10/50], Test AUC: 0.6231\n  -> Test AUC improved (0.6223 --> 0.6231).\nEpoch [11/50], Test AUC: 0.6213\n  -> EarlyStopping counter: 1 out of 10\nEpoch [12/50], Test AUC: 0.6172\n  -> EarlyStopping counter: 2 out of 10\nEpoch [13/50], Test AUC: 0.6132\n  -> EarlyStopping counter: 3 out of 10\nEpoch [14/50], Test AUC: 0.6102\n  -> EarlyStopping counter: 4 out of 10\nEpoch [15/50], Test AUC: 0.6081\n  -> EarlyStopping counter: 5 out of 10\nEpoch [16/50], Test AUC: 0.6064\n  -> EarlyStopping counter: 6 out of 10\nEpoch [17/50], Test AUC: 0.6043\n  -> EarlyStopping counter: 7 out of 10\nEpoch [18/50], Test AUC: 0.6024\n  -> EarlyStopping counter: 8 out of 10\nEpoch [19/50], Test AUC: 0.6020\n  -> EarlyStopping counter: 9 out of 10\nEpoch [20/50], Test AUC: 0.6034\n  -> EarlyStopping counter: 10 out of 10\n  -> Early stopping triggered.\nBest Test AUC for 1%: 0.6231\n\n--- Running Linear Probe for 10% Labels ---\nTraining on 1500 samples, testing on 15000 samples.\nEpoch [1/50], Test AUC: 0.6652\n  -> Test AUC improved (0.0000 --> 0.6652).\nEpoch [2/50], Test AUC: 0.6936\n  -> Test AUC improved (0.6652 --> 0.6936).\nEpoch [3/50], Test AUC: 0.7014\n  -> Test AUC improved (0.6936 --> 0.7014).\nEpoch [4/50], Test AUC: 0.7027\n  -> Test AUC improved (0.7014 --> 0.7027).\nEpoch [5/50], Test AUC: 0.7035\n  -> Test AUC improved (0.7027 --> 0.7035).\nEpoch [6/50], Test AUC: 0.7091\n  -> Test AUC improved (0.7035 --> 0.7091).\nEpoch [7/50], Test AUC: 0.7080\n  -> EarlyStopping counter: 1 out of 10\nEpoch [8/50], Test AUC: 0.7127\n  -> Test AUC improved (0.7091 --> 0.7127).\nEpoch [9/50], Test AUC: 0.7099\n  -> EarlyStopping counter: 1 out of 10\nEpoch [10/50], Test AUC: 0.7146\n  -> Test AUC improved (0.7127 --> 0.7146).\nEpoch [11/50], Test AUC: 0.7157\n  -> Test AUC improved (0.7146 --> 0.7157).\nEpoch [12/50], Test AUC: 0.7140\n  -> EarlyStopping counter: 1 out of 10\nEpoch [13/50], Test AUC: 0.7123\n  -> EarlyStopping counter: 2 out of 10\nEpoch [14/50], Test AUC: 0.7118\n  -> EarlyStopping counter: 3 out of 10\nEpoch [15/50], Test AUC: 0.7158\n  -> EarlyStopping counter: 4 out of 10\nEpoch [16/50], Test AUC: 0.7131\n  -> EarlyStopping counter: 5 out of 10\nEpoch [17/50], Test AUC: 0.7175\n  -> Test AUC improved (0.7157 --> 0.7175).\nEpoch [18/50], Test AUC: 0.7206\n  -> Test AUC improved (0.7175 --> 0.7206).\nEpoch [19/50], Test AUC: 0.7167\n  -> EarlyStopping counter: 1 out of 10\nEpoch [20/50], Test AUC: 0.7177\n  -> EarlyStopping counter: 2 out of 10\nEpoch [21/50], Test AUC: 0.7186\n  -> EarlyStopping counter: 3 out of 10\nEpoch [22/50], Test AUC: 0.7187\n  -> EarlyStopping counter: 4 out of 10\nEpoch [23/50], Test AUC: 0.7177\n  -> EarlyStopping counter: 5 out of 10\nEpoch [24/50], Test AUC: 0.7169\n  -> EarlyStopping counter: 6 out of 10\nEpoch [25/50], Test AUC: 0.7151\n  -> EarlyStopping counter: 7 out of 10\nEpoch [26/50], Test AUC: 0.7161\n  -> EarlyStopping counter: 8 out of 10\nEpoch [27/50], Test AUC: 0.7178\n  -> EarlyStopping counter: 9 out of 10\nEpoch [28/50], Test AUC: 0.7185\n  -> EarlyStopping counter: 10 out of 10\n  -> Early stopping triggered.\nBest Test AUC for 10%: 0.7206\n\n--- Running Linear Probe for 25% Labels ---\nTraining on 3750 samples, testing on 15000 samples.\nEpoch [1/50], Test AUC: 0.6616\n  -> Test AUC improved (0.0000 --> 0.6616).\nEpoch [2/50], Test AUC: 0.6716\n  -> Test AUC improved (0.6616 --> 0.6716).\nEpoch [3/50], Test AUC: 0.6822\n  -> Test AUC improved (0.6716 --> 0.6822).\nEpoch [4/50], Test AUC: 0.6798\n  -> EarlyStopping counter: 1 out of 10\nEpoch [5/50], Test AUC: 0.6880\n  -> Test AUC improved (0.6822 --> 0.6880).\nEpoch [6/50], Test AUC: 0.6900\n  -> Test AUC improved (0.6880 --> 0.6900).\nEpoch [7/50], Test AUC: 0.6895\n  -> EarlyStopping counter: 1 out of 10\nEpoch [8/50], Test AUC: 0.6933\n  -> Test AUC improved (0.6900 --> 0.6933).\nEpoch [9/50], Test AUC: 0.6903\n  -> EarlyStopping counter: 1 out of 10\nEpoch [10/50], Test AUC: 0.6925\n  -> EarlyStopping counter: 2 out of 10\nEpoch [11/50], Test AUC: 0.6941\n  -> Test AUC improved (0.6933 --> 0.6941).\nEpoch [12/50], Test AUC: 0.6894\n  -> EarlyStopping counter: 1 out of 10\nEpoch [13/50], Test AUC: 0.6957\n  -> Test AUC improved (0.6941 --> 0.6957).\nEpoch [14/50], Test AUC: 0.6906\n  -> EarlyStopping counter: 1 out of 10\nEpoch [15/50], Test AUC: 0.6984\n  -> Test AUC improved (0.6957 --> 0.6984).\nEpoch [16/50], Test AUC: 0.6994\n  -> Test AUC improved (0.6984 --> 0.6994).\nEpoch [17/50], Test AUC: 0.6987\n  -> EarlyStopping counter: 1 out of 10\nEpoch [18/50], Test AUC: 0.6967\n  -> EarlyStopping counter: 2 out of 10\nEpoch [19/50], Test AUC: 0.6979\n  -> EarlyStopping counter: 3 out of 10\nEpoch [20/50], Test AUC: 0.6998\n  -> Test AUC improved (0.6994 --> 0.6998).\nEpoch [21/50], Test AUC: 0.7005\n  -> Test AUC improved (0.6998 --> 0.7005).\nEpoch [22/50], Test AUC: 0.6950\n  -> EarlyStopping counter: 1 out of 10\nEpoch [23/50], Test AUC: 0.6964\n  -> EarlyStopping counter: 2 out of 10\nEpoch [24/50], Test AUC: 0.6981\n  -> EarlyStopping counter: 3 out of 10\nEpoch [25/50], Test AUC: 0.6973\n  -> EarlyStopping counter: 4 out of 10\nEpoch [26/50], Test AUC: 0.6990\n  -> EarlyStopping counter: 5 out of 10\nEpoch [27/50], Test AUC: 0.7014\n  -> Test AUC improved (0.7005 --> 0.7014).\nEpoch [28/50], Test AUC: 0.6990\n  -> EarlyStopping counter: 1 out of 10\nEpoch [29/50], Test AUC: 0.6980\n  -> EarlyStopping counter: 2 out of 10\nEpoch [30/50], Test AUC: 0.7044\n  -> Test AUC improved (0.7014 --> 0.7044).\nEpoch [31/50], Test AUC: 0.6995\n  -> EarlyStopping counter: 1 out of 10\nEpoch [32/50], Test AUC: 0.6961\n  -> EarlyStopping counter: 2 out of 10\nEpoch [33/50], Test AUC: 0.6993\n  -> EarlyStopping counter: 3 out of 10\nEpoch [34/50], Test AUC: 0.6960\n  -> EarlyStopping counter: 4 out of 10\nEpoch [35/50], Test AUC: 0.6956\n  -> EarlyStopping counter: 5 out of 10\nEpoch [36/50], Test AUC: 0.6951\n  -> EarlyStopping counter: 6 out of 10\nEpoch [37/50], Test AUC: 0.6951\n  -> EarlyStopping counter: 7 out of 10\nEpoch [38/50], Test AUC: 0.7031\n  -> EarlyStopping counter: 8 out of 10\nEpoch [39/50], Test AUC: 0.7025\n  -> EarlyStopping counter: 9 out of 10\nEpoch [40/50], Test AUC: 0.6959\n  -> EarlyStopping counter: 10 out of 10\n  -> Early stopping triggered.\nBest Test AUC for 25%: 0.7044\n\n--- Running Linear Probe for 50% Labels ---\nTraining on 7500 samples, testing on 15000 samples.\nEpoch [1/50], Test AUC: 0.7052\n  -> Test AUC improved (0.0000 --> 0.7052).\nEpoch [2/50], Test AUC: 0.7077\n  -> Test AUC improved (0.7052 --> 0.7077).\nEpoch [3/50], Test AUC: 0.7046\n  -> EarlyStopping counter: 1 out of 10\nEpoch [4/50], Test AUC: 0.7074\n  -> EarlyStopping counter: 2 out of 10\nEpoch [5/50], Test AUC: 0.7070\n  -> EarlyStopping counter: 3 out of 10\nEpoch [6/50], Test AUC: 0.7064\n  -> EarlyStopping counter: 4 out of 10\nEpoch [7/50], Test AUC: 0.7042\n  -> EarlyStopping counter: 5 out of 10\nEpoch [8/50], Test AUC: 0.7088\n  -> Test AUC improved (0.7077 --> 0.7088).\nEpoch [9/50], Test AUC: 0.7027\n  -> EarlyStopping counter: 1 out of 10\nEpoch [10/50], Test AUC: 0.7076\n  -> EarlyStopping counter: 2 out of 10\nEpoch [11/50], Test AUC: 0.7040\n  -> EarlyStopping counter: 3 out of 10\nEpoch [12/50], Test AUC: 0.7080\n  -> EarlyStopping counter: 4 out of 10\nEpoch [13/50], Test AUC: 0.7049\n  -> EarlyStopping counter: 5 out of 10\nEpoch [14/50], Test AUC: 0.7101\n  -> Test AUC improved (0.7088 --> 0.7101).\nEpoch [15/50], Test AUC: 0.7111\n  -> Test AUC improved (0.7101 --> 0.7111).\nEpoch [16/50], Test AUC: 0.7081\n  -> EarlyStopping counter: 1 out of 10\nEpoch [17/50], Test AUC: 0.7103\n  -> EarlyStopping counter: 2 out of 10\nEpoch [18/50], Test AUC: 0.7050\n  -> EarlyStopping counter: 3 out of 10\nEpoch [19/50], Test AUC: 0.7040\n  -> EarlyStopping counter: 4 out of 10\nEpoch [20/50], Test AUC: 0.7052\n  -> EarlyStopping counter: 5 out of 10\nEpoch [21/50], Test AUC: 0.7089\n  -> EarlyStopping counter: 6 out of 10\nEpoch [22/50], Test AUC: 0.7046\n  -> EarlyStopping counter: 7 out of 10\nEpoch [23/50], Test AUC: 0.7060\n  -> EarlyStopping counter: 8 out of 10\nEpoch [24/50], Test AUC: 0.7030\n  -> EarlyStopping counter: 9 out of 10\nEpoch [25/50], Test AUC: 0.7108\n  -> EarlyStopping counter: 10 out of 10\n  -> Early stopping triggered.\nBest Test AUC for 50%: 0.7111\n\n--- Running Linear Probe for 100% Labels ---\nTraining on 15000 samples, testing on 15000 samples.\nEpoch [1/50], Test AUC: 0.7018\n  -> Test AUC improved (0.0000 --> 0.7018).\nEpoch [2/50], Test AUC: 0.7117\n  -> Test AUC improved (0.7018 --> 0.7117).\nEpoch [3/50], Test AUC: 0.7137\n  -> Test AUC improved (0.7117 --> 0.7137).\nEpoch [4/50], Test AUC: 0.7152\n  -> Test AUC improved (0.7137 --> 0.7152).\nEpoch [5/50], Test AUC: 0.7136\n  -> EarlyStopping counter: 1 out of 10\nEpoch [6/50], Test AUC: 0.7104\n  -> EarlyStopping counter: 2 out of 10\nEpoch [7/50], Test AUC: 0.7161\n  -> Test AUC improved (0.7152 --> 0.7161).\nEpoch [8/50], Test AUC: 0.7167\n  -> Test AUC improved (0.7161 --> 0.7167).\nEpoch [9/50], Test AUC: 0.7174\n  -> Test AUC improved (0.7167 --> 0.7174).\nEpoch [10/50], Test AUC: 0.7138\n  -> EarlyStopping counter: 1 out of 10\nEpoch [11/50], Test AUC: 0.7155\n  -> EarlyStopping counter: 2 out of 10\nEpoch [12/50], Test AUC: 0.7186\n  -> Test AUC improved (0.7174 --> 0.7186).\nEpoch [13/50], Test AUC: 0.7175\n  -> EarlyStopping counter: 1 out of 10\nEpoch [14/50], Test AUC: 0.7206\n  -> Test AUC improved (0.7186 --> 0.7206).\nEpoch [15/50], Test AUC: 0.7191\n  -> EarlyStopping counter: 1 out of 10\nEpoch [16/50], Test AUC: 0.7168\n  -> EarlyStopping counter: 2 out of 10\nEpoch [17/50], Test AUC: 0.7182\n  -> EarlyStopping counter: 3 out of 10\nEpoch [18/50], Test AUC: 0.7118\n  -> EarlyStopping counter: 4 out of 10\nEpoch [19/50], Test AUC: 0.7177\n  -> EarlyStopping counter: 5 out of 10\nEpoch [20/50], Test AUC: 0.7181\n  -> EarlyStopping counter: 6 out of 10\nEpoch [21/50], Test AUC: 0.7180\n  -> EarlyStopping counter: 7 out of 10\nEpoch [22/50], Test AUC: 0.7149\n  -> EarlyStopping counter: 8 out of 10\nEpoch [23/50], Test AUC: 0.7222\n  -> Test AUC improved (0.7206 --> 0.7222).\nEpoch [24/50], Test AUC: 0.7173\n  -> EarlyStopping counter: 1 out of 10\nEpoch [25/50], Test AUC: 0.7167\n  -> EarlyStopping counter: 2 out of 10\nEpoch [26/50], Test AUC: 0.7188\n  -> EarlyStopping counter: 3 out of 10\nEpoch [27/50], Test AUC: 0.7163\n  -> EarlyStopping counter: 4 out of 10\nEpoch [28/50], Test AUC: 0.7165\n  -> EarlyStopping counter: 5 out of 10\nEpoch [29/50], Test AUC: 0.7174\n  -> EarlyStopping counter: 6 out of 10\nEpoch [30/50], Test AUC: 0.7147\n  -> EarlyStopping counter: 7 out of 10\nEpoch [31/50], Test AUC: 0.7151\n  -> EarlyStopping counter: 8 out of 10\nEpoch [32/50], Test AUC: 0.7124\n  -> EarlyStopping counter: 9 out of 10\nEpoch [33/50], Test AUC: 0.7206\n  -> EarlyStopping counter: 10 out of 10\n  -> Early stopping triggered.\nBest Test AUC for 100%: 0.7222\n\n--- Final Results ---\nBest AUC with 1% labels: 0.6231\nBest AUC with 10% labels: 0.7206\nBest AUC with 25% labels: 0.7044\nBest AUC with 50% labels: 0.7111\nBest AUC with 100% labels: 0.7222\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x600 with 1 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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,"execution_count":null}]}
\ No newline at end of file
diff --git a/E2E_Foundation_Model_Aaditya_Porwal/NoteBooks/Classification/Supervised_Baseline_Classification.ipynb b/E2E_Foundation_Model_Aaditya_Porwal/NoteBooks/Classification/Supervised_Baseline_Classification.ipynb
new file mode 100644
index 0000000..93787b9
--- /dev/null
+++ b/E2E_Foundation_Model_Aaditya_Porwal/NoteBooks/Classification/Supervised_Baseline_Classification.ipynb
@@ -0,0 +1 @@
+{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","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":"gpu","dataSources":[{"sourceId":12447031,"sourceType":"datasetVersion","datasetId":7851613}],"dockerImageVersionId":31090,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true},"papermill":{"default_parameters":{},"duration":6862.311693,"end_time":"2025-04-02T16:22:17.104023","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2025-04-02T14:27:54.792330","version":"2.6.0"}},"nbformat_minor":5,"nbformat":4,"cells":[{"id":"a456005b-067e-41e9-8185-3eaf196b5109","cell_type":"code","source":"import torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import Dataset, DataLoader, Subset\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import roc_auc_score\nimport torchvision.transforms as T\nimport matplotlib.pyplot as plt\n\nimport h5py\nimport numpy as np\nfrom tqdm.auto import tqdm\nimport os\n\n# --- 1. Configuration ---\nclass Config:\n    # --- Paths ---\n    DATA_FILE_PATH = \"/kaggle/input/qc0-1million/train_jet_train_meta_100k.h5\"\n    \n    # --- Data ---\n    IMAGE_SIZE = 125\n    EVAL_START_IDX = 70000\n    EVAL_END_IDX = 100000\n    TEST_SET_FRACTION = 0.5 # Use 50% of the 30k samples as a fixed test set\n    \n    # --- Augmentation ---\n    ROTATION_DEGREES = 60.0\n    GAUSSIAN_BLUR_SIGMA = (0.1, 2.0)\n    \n    # --- Training ---\n    BATCH_SIZE = 128\n    EPOCHS = 50\n    LEARNING_RATE = 1e-3\n    NUM_CLASSES = 2\n    \n    # --- System ---\n    DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\nprint(f\"Using device: {Config.DEVICE}\")\n\n# --- 2. Model Architecture ---\nclass ResidualBlock(nn.Module):\n    def __init__(self, in_channels, out_channels, stride=1):\n        super().__init__()\n        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)\n        self.bn1 = nn.BatchNorm2d(out_channels)\n        self.relu = nn.ReLU(inplace=True)\n        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)\n        self.bn2 = nn.BatchNorm2d(out_channels)\n        self.shortcut = nn.Sequential()\n        if stride != 1 or in_channels != out_channels:\n            self.shortcut = nn.Sequential(\n                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),\n                nn.BatchNorm2d(out_channels)\n            )\n    def forward(self, x):\n        out = self.relu(self.bn1(self.conv1(x)))\n        out = self.bn2(self.conv2(out))\n        out += self.shortcut(x)\n        out = self.relu(out)\n        return out\n\nclass Encoder(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.in_channels = 64\n        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)\n        self.bn1 = nn.BatchNorm2d(64)\n        self.relu = nn.ReLU(inplace=True)\n        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n        self.layer1 = self._make_layer(64, 2, stride=1)\n        self.layer2 = self._make_layer(128, 2, stride=2)\n        self.layer3 = self._make_layer(256, 2, stride=2)\n        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n    def _make_layer(self, out_channels, num_blocks, stride):\n        strides = [stride] + [1]*(num_blocks-1)\n        layers = []\n        for s in strides:\n            layers.append(ResidualBlock(self.in_channels, out_channels, s))\n            self.in_channels = out_channels\n        return nn.Sequential(*layers)\n    def forward(self, x):\n        x = self.relu(self.bn1(self.conv1(x)))\n        x = self.maxpool(x)\n        x = self.layer1(x)\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.avgpool(x)\n        x = torch.flatten(x, 1)\n        return x\n\nclass FullClassifier(nn.Module):\n    \"\"\"A model combining the encoder and a linear classifier.\"\"\"\n    def __init__(self, num_classes):\n        super().__init__()\n        self.encoder = Encoder()\n        # Determine feature size dynamically\n        with torch.no_grad():\n            dummy_input = torch.randn(1, 3, Config.IMAGE_SIZE, Config.IMAGE_SIZE)\n            feature_size = self.encoder(dummy_input / 255.0).shape[1]\n        self.classifier = nn.Linear(feature_size, num_classes)\n\n    def forward(self, x):\n        features = self.encoder(x)\n        return self.classifier(features)\n\n# --- 3. Data Pipeline (In-Memory for Speed) ---\nclass InMemoryJetDataset(Dataset):\n    def __init__(self, h5_path, start_idx, end_idx):\n        print(\"Loading evaluation data into memory...\")\n        with h5py.File(h5_path, 'r') as f:\n            self.images = f['train_jet'][start_idx:end_idx]\n            self.labels = f['train_meta'][start_idx:end_idx, 2]\n        print(\"Data loaded.\")\n    def __len__(self):\n        return len(self.images)\n    def __getitem__(self, idx):\n        image = self.images[idx]\n        label = self.labels[idx]\n        image_tensor = torch.from_numpy(image).permute(2, 0, 1).float()\n        label_tensor = torch.tensor(int(label), dtype=torch.long)\n        return image_tensor, label_tensor\n\n# --- 4. Early Stopping Class (Monitors AUC) ---\nclass EarlyStopping:\n    def __init__(self, patience=10, min_delta=0.0001, verbose=True):\n        self.patience, self.min_delta, self.verbose = patience, min_delta, verbose\n        self.counter, self.best_auc, self.early_stop = 0, 0.0, False\n\n    def __call__(self, current_auc):\n        if current_auc > self.best_auc + self.min_delta:\n            if self.verbose:\n                print(f\"  -> Test AUC improved ({self.best_auc:.4f} --> {current_auc:.4f}).\")\n            self.best_auc = current_auc\n            self.counter = 0\n        else:\n            self.counter += 1\n            if self.verbose:\n                print(f\"  -> EarlyStopping counter: {self.counter} out of {self.patience}\")\n            if self.counter >= self.patience:\n                print(\"  -> Early stopping triggered.\")\n                self.early_stop = True\n\n# --- 5. Training and Evaluation Function ---\ndef run_supervised_training(label_percentage, tuning_pool_indices, tuning_pool_labels, full_eval_dataset, test_dataset):\n    print(f\"\\n--- Training from Scratch with {label_percentage*100:.0f}% Labels ---\")\n    \n    # Data Setup\n    if label_percentage == 1.0:\n        train_indices = tuning_pool_indices\n    else:\n        train_indices, _ = train_test_split(\n            tuning_pool_indices, train_size=label_percentage,\n            stratify=tuning_pool_labels, random_state=42\n        )\n    train_subset = Subset(full_eval_dataset, train_indices)\n    print(f\"Training on {len(train_subset)} samples, testing on {len(test_dataset)} samples.\")\n    train_loader = DataLoader(train_subset, batch_size=Config.BATCH_SIZE, shuffle=True, num_workers=0)\n    test_loader = DataLoader(test_dataset, batch_size=Config.BATCH_SIZE, shuffle=False, num_workers=0)\n\n    # Model Setup: Initialize a new model from scratch for each run\n    model = FullClassifier(num_classes=Config.NUM_CLASSES).to(Config.DEVICE)\n    \n    # Training\n    optimizer = optim.Adam(model.parameters(), lr=Config.LEARNING_RATE)\n    criterion = nn.CrossEntropyLoss()\n    early_stopper = EarlyStopping(patience=5)\n    \n    best_auc = 0.0\n    auc_history = []\n    for epoch in range(Config.EPOCHS):\n        model.train()\n        for images, labels in train_loader:\n            images, labels = images.to(Config.DEVICE), labels.to(Config.DEVICE)\n            images = images / 255.0\n            images = train_transform(images)\n            optimizer.zero_grad()\n            outputs = model(images)\n            loss = criterion(outputs, labels)\n            loss.backward()\n            optimizer.step()\n        \n        # Evaluation\n        model.eval()\n        all_labels, all_outputs = [], []\n        with torch.no_grad():\n            for images, labels in test_loader:\n                images, labels = images.to(Config.DEVICE), labels.to(Config.DEVICE)\n                images = images / 255.0\n                images = test_transform(images)\n                outputs = model(images)\n                all_labels.extend(labels.cpu().numpy())\n                all_outputs.extend(torch.softmax(outputs, dim=1)[:, 1].cpu().numpy())\n        \n        auc_score = roc_auc_score(all_labels, all_outputs)\n        auc_history.append(auc_score)\n        \n        print(f\"Epoch [{epoch+1}/{Config.EPOCHS}], Test AUC: {auc_score:.4f}\")\n        if auc_score > best_auc: best_auc = auc_score\n        \n        early_stopper(auc_score)\n        if early_stopper.early_stop: break\n            \n    print(f\"Best Test AUC for {label_percentage*100:.0f}%: {best_auc:.4f}\")\n    return best_auc, auc_history\n\n# --- 6. Main Execution ---\nif __name__ == '__main__':\n    # Augmentations\n    train_transform = nn.Sequential(\n        T.RandomHorizontalFlip(),\n        T.RandomVerticalFlip(),\n        T.RandomRotation(degrees=Config.ROTATION_DEGREES),\n        T.GaussianBlur(kernel_size=5, sigma=Config.GAUSSIAN_BLUR_SIGMA)\n    ).to(Config.DEVICE)\n    test_transform = nn.Identity().to(Config.DEVICE)\n    \n    # Load Data\n    full_eval_dataset = InMemoryJetDataset(Config.DATA_FILE_PATH, Config.EVAL_START_IDX, Config.EVAL_END_IDX)\n    \n    # Create Fixed Data Splits\n    all_indices, all_labels = list(range(len(full_eval_dataset))), full_eval_dataset.labels\n    tuning_pool_indices, test_indices, tuning_pool_labels, _ = train_test_split(\n        all_indices, all_labels, test_size=0.5, stratify=all_labels, random_state=42\n    )\n    test_dataset = Subset(full_eval_dataset, test_indices)\n    \n    # Run Experiments\n    label_percentages = [0.01, 0.10, 0.25, 0.50, 1.0]\n    results_auc, history_auc = {}, {}\n    \n    for percent in label_percentages:\n        best_auc, auc_history = run_supervised_training(percent, tuning_pool_indices, tuning_pool_labels, full_eval_dataset, test_dataset)\n        results_auc[f\"{int(percent*100)}%\"] = best_auc\n        history_auc[f\"{int(percent*100)}%\"] = auc_history\n        \n    # Plotting Results\n    print(\"\\n--- Final Results (Supervised Training from Scratch) ---\")\n    for percent, auc in results_auc.items(): print(f\"Best AUC with {percent} labels: {auc:.4f}\")\n        \n    plt.style.use('seaborn-v0_8-whitegrid')\n    \n    # Plot 1: Bar chart of best AUC scores\n    fig1, ax1 = plt.subplots(figsize=(10, 6))\n    bars = ax1.bar(results_auc.keys(), results_auc.values(), color=['#ff9999','#66b3ff','#99ff99','#ffcc99', '#c2c2f0'])\n    ax1.set_ylabel('ROC AUC Score', fontsize=14)\n    ax1.set_xlabel('Percentage of Labeled Data Used', fontsize=14)\n    ax1.set_title('Supervised Baseline Performance (AUC)', fontsize=16, weight='bold')\n    ax1.set_ylim(0.5, 1.0)\n    for bar in bars:\n        yval = bar.get_height()\n        ax1.text(bar.get_x() + bar.get_width()/2.0, yval + 0.01, f'{yval:.4f}', ha='center', va='bottom', fontsize=12)\n    plt.show()\n\n    # Plot 2: Line chart of AUC vs. Epochs\n    fig2, ax2 = plt.subplots(figsize=(12, 8))\n    for percent_str, history in history_auc.items():\n        ax2.plot(range(1, len(history) + 1), history, marker='o', linestyle='-', label=f'{percent_str} Labels')\n    ax2.set_ylabel('Test ROC AUC', fontsize=14)\n    ax2.set_xlabel('Epoch', fontsize=14)\n    ax2.set_title('Supervised Baseline: AUC vs. Epochs', fontsize=16, weight='bold')\n    ax2.grid(True, which='both', linestyle='--')\n    ax2.legend(fontsize=12)\n    plt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-23T06:11:30.569369Z","iopub.execute_input":"2025-07-23T06:11:30.569670Z","iopub.status.idle":"2025-07-23T06:32:00.140590Z","shell.execute_reply.started":"2025-07-23T06:11:30.569646Z","shell.execute_reply":"2025-07-23T06:32:00.139780Z"}},"outputs":[{"name":"stdout","text":"Using device: cuda\nLoading evaluation data into memory...\nData loaded.\n\n--- Training from Scratch with 1% Labels ---\nTraining on 150 samples, testing on 15000 samples.\nEpoch [1/50], Test AUC: 0.4935\n  -> Test AUC improved (0.0000 --> 0.4935).\nEpoch [2/50], Test AUC: 0.6504\n  -> Test AUC improved (0.4935 --> 0.6504).\nEpoch [3/50], Test AUC: 0.6640\n  -> Test AUC improved (0.6504 --> 0.6640).\nEpoch [4/50], Test AUC: 0.6606\n  -> EarlyStopping counter: 1 out of 10\nEpoch [5/50], Test AUC: 0.6775\n  -> Test AUC improved (0.6640 --> 0.6775).\nEpoch [6/50], Test AUC: 0.6798\n  -> Test AUC improved (0.6775 --> 0.6798).\nEpoch [7/50], Test AUC: 0.6815\n  -> Test AUC improved (0.6798 --> 0.6815).\nEpoch [8/50], Test AUC: 0.6809\n  -> EarlyStopping counter: 1 out of 10\nEpoch [9/50], Test AUC: 0.6810\n  -> EarlyStopping counter: 2 out of 10\nEpoch [10/50], Test AUC: 0.6813\n  -> EarlyStopping counter: 3 out of 10\nEpoch [11/50], Test AUC: 0.6823\n  -> Test AUC improved (0.6815 --> 0.6823).\nEpoch [12/50], Test AUC: 0.6838\n  -> Test AUC improved (0.6823 --> 0.6838).\nEpoch [13/50], Test AUC: 0.6846\n  -> Test AUC improved (0.6838 --> 0.6846).\nEpoch [14/50], Test AUC: 0.6858\n  -> Test AUC improved (0.6846 --> 0.6858).\nEpoch [15/50], Test AUC: 0.6859\n  -> EarlyStopping counter: 1 out of 10\nEpoch [16/50], Test AUC: 0.6794\n  -> EarlyStopping counter: 2 out of 10\nEpoch [17/50], Test AUC: 0.6722\n  -> EarlyStopping counter: 3 out of 10\nEpoch [18/50], Test AUC: 0.6591\n  -> EarlyStopping counter: 4 out of 10\nEpoch [19/50], Test AUC: 0.6575\n  -> EarlyStopping counter: 5 out of 10\nEpoch [20/50], Test AUC: 0.6669\n  -> EarlyStopping counter: 6 out of 10\nEpoch [21/50], Test AUC: 0.6767\n  -> EarlyStopping counter: 7 out of 10\nEpoch [22/50], Test AUC: 0.6821\n  -> EarlyStopping counter: 8 out of 10\nEpoch [23/50], Test AUC: 0.6851\n  -> EarlyStopping counter: 9 out of 10\nEpoch [24/50], Test AUC: 0.6879\n  -> Test AUC improved (0.6858 --> 0.6879).\nEpoch [25/50], Test AUC: 0.6881\n  -> Test AUC improved (0.6879 --> 0.6881).\nEpoch [26/50], Test AUC: 0.6867\n  -> EarlyStopping counter: 1 out of 10\nEpoch [27/50], Test AUC: 0.6818\n  -> EarlyStopping counter: 2 out of 10\nEpoch [28/50], Test AUC: 0.6695\n  -> EarlyStopping counter: 3 out of 10\nEpoch [29/50], Test AUC: 0.6733\n  -> EarlyStopping counter: 4 out of 10\nEpoch [30/50], Test AUC: 0.6820\n  -> EarlyStopping counter: 5 out of 10\nEpoch [31/50], Test AUC: 0.6841\n  -> EarlyStopping counter: 6 out of 10\nEpoch [32/50], Test AUC: 0.6816\n  -> EarlyStopping counter: 7 out of 10\nEpoch [33/50], Test AUC: 0.6840\n  -> EarlyStopping counter: 8 out of 10\nEpoch [34/50], Test AUC: 0.6847\n  -> EarlyStopping counter: 9 out of 10\nEpoch [35/50], Test AUC: 0.6824\n  -> EarlyStopping counter: 10 out of 10\n  -> Early stopping triggered.\nBest Test AUC for 1%: 0.6881\n\n--- Training from Scratch with 10% Labels ---\nTraining on 1500 samples, testing on 15000 samples.\nEpoch [1/50], Test AUC: 0.6806\n  -> Test AUC improved (0.0000 --> 0.6806).\nEpoch [2/50], Test AUC: 0.6981\n  -> Test AUC improved (0.6806 --> 0.6981).\nEpoch [3/50], Test AUC: 0.7179\n  -> Test AUC improved (0.6981 --> 0.7179).\nEpoch [4/50], Test AUC: 0.7247\n  -> Test AUC improved (0.7179 --> 0.7247).\nEpoch [5/50], Test AUC: 0.7352\n  -> Test AUC improved (0.7247 --> 0.7352).\nEpoch [6/50], Test AUC: 0.7387\n  -> Test AUC improved (0.7352 --> 0.7387).\nEpoch [7/50], Test AUC: 0.7328\n  -> EarlyStopping counter: 1 out of 10\nEpoch [8/50], Test AUC: 0.7358\n  -> EarlyStopping counter: 2 out of 10\nEpoch [9/50], Test AUC: 0.7332\n  -> EarlyStopping counter: 3 out of 10\nEpoch [10/50], Test AUC: 0.7391\n  -> Test AUC improved (0.7387 --> 0.7391).\nEpoch [11/50], Test AUC: 0.7466\n  -> Test AUC improved (0.7391 --> 0.7466).\nEpoch [12/50], Test AUC: 0.7299\n  -> EarlyStopping counter: 1 out of 10\nEpoch [13/50], Test AUC: 0.7424\n  -> EarlyStopping counter: 2 out of 10\nEpoch [14/50], Test AUC: 0.7390\n  -> EarlyStopping counter: 3 out of 10\nEpoch [15/50], Test AUC: 0.7482\n  -> Test AUC improved (0.7466 --> 0.7482).\nEpoch [16/50], Test AUC: 0.7373\n  -> EarlyStopping counter: 1 out of 10\nEpoch [17/50], Test AUC: 0.7489\n  -> Test AUC improved (0.7482 --> 0.7489).\nEpoch [18/50], Test AUC: 0.7424\n  -> EarlyStopping counter: 1 out of 10\nEpoch [19/50], Test AUC: 0.7313\n  -> EarlyStopping counter: 2 out of 10\nEpoch [20/50], Test AUC: 0.7447\n  -> EarlyStopping counter: 3 out of 10\nEpoch [21/50], Test AUC: 0.7507\n  -> Test AUC improved (0.7489 --> 0.7507).\nEpoch [22/50], Test AUC: 0.7113\n  -> EarlyStopping counter: 1 out of 10\nEpoch [23/50], Test AUC: 0.7457\n  -> EarlyStopping counter: 2 out of 10\nEpoch [24/50], Test AUC: 0.7500\n  -> EarlyStopping counter: 3 out of 10\nEpoch [25/50], Test AUC: 0.7522\n  -> Test AUC improved (0.7507 --> 0.7522).\nEpoch [26/50], Test AUC: 0.7434\n  -> EarlyStopping counter: 1 out of 10\nEpoch [27/50], Test AUC: 0.7458\n  -> EarlyStopping counter: 2 out of 10\nEpoch [28/50], Test AUC: 0.7382\n  -> EarlyStopping counter: 3 out of 10\nEpoch [29/50], Test AUC: 0.7267\n  -> EarlyStopping counter: 4 out of 10\nEpoch [30/50], Test AUC: 0.7489\n  -> EarlyStopping counter: 5 out of 10\nEpoch [31/50], Test AUC: 0.7442\n  -> EarlyStopping counter: 6 out of 10\nEpoch [32/50], Test AUC: 0.7488\n  -> EarlyStopping counter: 7 out of 10\nEpoch [33/50], Test AUC: 0.7480\n  -> EarlyStopping counter: 8 out of 10\nEpoch [34/50], Test AUC: 0.7480\n  -> EarlyStopping counter: 9 out of 10\nEpoch [35/50], Test AUC: 0.7481\n  -> EarlyStopping counter: 10 out of 10\n  -> Early stopping triggered.\nBest Test AUC for 10%: 0.7522\n\n--- Training from Scratch with 25% Labels ---\nTraining on 3750 samples, testing on 15000 samples.\nEpoch [1/50], Test AUC: 0.7059\n  -> Test AUC improved (0.0000 --> 0.7059).\nEpoch [2/50], Test AUC: 0.7247\n  -> Test AUC improved (0.7059 --> 0.7247).\nEpoch [3/50], Test AUC: 0.7404\n  -> Test AUC improved (0.7247 --> 0.7404).\nEpoch [4/50], Test AUC: 0.7379\n  -> EarlyStopping counter: 1 out of 10\nEpoch [5/50], Test AUC: 0.7299\n  -> EarlyStopping counter: 2 out of 10\nEpoch [6/50], Test AUC: 0.7506\n  -> Test AUC improved (0.7404 --> 0.7506).\nEpoch [7/50], Test AUC: 0.7457\n  -> EarlyStopping counter: 1 out of 10\nEpoch [8/50], Test AUC: 0.7539\n  -> Test AUC improved (0.7506 --> 0.7539).\nEpoch [9/50], Test AUC: 0.7444\n  -> EarlyStopping counter: 1 out of 10\nEpoch [10/50], Test AUC: 0.7513\n  -> EarlyStopping counter: 2 out of 10\nEpoch [11/50], Test AUC: 0.7505\n  -> EarlyStopping counter: 3 out of 10\nEpoch [12/50], Test AUC: 0.7551\n  -> Test AUC improved (0.7539 --> 0.7551).\nEpoch [13/50], Test AUC: 0.7581\n  -> Test AUC improved (0.7551 --> 0.7581).\nEpoch [14/50], Test AUC: 0.7596\n  -> Test AUC improved (0.7581 --> 0.7596).\nEpoch [15/50], Test AUC: 0.7584\n  -> EarlyStopping counter: 1 out of 10\nEpoch [16/50], Test AUC: 0.7322\n  -> EarlyStopping counter: 2 out of 10\nEpoch [17/50], Test AUC: 0.7574\n  -> EarlyStopping counter: 3 out of 10\nEpoch [18/50], Test AUC: 0.7600\n  -> Test AUC improved (0.7596 --> 0.7600).\nEpoch [19/50], Test AUC: 0.7611\n  -> Test AUC improved (0.7600 --> 0.7611).\nEpoch [20/50], Test AUC: 0.7596\n  -> EarlyStopping counter: 1 out of 10\nEpoch [21/50], Test AUC: 0.7578\n  -> EarlyStopping counter: 2 out of 10\nEpoch [22/50], Test AUC: 0.7493\n  -> EarlyStopping counter: 3 out of 10\nEpoch [23/50], Test AUC: 0.7323\n  -> EarlyStopping counter: 4 out of 10\nEpoch [24/50], Test AUC: 0.7612\n  -> Test AUC improved (0.7611 --> 0.7612).\nEpoch [25/50], Test AUC: 0.7634\n  -> Test AUC improved (0.7612 --> 0.7634).\nEpoch [26/50], Test AUC: 0.7562\n  -> EarlyStopping counter: 1 out of 10\nEpoch [27/50], Test AUC: 0.7602\n  -> EarlyStopping counter: 2 out of 10\nEpoch [28/50], Test AUC: 0.7595\n  -> EarlyStopping counter: 3 out of 10\nEpoch [29/50], Test AUC: 0.7626\n  -> EarlyStopping counter: 4 out of 10\nEpoch [30/50], Test AUC: 0.7662\n  -> Test AUC improved (0.7634 --> 0.7662).\nEpoch [31/50], Test AUC: 0.7627\n  -> EarlyStopping counter: 1 out of 10\nEpoch [32/50], Test AUC: 0.7480\n  -> EarlyStopping counter: 2 out of 10\nEpoch [33/50], Test AUC: 0.7555\n  -> EarlyStopping counter: 3 out of 10\nEpoch [34/50], Test AUC: 0.7655\n  -> EarlyStopping counter: 4 out of 10\nEpoch [35/50], Test AUC: 0.7687\n  -> Test AUC improved (0.7662 --> 0.7687).\nEpoch [36/50], Test AUC: 0.7580\n  -> EarlyStopping counter: 1 out of 10\nEpoch [37/50], Test AUC: 0.7607\n  -> EarlyStopping counter: 2 out of 10\nEpoch [38/50], Test AUC: 0.7654\n  -> EarlyStopping counter: 3 out of 10\nEpoch [39/50], Test AUC: 0.7481\n  -> EarlyStopping counter: 4 out of 10\nEpoch [40/50], Test AUC: 0.7557\n  -> EarlyStopping counter: 5 out of 10\nEpoch [41/50], Test AUC: 0.7630\n  -> EarlyStopping counter: 6 out of 10\nEpoch [42/50], Test AUC: 0.7687\n  -> EarlyStopping counter: 7 out of 10\nEpoch [43/50], Test AUC: 0.7653\n  -> EarlyStopping counter: 8 out of 10\nEpoch [44/50], Test AUC: 0.7667\n  -> EarlyStopping counter: 9 out of 10\nEpoch [45/50], Test AUC: 0.7623\n  -> EarlyStopping counter: 10 out of 10\n  -> Early stopping triggered.\nBest Test AUC for 25%: 0.7687\n\n--- Training from Scratch with 50% Labels ---\nTraining on 7500 samples, testing on 15000 samples.\nEpoch [1/50], Test AUC: 0.7280\n  -> Test AUC improved (0.0000 --> 0.7280).\nEpoch [2/50], Test AUC: 0.7451\n  -> Test AUC improved (0.7280 --> 0.7451).\nEpoch [3/50], Test AUC: 0.7491\n  -> Test AUC improved (0.7451 --> 0.7491).\nEpoch [4/50], Test AUC: 0.7543\n  -> Test AUC improved (0.7491 --> 0.7543).\nEpoch [5/50], Test AUC: 0.7557\n  -> Test AUC improved (0.7543 --> 0.7557).\nEpoch [6/50], Test AUC: 0.7559\n  -> Test AUC improved (0.7557 --> 0.7559).\nEpoch [7/50], Test AUC: 0.7615\n  -> Test AUC improved (0.7559 --> 0.7615).\nEpoch [8/50], Test AUC: 0.7658\n  -> Test AUC improved (0.7615 --> 0.7658).\nEpoch [9/50], Test AUC: 0.7642\n  -> EarlyStopping counter: 1 out of 10\nEpoch [10/50], Test AUC: 0.7630\n  -> EarlyStopping counter: 2 out of 10\nEpoch [11/50], Test AUC: 0.7582\n  -> EarlyStopping counter: 3 out of 10\nEpoch [12/50], Test AUC: 0.7610\n  -> EarlyStopping counter: 4 out of 10\nEpoch [13/50], Test AUC: 0.7652\n  -> EarlyStopping counter: 5 out of 10\nEpoch [14/50], Test AUC: 0.7542\n  -> EarlyStopping counter: 6 out of 10\nEpoch [15/50], Test AUC: 0.7660\n  -> Test AUC improved (0.7658 --> 0.7660).\nEpoch [16/50], Test AUC: 0.7673\n  -> Test AUC improved (0.7660 --> 0.7673).\nEpoch [17/50], Test AUC: 0.7684\n  -> Test AUC improved (0.7673 --> 0.7684).\nEpoch [18/50], Test AUC: 0.7670\n  -> EarlyStopping counter: 1 out of 10\nEpoch [19/50], Test AUC: 0.7707\n  -> Test AUC improved (0.7684 --> 0.7707).\nEpoch [20/50], Test AUC: 0.7684\n  -> EarlyStopping counter: 1 out of 10\nEpoch [21/50], Test AUC: 0.7662\n  -> EarlyStopping counter: 2 out of 10\nEpoch [22/50], Test AUC: 0.7663\n  -> EarlyStopping counter: 3 out of 10\nEpoch [23/50], Test AUC: 0.7683\n  -> EarlyStopping counter: 4 out of 10\nEpoch [24/50], Test AUC: 0.7706\n  -> EarlyStopping counter: 5 out of 10\nEpoch [25/50], Test AUC: 0.7682\n  -> EarlyStopping counter: 6 out of 10\nEpoch [26/50], Test AUC: 0.7717\n  -> Test AUC improved (0.7707 --> 0.7717).\nEpoch [27/50], Test AUC: 0.7708\n  -> EarlyStopping counter: 1 out of 10\nEpoch [28/50], Test AUC: 0.7722\n  -> Test AUC improved (0.7717 --> 0.7722).\nEpoch [29/50], Test AUC: 0.7707\n  -> EarlyStopping counter: 1 out of 10\nEpoch [30/50], Test AUC: 0.7734\n  -> Test AUC improved (0.7722 --> 0.7734).\nEpoch [31/50], Test AUC: 0.7685\n  -> EarlyStopping counter: 1 out of 10\nEpoch [32/50], Test AUC: 0.7644\n  -> EarlyStopping counter: 2 out of 10\nEpoch [33/50], Test AUC: 0.7697\n  -> EarlyStopping counter: 3 out of 10\nEpoch [34/50], Test AUC: 0.7667\n  -> EarlyStopping counter: 4 out of 10\nEpoch [35/50], Test AUC: 0.7725\n  -> EarlyStopping counter: 5 out of 10\nEpoch [36/50], Test AUC: 0.7728\n  -> EarlyStopping counter: 6 out of 10\nEpoch [37/50], Test AUC: 0.7669\n  -> EarlyStopping counter: 7 out of 10\nEpoch [38/50], Test AUC: 0.7726\n  -> EarlyStopping counter: 8 out of 10\nEpoch [39/50], Test AUC: 0.7693\n  -> EarlyStopping counter: 9 out of 10\nEpoch [40/50], Test AUC: 0.7706\n  -> EarlyStopping counter: 10 out of 10\n  -> Early stopping triggered.\nBest Test AUC for 50%: 0.7734\n\n--- Training from Scratch with 100% Labels ---\nTraining on 15000 samples, testing on 15000 samples.\nEpoch [1/50], Test AUC: 0.7441\n  -> Test AUC improved (0.0000 --> 0.7441).\nEpoch [2/50], Test AUC: 0.7559\n  -> Test AUC improved (0.7441 --> 0.7559).\nEpoch [3/50], Test AUC: 0.7640\n  -> Test AUC improved (0.7559 --> 0.7640).\nEpoch [4/50], Test AUC: 0.7532\n  -> EarlyStopping counter: 1 out of 10\nEpoch [5/50], Test AUC: 0.7652\n  -> Test AUC improved (0.7640 --> 0.7652).\nEpoch [6/50], Test AUC: 0.7561\n  -> EarlyStopping counter: 1 out of 10\nEpoch [7/50], Test AUC: 0.7604\n  -> EarlyStopping counter: 2 out of 10\nEpoch [8/50], Test AUC: 0.7700\n  -> Test AUC improved (0.7652 --> 0.7700).\nEpoch [9/50], Test AUC: 0.7701\n  -> EarlyStopping counter: 1 out of 10\nEpoch [10/50], Test AUC: 0.7659\n  -> EarlyStopping counter: 2 out of 10\nEpoch [11/50], Test AUC: 0.7683\n  -> EarlyStopping counter: 3 out of 10\nEpoch [12/50], Test AUC: 0.7695\n  -> EarlyStopping counter: 4 out of 10\nEpoch [13/50], Test AUC: 0.7703\n  -> Test AUC improved (0.7700 --> 0.7703).\nEpoch [14/50], Test AUC: 0.7714\n  -> Test AUC improved (0.7703 --> 0.7714).\nEpoch [15/50], Test AUC: 0.7753\n  -> Test AUC improved (0.7714 --> 0.7753).\nEpoch [16/50], Test AUC: 0.7739\n  -> EarlyStopping counter: 1 out of 10\nEpoch [17/50], Test AUC: 0.7753\n  -> EarlyStopping counter: 2 out of 10\nEpoch [18/50], Test AUC: 0.7703\n  -> EarlyStopping counter: 3 out of 10\nEpoch [19/50], Test AUC: 0.7746\n  -> EarlyStopping counter: 4 out of 10\nEpoch [20/50], Test AUC: 0.7737\n  -> EarlyStopping counter: 5 out of 10\nEpoch [21/50], Test AUC: 0.7569\n  -> EarlyStopping counter: 6 out of 10\nEpoch [22/50], Test AUC: 0.7717\n  -> EarlyStopping counter: 7 out of 10\nEpoch [23/50], Test AUC: 0.7706\n  -> EarlyStopping counter: 8 out of 10\nEpoch [24/50], Test AUC: 0.7688\n  -> EarlyStopping counter: 9 out of 10\nEpoch [25/50], Test AUC: 0.7731\n  -> EarlyStopping counter: 10 out of 10\n  -> Early stopping triggered.\nBest Test AUC for 100%: 0.7753\n\n--- Final Results (Supervised Training from Scratch) ---\nBest AUC with 1% labels: 0.6881\nBest AUC with 10% labels: 0.7522\nBest AUC with 25% labels: 0.7687\nBest AUC with 50% labels: 0.7734\nBest AUC with 100% labels: 0.7753\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x600 with 1 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{}}],"execution_count":1},{"id":"2faaad28-b115-4e95-9994-83a0e19dcf36","cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}
\ No newline at end of file
diff --git a/E2E_Foundation_Model_Aaditya_Porwal/NoteBooks/Pretraining/Pretraining_Using_NTXent_Loss.ipynb b/E2E_Foundation_Model_Aaditya_Porwal/NoteBooks/Pretraining/Pretraining_Using_NTXent_Loss.ipynb
new file mode 100644
index 0000000..a2996c1
--- /dev/null
+++ b/E2E_Foundation_Model_Aaditya_Porwal/NoteBooks/Pretraining/Pretraining_Using_NTXent_Loss.ipynb
@@ -0,0 +1,1152 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "45673959",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-29T12:12:38.675779Z",
+     "iopub.status.busy": "2025-07-29T12:12:38.675461Z",
+     "iopub.status.idle": "2025-07-29T12:12:48.516429Z",
+     "shell.execute_reply": "2025-07-29T12:12:48.515636Z"
+    },
+    "papermill": {
+     "duration": 9.846567,
+     "end_time": "2025-07-29T12:12:48.517921",
+     "exception": false,
+     "start_time": "2025-07-29T12:12:38.671354",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "import torchvision.transforms as T\n",
+    "\n",
+    "import h5py\n",
+    "import numpy as np\n",
+    "from scipy.ndimage import gaussian_filter\n",
+    "from tqdm.auto import tqdm\n",
+    "import os\n",
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "82abf231",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-29T12:12:48.524359Z",
+     "iopub.status.busy": "2025-07-29T12:12:48.524075Z",
+     "iopub.status.idle": "2025-07-29T12:12:48.574718Z",
+     "shell.execute_reply": "2025-07-29T12:12:48.574134Z"
+    },
+    "papermill": {
+     "duration": 0.054741,
+     "end_time": "2025-07-29T12:12:48.575743",
+     "exception": false,
+     "start_time": "2025-07-29T12:12:48.521002",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# In your resnet15.ipynb (pre-training) notebook\n",
+    "class Config:\n",
+    "    FILE_PATH = \"/kaggle/input/qc0-1million/train_jet_train_meta_100k.h5\"\n",
+    "    IMAGE_SIZE = 125\n",
+    "    BATCH_SIZE = 256\n",
+    "    EPOCHS = 100\n",
+    "    # CRITICAL: Adjusted learning rate for LARS optimizer\n",
+    "    LEARNING_RATE = 0.3 \n",
+    "    TEMPERATURE = 0.1\n",
+    "    ROTATION_DEGREES = 60.0\n",
+    "    GAUSSIAN_BLUR_SIGMA = (0.1, 2.0) # Range for sigma of Gaussian blur\n",
+    "    MODEL_SAVE_DIR = \"./simclr_pytorch_model\"\n",
+    "    DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f1d4c6a4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-29T12:12:48.581713Z",
+     "iopub.status.busy": "2025-07-29T12:12:48.581203Z",
+     "iopub.status.idle": "2025-07-29T12:12:48.590946Z",
+     "shell.execute_reply": "2025-07-29T12:12:48.590325Z"
+    },
+    "papermill": {
+     "duration": 0.013703,
+     "end_time": "2025-07-29T12:12:48.592032",
+     "exception": false,
+     "start_time": "2025-07-29T12:12:48.578329",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using device: cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "if not os.path.exists(Config.MODEL_SAVE_DIR):\n",
+    "    os.makedirs(Config.MODEL_SAVE_DIR)\n",
+    "    \n",
+    "print(f\"Using device: {Config.DEVICE}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b3256a11",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-29T12:12:48.597776Z",
+     "iopub.status.busy": "2025-07-29T12:12:48.597585Z",
+     "iopub.status.idle": "2025-07-29T12:12:48.602665Z",
+     "shell.execute_reply": "2025-07-29T12:12:48.602159Z"
+    },
+    "papermill": {
+     "duration": 0.009078,
+     "end_time": "2025-07-29T12:12:48.603687",
+     "exception": false,
+     "start_time": "2025-07-29T12:12:48.594609",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class InMemoryPretrainDataset(Dataset):\n",
+    "    \"\"\"A dataset that loads pre-training data (images + labels) into RAM for speed.\"\"\"\n",
+    "    def __init__(self, h5_path):\n",
+    "        print(\"Loading pre-training data into memory... (This may take a few minutes)\")\n",
+    "        with h5py.File(h5_path, 'r') as f:\n",
+    "            self.images = f['train_jet'][:70000]             # Shape: (70000, 125, 125, 8)\n",
+    "            self.labels = f['train_meta'][:70000, 2]         # Shape: (70000,)\n",
+    "        print(\"Data loaded into RAM.\")\n",
+    "        \n",
+    "        self.length = len(self.images)\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return self.length\n",
+    "\n",
+    "    def __getitem__(self, idx):\n",
+    "        image = self.images[idx]                             # (125, 125, 8)\n",
+    "        label = self.labels[idx]                             # Scalar\n",
+    "\n",
+    "        image_tensor = torch.from_numpy(image).permute(2, 0, 1).float()  # (8, 125, 125)\n",
+    "        label_tensor = torch.tensor(label, dtype=torch.float32)\n",
+    "\n",
+    "        return image_tensor, label_tensor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df93355e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-29T12:12:48.609263Z",
+     "iopub.status.busy": "2025-07-29T12:12:48.609080Z",
+     "iopub.status.idle": "2025-07-29T12:12:48.614423Z",
+     "shell.execute_reply": "2025-07-29T12:12:48.613910Z"
+    },
+    "papermill": {
+     "duration": 0.009276,
+     "end_time": "2025-07-29T12:12:48.615457",
+     "exception": false,
+     "start_time": "2025-07-29T12:12:48.606181",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class ResidualBlock(nn.Module):\n",
+    "    def __init__(self, in_channels, out_channels, stride=1):\n",
+    "        super().__init__()\n",
+    "        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)\n",
+    "        self.bn1 = nn.BatchNorm2d(out_channels)\n",
+    "        self.relu = nn.ReLU(inplace=True)\n",
+    "        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)\n",
+    "        self.bn2 = nn.BatchNorm2d(out_channels)\n",
+    "        \n",
+    "        self.shortcut = nn.Sequential()\n",
+    "        if stride != 1 or in_channels != out_channels:\n",
+    "            self.shortcut = nn.Sequential(\n",
+    "                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),\n",
+    "                nn.BatchNorm2d(out_channels)\n",
+    "            )\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        out = self.relu(self.bn1(self.conv1(x)))\n",
+    "        out = self.bn2(self.conv2(out))\n",
+    "        out += self.shortcut(x)\n",
+    "        out = self.relu(out)\n",
+    "        return out"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9266d08e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-29T12:12:48.621019Z",
+     "iopub.status.busy": "2025-07-29T12:12:48.620514Z",
+     "iopub.status.idle": "2025-07-29T12:12:48.627798Z",
+     "shell.execute_reply": "2025-07-29T12:12:48.627135Z"
+    },
+    "papermill": {
+     "duration": 0.011148,
+     "end_time": "2025-07-29T12:12:48.628920",
+     "exception": false,
+     "start_time": "2025-07-29T12:12:48.617772",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class Encoder(nn.Module):\n",
+    "    \"\"\"ResNet-15 Encoder\"\"\"\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "        self.in_channels = 64\n",
+    "        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)\n",
+    "        self.bn1 = nn.BatchNorm2d(64)\n",
+    "        self.relu = nn.ReLU(inplace=True)\n",
+    "        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
+    "        \n",
+    "        self.layer1 = self._make_layer(64, 2, stride=1)\n",
+    "        self.layer2 = self._make_layer(128, 2, stride=2)\n",
+    "        self.layer3 = self._make_layer(256, 2, stride=2)\n",
+    "        # Total layers: 1 (conv1) + 2*2 + 2*2 + 2*2 = 13, a good ResNet-15 approx.\n",
+    "        \n",
+    "        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n",
+    "\n",
+    "    def _make_layer(self, out_channels, num_blocks, stride):\n",
+    "        strides = [stride] + [1]*(num_blocks-1)\n",
+    "        layers = []\n",
+    "        for s in strides:\n",
+    "            layers.append(ResidualBlock(self.in_channels, out_channels, s))\n",
+    "            self.in_channels = out_channels\n",
+    "        return nn.Sequential(*layers)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = self.relu(self.bn1(self.conv1(x)))\n",
+    "        x = self.maxpool(x)\n",
+    "        x = self.layer1(x)\n",
+    "        x = self.layer2(x)\n",
+    "        x = self.layer3(x)\n",
+    "        x = self.avgpool(x)\n",
+    "        x = torch.flatten(x, 1) # Flatten\n",
+    "        return x\n",
+    "\n",
+    "class ProjectionHead(nn.Module):\n",
+    "    def __init__(self, in_features, hidden_features=256, out_features=128):\n",
+    "        super().__init__()\n",
+    "        self.net = nn.Sequential(\n",
+    "            nn.Linear(in_features, hidden_features),\n",
+    "            nn.ReLU(),\n",
+    "            nn.Linear(hidden_features, out_features)\n",
+    "        )\n",
+    "    def forward(self, x):\n",
+    "        return self.net(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e8611040",
+   "metadata": {
+    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+    "execution": {
+     "iopub.execute_input": "2025-07-29T12:12:48.634350Z",
+     "iopub.status.busy": "2025-07-29T12:12:48.633937Z",
+     "iopub.status.idle": "2025-07-29T12:12:48.639951Z",
+     "shell.execute_reply": "2025-07-29T12:12:48.639286Z"
+    },
+    "papermill": {
+     "duration": 0.009734,
+     "end_time": "2025-07-29T12:12:48.640953",
+     "exception": false,
+     "start_time": "2025-07-29T12:12:48.631219",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class NTXentLoss(nn.Module):\n",
+    "    def __init__(self, temperature=0.1):\n",
+    "        super().__init__()\n",
+    "        self.temperature = temperature\n",
+    "        self.criterion = nn.CrossEntropyLoss()\n",
+    "\n",
+    "    def forward(self, z1, z2):\n",
+    "        z1 = nn.functional.normalize(z1, dim=1)\n",
+    "        z2 = nn.functional.normalize(z2, dim=1)\n",
+    "        \n",
+    "        features = torch.cat([z1, z2], dim=0)\n",
+    "        \n",
+    "        similarity_matrix = torch.matmul(features, features.T)\n",
+    "        \n",
+    "        # Create labels and masks\n",
+    "        batch_size = z1.shape[0]\n",
+    "        labels = torch.arange(2 * batch_size).to(Config.DEVICE)\n",
+    "        \n",
+    "        # Mask to remove diagonal elements (self-similarity)\n",
+    "        mask = ~torch.eye(2 * batch_size, dtype=torch.bool).to(Config.DEVICE)\n",
+    "        \n",
+    "        # Select positive pairs (i-th image with its other view)\n",
+    "        pos_mask = torch.zeros_like(similarity_matrix, dtype=torch.bool)\n",
+    "        pos_mask[torch.arange(batch_size), torch.arange(batch_size) + batch_size] = True\n",
+    "        pos_mask[torch.arange(batch_size) + batch_size, torch.arange(batch_size)] = True\n",
+    "        \n",
+    "        positives = similarity_matrix[pos_mask].view(2 * batch_size, 1)\n",
+    "        negatives = similarity_matrix[mask].view(2 * batch_size, -1)\n",
+    "        \n",
+    "        logits = torch.cat([positives, negatives], dim=1)\n",
+    "        logits /= self.temperature\n",
+    "        \n",
+    "        # The labels are all zeros since the positive pair is always in the first column\n",
+    "        return self.criterion(logits, torch.zeros(2 * batch_size, dtype=torch.long).to(Config.DEVICE))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "362b4d45",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-29T12:12:48.646423Z",
+     "iopub.status.busy": "2025-07-29T12:12:48.646061Z",
+     "iopub.status.idle": "2025-07-29T12:12:48.651006Z",
+     "shell.execute_reply": "2025-07-29T12:12:48.650342Z"
+    },
+    "papermill": {
+     "duration": 0.008806,
+     "end_time": "2025-07-29T12:12:48.652103",
+     "exception": false,
+     "start_time": "2025-07-29T12:12:48.643297",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# --- NEW: Early Stopping Class ---\n",
+    "class EarlyStopping:\n",
+    "    \"\"\"Stops training when the loss does not improve after a given patience.\"\"\"\n",
+    "    def __init__(self, patience=5, min_delta=0.001, verbose=True):\n",
+    "        \"\"\"\n",
+    "        Args:\n",
+    "            patience (int): How long to wait after last time validation loss improved.\n",
+    "            min_delta (float): Minimum change in the monitored quantity to qualify as an improvement.\n",
+    "            verbose (bool): If True, prints a message for each validation loss improvement.\n",
+    "        \"\"\"\n",
+    "        self.patience = patience\n",
+    "        self.min_delta = min_delta\n",
+    "        self.verbose = verbose\n",
+    "        self.counter = 0\n",
+    "        self.best_loss = float('inf')\n",
+    "        self.early_stop = False\n",
+    "\n",
+    "    def __call__(self, val_loss):\n",
+    "        if self.best_loss - val_loss > self.min_delta:\n",
+    "            if self.verbose:\n",
+    "                print(f\"Loss decreased ({self.best_loss:.6f} --> {val_loss:.6f}).\")\n",
+    "            self.best_loss = val_loss\n",
+    "            self.counter = 0\n",
+    "        else:\n",
+    "            self.counter += 1\n",
+    "            print(f\"EarlyStopping counter: {self.counter} out of {self.patience}\")\n",
+    "            if self.counter >= self.patience:\n",
+    "                print(\"Early stopping\")\n",
+    "                self.early_stop = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9c5b77b0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-29T12:12:48.657509Z",
+     "iopub.status.busy": "2025-07-29T12:12:48.657319Z",
+     "iopub.status.idle": "2025-07-29T12:12:48.663803Z",
+     "shell.execute_reply": "2025-07-29T12:12:48.663176Z"
+    },
+    "papermill": {
+     "duration": 0.010391,
+     "end_time": "2025-07-29T12:12:48.664846",
+     "exception": false,
+     "start_time": "2025-07-29T12:12:48.654455",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# In your resnet15.ipynb (pre-training) notebook\n",
+    "\n",
+    "class LARS(optim.Optimizer):\n",
+    "    \"\"\"\n",
+    "    LARS optimizer, implementation from PyTorch Lightning Bolts.\n",
+    "    https://github.com/Lightning-AI/lightning-bolts/blob/master/pl_bolts/optimizers/lars.py\n",
+    "    \"\"\"\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        params,\n",
+    "        lr,\n",
+    "        momentum=0.9,\n",
+    "        weight_decay=1e-6,\n",
+    "        trust_coefficient=0.001,\n",
+    "        eps=1e-8,\n",
+    "    ):\n",
+    "        defaults = dict(\n",
+    "            lr=lr,\n",
+    "            momentum=momentum,\n",
+    "            weight_decay=weight_decay,\n",
+    "            trust_coefficient=trust_coefficient,\n",
+    "            eps=eps,\n",
+    "        )\n",
+    "        super().__init__(params, defaults)\n",
+    "\n",
+    "    @torch.no_grad()\n",
+    "    def step(self, closure=None):\n",
+    "        loss = None\n",
+    "        if closure is not None:\n",
+    "            with torch.enable_grad():\n",
+    "                loss = closure()\n",
+    "\n",
+    "        for group in self.param_groups:\n",
+    "            weight_decay = group[\"weight_decay\"]\n",
+    "            momentum = group[\"momentum\"]\n",
+    "            lr = group[\"lr\"]\n",
+    "            trust_coefficient = group[\"trust_coefficient\"]\n",
+    "            eps = group[\"eps\"]\n",
+    "\n",
+    "            for p in group[\"params\"]:\n",
+    "                if p.grad is None:\n",
+    "                    continue\n",
+    "\n",
+    "                grad = p.grad\n",
+    "                \n",
+    "                if weight_decay != 0:\n",
+    "                    grad = grad.add(p, alpha=weight_decay)\n",
+    "\n",
+    "                param_norm = torch.norm(p)\n",
+    "                grad_norm = torch.norm(grad)\n",
+    "                \n",
+    "                # Compute the local learning rate for this layer\n",
+    "                local_lr = lr * trust_coefficient * param_norm / (grad_norm + eps)\n",
+    "\n",
+    "                # Update the momentum term\n",
+    "                if momentum != 0:\n",
+    "                    param_state = self.state[p]\n",
+    "                    if \"momentum_buffer\" not in param_state:\n",
+    "                        buf = param_state[\"momentum_buffer\"] = torch.clone(grad).detach()\n",
+    "                    else:\n",
+    "                        buf = param_state[\"momentum_buffer\"]\n",
+    "                        buf.mul_(momentum).add_(grad)\n",
+    "                    grad = buf\n",
+    "\n",
+    "                p.add_(grad, alpha=-local_lr)\n",
+    "        return loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3db70c2e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-29T12:12:48.670446Z",
+     "iopub.status.busy": "2025-07-29T12:12:48.670275Z",
+     "iopub.status.idle": "2025-07-29T12:12:48.682906Z",
+     "shell.execute_reply": "2025-07-29T12:12:48.682250Z"
+    },
+    "papermill": {
+     "duration": 0.016751,
+     "end_time": "2025-07-29T12:12:48.684004",
+     "exception": false,
+     "start_time": "2025-07-29T12:12:48.667253",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import random\n",
+    "import os\n",
+    "from torch import nn\n",
+    "from torch.utils.data import Subset, DataLoader\n",
+    "from torchvision import transforms as T\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "# Utility function to select balanced validation indices\n",
+    "def get_balanced_validation_indices(dataset, num_samples_per_class=500):\n",
+    "    class0_indices, class1_indices = [], []\n",
+    "\n",
+    "    for idx in range(len(dataset)):\n",
+    "        _, y = dataset[idx]\n",
+    "        label = int(y.item())\n",
+    "        if label == 0 and len(class0_indices) < num_samples_per_class:\n",
+    "            class0_indices.append(idx)\n",
+    "        elif label == 1 and len(class1_indices) < num_samples_per_class:\n",
+    "            class1_indices.append(idx)\n",
+    "\n",
+    "        if len(class0_indices) == num_samples_per_class and len(class1_indices) == num_samples_per_class:\n",
+    "            break\n",
+    "\n",
+    "    return class0_indices + class1_indices\n",
+    "\n",
+    "\n",
+    "def main():\n",
+    "    # GPU-based augmentation\n",
+    "    train_transform = nn.Sequential(\n",
+    "        T.RandomHorizontalFlip(),\n",
+    "        T.RandomVerticalFlip(),\n",
+    "        T.RandomRotation(degrees=Config.ROTATION_DEGREES),\n",
+    "        T.GaussianBlur(kernel_size=5, sigma=Config.GAUSSIAN_BLUR_SIGMA)\n",
+    "    ).to(Config.DEVICE)\n",
+    "\n",
+    "    # Load dataset from latent feature file\n",
+    "    dataset = InMemoryPretrainDataset(Config.FILE_PATH)  # This loads features+labels from latent_vectors.pt\n",
+    "\n",
+    "    # Balanced validation split\n",
+    "    val_indices = get_balanced_validation_indices(dataset)\n",
+    "    train_indices = list(set(range(len(dataset))) - set(val_indices))\n",
+    "\n",
+    "    train_dataset = Subset(dataset, train_indices)\n",
+    "    val_dataset = Subset(dataset, val_indices)\n",
+    "    print(f\"Total samples in dataset: {len(dataset)}\")\n",
+    "    print(f\"Samples used for pretraining: {len(train_dataset)}\")\n",
+    "    print(f\"Samples used for validation: {len(val_dataset)}\")\n",
+    "\n",
+    "\n",
+    "    train_loader = DataLoader(train_dataset, batch_size=Config.BATCH_SIZE, shuffle=True, num_workers=0, drop_last=True)\n",
+    "    val_loader = DataLoader(val_dataset, batch_size=Config.BATCH_SIZE, shuffle=False, num_workers=0)\n",
+    "    \n",
+    "\n",
+    "    # Model setup\n",
+    "    encoder = Encoder().to(Config.DEVICE)\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        dummy_input = torch.randn(1, 3, Config.IMAGE_SIZE, Config.IMAGE_SIZE).to(Config.DEVICE)\n",
+    "        feature_size = encoder(dummy_input).shape[1]\n",
+    "\n",
+    "    projection_head = ProjectionHead(in_features=feature_size).to(Config.DEVICE)\n",
+    "\n",
+    "    optimizer = LARS(\n",
+    "        params=list(encoder.parameters()) + list(projection_head.parameters()),\n",
+    "        lr=Config.LEARNING_RATE,\n",
+    "        weight_decay=1e-4\n",
+    "    )\n",
+    "\n",
+    "    loss_fn = NTXentLoss(temperature=Config.TEMPERATURE)\n",
+    "\n",
+    "    best_val_loss = float(\"inf\")\n",
+    "    early_stopper = EarlyStopping(patience=10, min_delta=0.001)\n",
+    "\n",
+    "    print(\"🚀 Starting SimCLR-style pretraining...\")\n",
+    "\n",
+    "    for epoch in range(Config.EPOCHS):\n",
+    "        encoder.train()\n",
+    "        projection_head.train()\n",
+    "        total_loss = 0.0\n",
+    "        pbar = tqdm(train_loader, desc=f\"Epoch {epoch+1}/{Config.EPOCHS}\")\n",
+    "\n",
+    "        for images, _ in pbar:\n",
+    "            images = images.to(Config.DEVICE).float() / 255.0\n",
+    "            view1 = train_transform(images)\n",
+    "            view2 = train_transform(images)\n",
+    "\n",
+    "            z1 = projection_head(encoder(view1))\n",
+    "            z2 = projection_head(encoder(view2))\n",
+    "            loss = loss_fn(z1, z2)\n",
+    "\n",
+    "            optimizer.zero_grad()\n",
+    "            loss.backward()\n",
+    "            optimizer.step()\n",
+    "\n",
+    "            total_loss += loss.item()\n",
+    "            pbar.set_postfix({\"Loss\": loss.item()})\n",
+    "\n",
+    "        avg_train_loss = total_loss / len(train_loader)\n",
+    "\n",
+    "        # Validation loop\n",
+    "        encoder.eval()\n",
+    "        projection_head.eval()\n",
+    "        val_loss = 0.0\n",
+    "        with torch.no_grad():\n",
+    "            for images, _ in val_loader:\n",
+    "                images = images.to(Config.DEVICE).float() / 255.0\n",
+    "                view1 = train_transform(images)\n",
+    "                view2 = train_transform(images)\n",
+    "\n",
+    "                z1 = projection_head(encoder(view1))\n",
+    "                z2 = projection_head(encoder(view2))\n",
+    "                loss = loss_fn(z1, z2)\n",
+    "                val_loss += loss.item()\n",
+    "\n",
+    "        avg_val_loss = val_loss / len(val_loader)\n",
+    "        print(f\"Epoch {epoch+1} - Train Loss: {avg_train_loss:.4f}, Val Loss: {avg_val_loss:.4f}\")\n",
+    "\n",
+    "        # Save best model\n",
+    "        if avg_val_loss < best_val_loss:\n",
+    "            best_val_loss = avg_val_loss\n",
+    "            best_model_path = os.path.join(Config.MODEL_SAVE_DIR, \"best_encoder.pth\")\n",
+    "            torch.save(encoder.state_dict(), best_model_path)\n",
+    "            print(f\"✅ Saved best encoder to {best_model_path}\")\n",
+    "\n",
+    "        early_stopper(avg_val_loss)\n",
+    "        if early_stopper.early_stop:\n",
+    "            print(\"⏹️ Early stopping triggered.\")\n",
+    "            break\n",
+    "\n",
+    "    final_path = os.path.join(Config.MODEL_SAVE_DIR, \"final_encoder.pth\")\n",
+    "    torch.save(encoder.state_dict(), final_path)\n",
+    "    print(f\"🏁 Final encoder weights saved to {final_path}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3c5ba97e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-29T12:12:48.689289Z",
+     "iopub.status.busy": "2025-07-29T12:12:48.689100Z",
+     "iopub.status.idle": "2025-07-29T12:53:01.959968Z",
+     "shell.execute_reply": "2025-07-29T12:53:01.959187Z"
+    },
+    "papermill": {
+     "duration": 2413.872403,
+     "end_time": "2025-07-29T12:53:02.558691",
+     "exception": false,
+     "start_time": "2025-07-29T12:12:48.686288",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading pre-training data into memory... (This may take a few minutes)\n",
+      "Data loaded into RAM.\n",
+      "Total samples in dataset: 70000\n",
+      "Samples used for pretraining: 69000\n",
+      "Samples used for validation: 1000\n",
+      "🚀 Starting SimCLR-style pretraining...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/100: 100%|██████████| 269/269 [01:27<00:00,  3.08it/s, Loss=0.893]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1 - Train Loss: 1.9092, Val Loss: 2.7946\n",
+      "✅ Saved best encoder to ./simclr_pytorch_model/best_encoder.pth\n",
+      "Loss decreased (inf --> 2.794630).\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2/100: 100%|██████████| 269/269 [01:26<00:00,  3.12it/s, Loss=0.777]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2 - Train Loss: 0.8416, Val Loss: 3.8602\n",
+      "EarlyStopping counter: 1 out of 10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 3/100: 100%|██████████| 269/269 [01:26<00:00,  3.12it/s, Loss=0.755]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 3 - Train Loss: 0.7754, Val Loss: 2.6614\n",
+      "✅ Saved best encoder to ./simclr_pytorch_model/best_encoder.pth\n",
+      "Loss decreased (2.794630 --> 2.661447).\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 4/100: 100%|██████████| 269/269 [01:26<00:00,  3.10it/s, Loss=0.749]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 4 - Train Loss: 0.7539, Val Loss: 2.2269\n",
+      "✅ Saved best encoder to ./simclr_pytorch_model/best_encoder.pth\n",
+      "Loss decreased (2.661447 --> 2.226898).\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 5/100: 100%|██████████| 269/269 [01:26<00:00,  3.11it/s, Loss=0.743]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 5 - Train Loss: 0.7457, Val Loss: 2.4167\n",
+      "EarlyStopping counter: 1 out of 10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 6/100: 100%|██████████| 269/269 [01:26<00:00,  3.12it/s, Loss=0.739]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 6 - Train Loss: 0.7407, Val Loss: 3.3769\n",
+      "EarlyStopping counter: 2 out of 10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 7/100: 100%|██████████| 269/269 [01:26<00:00,  3.12it/s, Loss=0.733]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 7 - Train Loss: 0.7348, Val Loss: 2.2349\n",
+      "EarlyStopping counter: 3 out of 10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 8/100: 100%|██████████| 269/269 [01:26<00:00,  3.12it/s, Loss=0.731]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 8 - Train Loss: 0.7327, Val Loss: 1.6651\n",
+      "✅ Saved best encoder to ./simclr_pytorch_model/best_encoder.pth\n",
+      "Loss decreased (2.226898 --> 1.665068).\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 9/100: 100%|██████████| 269/269 [01:26<00:00,  3.12it/s, Loss=0.734]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 9 - Train Loss: 0.7347, Val Loss: 1.3454\n",
+      "✅ Saved best encoder to ./simclr_pytorch_model/best_encoder.pth\n",
+      "Loss decreased (1.665068 --> 1.345362).\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 10/100: 100%|██████████| 269/269 [01:26<00:00,  3.12it/s, Loss=0.73]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 10 - Train Loss: 0.7296, Val Loss: 0.8662\n",
+      "✅ Saved best encoder to ./simclr_pytorch_model/best_encoder.pth\n",
+      "Loss decreased (1.345362 --> 0.866218).\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 11/100: 100%|██████████| 269/269 [01:26<00:00,  3.12it/s, Loss=0.725]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 11 - Train Loss: 0.7276, Val Loss: 1.5734\n",
+      "EarlyStopping counter: 1 out of 10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 12/100: 100%|██████████| 269/269 [01:26<00:00,  3.12it/s, Loss=0.736]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 12 - Train Loss: 0.7270, Val Loss: 1.3436\n",
+      "EarlyStopping counter: 2 out of 10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 13/100: 100%|██████████| 269/269 [01:26<00:00,  3.11it/s, Loss=0.724]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 13 - Train Loss: 0.7248, Val Loss: 1.4811\n",
+      "EarlyStopping counter: 3 out of 10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 14/100: 100%|██████████| 269/269 [01:26<00:00,  3.12it/s, Loss=0.722]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 14 - Train Loss: 0.7245, Val Loss: 1.5902\n",
+      "EarlyStopping counter: 4 out of 10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 15/100: 100%|██████████| 269/269 [01:26<00:00,  3.11it/s, Loss=0.724]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 15 - Train Loss: 0.7229, Val Loss: 1.9946\n",
+      "EarlyStopping counter: 5 out of 10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 16/100: 100%|██████████| 269/269 [01:26<00:00,  3.11it/s, Loss=0.883]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 16 - Train Loss: 0.7304, Val Loss: 3.4238\n",
+      "EarlyStopping counter: 6 out of 10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 17/100: 100%|██████████| 269/269 [01:26<00:00,  3.11it/s, Loss=0.723]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 17 - Train Loss: 0.7290, Val Loss: 0.8436\n",
+      "✅ Saved best encoder to ./simclr_pytorch_model/best_encoder.pth\n",
+      "Loss decreased (0.866218 --> 0.843631).\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 18/100: 100%|██████████| 269/269 [01:26<00:00,  3.11it/s, Loss=0.721]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 18 - Train Loss: 0.7211, Val Loss: 1.6782\n",
+      "EarlyStopping counter: 1 out of 10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 19/100: 100%|██████████| 269/269 [01:26<00:00,  3.12it/s, Loss=0.719]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 19 - Train Loss: 0.7204, Val Loss: 3.4094\n",
+      "EarlyStopping counter: 2 out of 10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 20/100: 100%|██████████| 269/269 [01:26<00:00,  3.12it/s, Loss=0.724]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 20 - Train Loss: 0.7207, Val Loss: 1.4447\n",
+      "EarlyStopping counter: 3 out of 10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 21/100: 100%|██████████| 269/269 [01:26<00:00,  3.11it/s, Loss=0.76]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 21 - Train Loss: 0.7393, Val Loss: 2.1986\n",
+      "EarlyStopping counter: 4 out of 10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 22/100: 100%|██████████| 269/269 [01:26<00:00,  3.12it/s, Loss=0.72]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 22 - Train Loss: 0.7310, Val Loss: 1.4102\n",
+      "EarlyStopping counter: 5 out of 10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 23/100: 100%|██████████| 269/269 [01:26<00:00,  3.12it/s, Loss=0.721]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 23 - Train Loss: 0.7198, Val Loss: 2.6091\n",
+      "EarlyStopping counter: 6 out of 10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 24/100: 100%|██████████| 269/269 [01:26<00:00,  3.11it/s, Loss=0.718]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 24 - Train Loss: 0.7198, Val Loss: 1.6208\n",
+      "EarlyStopping counter: 7 out of 10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 25/100: 100%|██████████| 269/269 [01:26<00:00,  3.10it/s, Loss=0.719]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 25 - Train Loss: 0.7184, Val Loss: 3.7514\n",
+      "EarlyStopping counter: 8 out of 10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 26/100: 100%|██████████| 269/269 [01:26<00:00,  3.11it/s, Loss=0.718]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 26 - Train Loss: 0.7178, Val Loss: 0.8441\n",
+      "EarlyStopping counter: 9 out of 10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 27/100: 100%|██████████| 269/269 [01:26<00:00,  3.12it/s, Loss=0.719]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 27 - Train Loss: 0.7207, Val Loss: 1.5396\n",
+      "EarlyStopping counter: 10 out of 10\n",
+      "Early stopping\n",
+      "⏹️ Early stopping triggered.\n",
+      "🏁 Final encoder weights saved to ./simclr_pytorch_model/final_encoder.pth\n"
+     ]
+    }
+   ],
+   "source": [
+    "if __name__ == '__main__':\n",
+    "    main()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bc2c608b",
+   "metadata": {
+    "papermill": {
+     "duration": 0.583415,
+     "end_time": "2025-07-29T12:53:03.780484",
+     "exception": false,
+     "start_time": "2025-07-29T12:53:03.197069",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kaggle": {
+   "accelerator": "gpu",
+   "dataSources": [
+    {
+     "datasetId": 7851613,
+     "sourceId": 12447031,
+     "sourceType": "datasetVersion"
+    }
+   ],
+   "dockerImageVersionId": 31090,
+   "isGpuEnabled": true,
+   "isInternetEnabled": true,
+   "language": "python",
+   "sourceType": "notebook"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.13"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "duration": 2431.331951,
+   "end_time": "2025-07-29T12:53:05.937587",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "__notebook__.ipynb",
+   "output_path": "__notebook__.ipynb",
+   "parameters": {},
+   "start_time": "2025-07-29T12:12:34.605636",
+   "version": "2.6.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/E2E_Foundation_Model_Aaditya_Porwal/NoteBooks/Pretraining/Pretraining_VICReg.ipynb b/E2E_Foundation_Model_Aaditya_Porwal/NoteBooks/Pretraining/Pretraining_VICReg.ipynb
new file mode 100644
index 0000000..49d4569
--- /dev/null
+++ b/E2E_Foundation_Model_Aaditya_Porwal/NoteBooks/Pretraining/Pretraining_VICReg.ipynb
@@ -0,0 +1,30213 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2a904772",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-25T12:24:40.372279Z",
+     "iopub.status.busy": "2025-07-25T12:24:40.371706Z",
+     "iopub.status.idle": "2025-07-25T12:24:50.323218Z",
+     "shell.execute_reply": "2025-07-25T12:24:50.322401Z"
+    },
+    "papermill": {
+     "duration": 9.957168,
+     "end_time": "2025-07-25T12:24:50.324786",
+     "exception": false,
+     "start_time": "2025-07-25T12:24:40.367618",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "import torchvision.transforms as T\n",
+    "\n",
+    "import h5py\n",
+    "import numpy as np\n",
+    "from scipy.ndimage import gaussian_filter\n",
+    "from tqdm.auto import tqdm\n",
+    "import os\n",
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ef4bdd4c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-25T12:24:50.331499Z",
+     "iopub.status.busy": "2025-07-25T12:24:50.330733Z",
+     "iopub.status.idle": "2025-07-25T12:24:50.384296Z",
+     "shell.execute_reply": "2025-07-25T12:24:50.383512Z"
+    },
+    "papermill": {
+     "duration": 0.057857,
+     "end_time": "2025-07-25T12:24:50.385691",
+     "exception": false,
+     "start_time": "2025-07-25T12:24:50.327834",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# In your resnet15.ipynb (pre-training) notebook\n",
+    "class Config:\n",
+    "    FILE_PATH = \"/kaggle/input/qc0-1million/train_jet_train_meta_100k.h5\"\n",
+    "    IMAGE_SIZE = 125\n",
+    "    BATCH_SIZE = 256\n",
+    "    EPOCHS = 100\n",
+    "    # CRITICAL: Adjusted learning rate for VICReg with LARS\n",
+    "    LEARNING_RATE = 0.2 \n",
+    "    ROTATION_DEGREES = 60.0\n",
+    "    GAUSSIAN_BLUR_SIGMA = (0.1, 2.0)\n",
+    "    MODEL_SAVE_DIR = \"./simclr_pytorch_model\"\n",
+    "    DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "    \n",
+    "    # VICReg Hyperparameters\n",
+    "    SIM_COEFF = 25.0\n",
+    "    STD_COEFF = 25.0\n",
+    "    COV_COEFF = 1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "db96251c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-25T12:24:50.391734Z",
+     "iopub.status.busy": "2025-07-25T12:24:50.391203Z",
+     "iopub.status.idle": "2025-07-25T12:24:50.404605Z",
+     "shell.execute_reply": "2025-07-25T12:24:50.403818Z"
+    },
+    "papermill": {
+     "duration": 0.017575,
+     "end_time": "2025-07-25T12:24:50.405864",
+     "exception": false,
+     "start_time": "2025-07-25T12:24:50.388289",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using device: cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "if not os.path.exists(Config.MODEL_SAVE_DIR):\n",
+    "    os.makedirs(Config.MODEL_SAVE_DIR)\n",
+    "    \n",
+    "print(f\"Using device: {Config.DEVICE}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bb1f3797",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-25T12:24:50.411398Z",
+     "iopub.status.busy": "2025-07-25T12:24:50.411105Z",
+     "iopub.status.idle": "2025-07-25T12:24:50.416145Z",
+     "shell.execute_reply": "2025-07-25T12:24:50.415479Z"
+    },
+    "papermill": {
+     "duration": 0.009028,
+     "end_time": "2025-07-25T12:24:50.417217",
+     "exception": false,
+     "start_time": "2025-07-25T12:24:50.408189",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# In your resnet15.ipynb (pre-training) notebook\n",
+    "class InMemoryPretrainDataset(Dataset):\n",
+    "    \"\"\"A dataset that loads all pre-training data into RAM for speed.\"\"\"\n",
+    "    def __init__(self, h5_path):\n",
+    "        print(\"Loading pre-training data into memory... (This may take a few minutes)\")\n",
+    "        with h5py.File(h5_path, 'r') as f:\n",
+    "            self.images = f['train_jet'][:70000]\n",
+    "        print(\"Data loaded into RAM.\")\n",
+    "        \n",
+    "        self.length = len(self.images)\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return self.length\n",
+    "\n",
+    "    def __getitem__(self, idx):\n",
+    "        image = self.images[idx]\n",
+    "        # Convert to tensor. Normalization will happen on the GPU.\n",
+    "        image_tensor = torch.from_numpy(image).permute(2, 0, 1).float()\n",
+    "        return image_tensor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d66e1098",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-25T12:24:50.422502Z",
+     "iopub.status.busy": "2025-07-25T12:24:50.422300Z",
+     "iopub.status.idle": "2025-07-25T12:24:50.427828Z",
+     "shell.execute_reply": "2025-07-25T12:24:50.427146Z"
+    },
+    "papermill": {
+     "duration": 0.009382,
+     "end_time": "2025-07-25T12:24:50.428883",
+     "exception": false,
+     "start_time": "2025-07-25T12:24:50.419501",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class ResidualBlock(nn.Module):\n",
+    "    def __init__(self, in_channels, out_channels, stride=1):\n",
+    "        super().__init__()\n",
+    "        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)\n",
+    "        self.bn1 = nn.BatchNorm2d(out_channels)\n",
+    "        self.relu = nn.ReLU(inplace=True)\n",
+    "        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)\n",
+    "        self.bn2 = nn.BatchNorm2d(out_channels)\n",
+    "        \n",
+    "        self.shortcut = nn.Sequential()\n",
+    "        if stride != 1 or in_channels != out_channels:\n",
+    "            self.shortcut = nn.Sequential(\n",
+    "                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),\n",
+    "                nn.BatchNorm2d(out_channels)\n",
+    "            )\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        out = self.relu(self.bn1(self.conv1(x)))\n",
+    "        out = self.bn2(self.conv2(out))\n",
+    "        out += self.shortcut(x)\n",
+    "        out = self.relu(out)\n",
+    "        return out"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "956d09b1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-25T12:24:50.434098Z",
+     "iopub.status.busy": "2025-07-25T12:24:50.433906Z",
+     "iopub.status.idle": "2025-07-25T12:24:50.442650Z",
+     "shell.execute_reply": "2025-07-25T12:24:50.441983Z"
+    },
+    "papermill": {
+     "duration": 0.012698,
+     "end_time": "2025-07-25T12:24:50.443811",
+     "exception": false,
+     "start_time": "2025-07-25T12:24:50.431113",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class Encoder(nn.Module):\n",
+    "    \"\"\"ResNet-15 Encoder\"\"\"\n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "        self.in_channels = 64\n",
+    "        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)\n",
+    "        self.bn1 = nn.BatchNorm2d(64)\n",
+    "        self.relu = nn.ReLU(inplace=True)\n",
+    "        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
+    "        \n",
+    "        self.layer1 = self._make_layer(64, 2, stride=1)\n",
+    "        self.layer2 = self._make_layer(128, 2, stride=2)\n",
+    "        self.layer3 = self._make_layer(256, 2, stride=2)\n",
+    "        # Total layers: 1 (conv1) + 2*2 + 2*2 + 2*2 = 13, a good ResNet-15 approx.\n",
+    "        \n",
+    "        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n",
+    "\n",
+    "    def _make_layer(self, out_channels, num_blocks, stride):\n",
+    "        strides = [stride] + [1]*(num_blocks-1)\n",
+    "        layers = []\n",
+    "        for s in strides:\n",
+    "            layers.append(ResidualBlock(self.in_channels, out_channels, s))\n",
+    "            self.in_channels = out_channels\n",
+    "        return nn.Sequential(*layers)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = self.relu(self.bn1(self.conv1(x)))\n",
+    "        x = self.maxpool(x)\n",
+    "        x = self.layer1(x)\n",
+    "        x = self.layer2(x)\n",
+    "        x = self.layer3(x)\n",
+    "        x = self.avgpool(x)\n",
+    "        x = torch.flatten(x, 1) # Flatten\n",
+    "        return x\n",
+    "\n",
+    "# In your resnet15.ipynb (pre-training) notebook\n",
+    "class ProjectionHead(nn.Module):\n",
+    "    \"\"\"A larger 3-layer MLP expander, as recommended for VICReg.\"\"\"\n",
+    "    def __init__(self, in_features, hidden_features=1024, out_features=1024):\n",
+    "        super().__init__()\n",
+    "        self.net = nn.Sequential(\n",
+    "            nn.Linear(in_features, hidden_features),\n",
+    "            nn.BatchNorm1d(hidden_features),\n",
+    "            nn.ReLU(),\n",
+    "            nn.Linear(hidden_features, hidden_features),\n",
+    "            nn.BatchNorm1d(hidden_features),\n",
+    "            nn.ReLU(),\n",
+    "            nn.Linear(hidden_features, out_features)\n",
+    "        )\n",
+    "    def forward(self, x):\n",
+    "        return self.net(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "70e2d607",
+   "metadata": {
+    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+    "execution": {
+     "iopub.execute_input": "2025-07-25T12:24:50.449059Z",
+     "iopub.status.busy": "2025-07-25T12:24:50.448866Z",
+     "iopub.status.idle": "2025-07-25T12:24:50.455303Z",
+     "shell.execute_reply": "2025-07-25T12:24:50.454579Z"
+    },
+    "papermill": {
+     "duration": 0.010427,
+     "end_time": "2025-07-25T12:24:50.456372",
+     "exception": false,
+     "start_time": "2025-07-25T12:24:50.445945",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# In your resnet15.ipynb (pre-training) notebook\n",
+    "# --- REPLACE NTXentLoss with VICRegLoss ---\n",
+    "class VICRegLoss(nn.Module):\n",
+    "    def __init__(self, sim_coeff=25.0, std_coeff=25.0, cov_coeff=1.0):\n",
+    "        super().__init__()\n",
+    "        self.sim_coeff = sim_coeff\n",
+    "        self.std_coeff = std_coeff\n",
+    "        self.cov_coeff = cov_coeff\n",
+    "\n",
+    "    def forward(self, z1, z2):\n",
+    "        batch_size, num_features = z1.shape\n",
+    "\n",
+    "        # --- Invariance Loss (Similarity) ---\n",
+    "        # Pushes the two views to have the same mean embedding\n",
+    "        sim_loss = nn.functional.mse_loss(z1, z2)\n",
+    "\n",
+    "        # --- Variance Loss ---\n",
+    "        # Prevents the model from collapsing by ensuring the variance\n",
+    "        # along each feature dimension is above a threshold (gamma=1)\n",
+    "        std_z1 = torch.sqrt(z1.var(dim=0) + 1e-04)\n",
+    "        std_z2 = torch.sqrt(z2.var(dim=0) + 1e-04)\n",
+    "        std_loss = torch.mean(nn.functional.relu(1 - std_z1)) + torch.mean(nn.functional.relu(1 - std_z2))\n",
+    "\n",
+    "        # --- Covariance Loss ---\n",
+    "        # Decorrelates the feature dimensions\n",
+    "        z1 = z1 - z1.mean(dim=0)\n",
+    "        z2 = z2 - z2.mean(dim=0)\n",
+    "        cov_z1 = (z1.T @ z1) / (batch_size - 1)\n",
+    "        cov_z2 = (z2.T @ z2) / (batch_size - 1)\n",
+    "        \n",
+    "        # Sum the squared off-diagonal elements\n",
+    "        cov_loss = (self.off_diagonal(cov_z1).pow_(2).sum() / num_features) + \\\n",
+    "                   (self.off_diagonal(cov_z2).pow_(2).sum() / num_features)\n",
+    "\n",
+    "        # Combine the three loss terms with their coefficients\n",
+    "        total_loss = (self.sim_coeff * sim_loss) + \\\n",
+    "                     (self.std_coeff * std_loss) + \\\n",
+    "                     (self.cov_coeff * cov_loss)\n",
+    "        \n",
+    "        return total_loss\n",
+    "\n",
+    "    def off_diagonal(self, x):\n",
+    "        # return a flattened view of the off-diagonal elements of a square matrix\n",
+    "        n, m = x.shape\n",
+    "        assert n == m\n",
+    "        return x.flatten()[:-1].view(n - 1, n + 1)[:, 1:].flatten()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "73db2892",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-25T12:24:50.461767Z",
+     "iopub.status.busy": "2025-07-25T12:24:50.461263Z",
+     "iopub.status.idle": "2025-07-25T12:24:50.466197Z",
+     "shell.execute_reply": "2025-07-25T12:24:50.465671Z"
+    },
+    "papermill": {
+     "duration": 0.008577,
+     "end_time": "2025-07-25T12:24:50.467166",
+     "exception": false,
+     "start_time": "2025-07-25T12:24:50.458589",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# In your resnet15.ipynb (pre-training) notebook\n",
+    "class EarlyStopping:\n",
+    "    \"\"\"\n",
+    "    Stops training when validation loss doesn't improve.\n",
+    "    Saves the best model based on minimum validation loss.\n",
+    "    \"\"\"\n",
+    "    def __init__(self, encoder, patience=10, min_delta=0.001, save_path=\"best_encoder.pth\"):\n",
+    "        self.encoder = encoder\n",
+    "        self.patience = patience\n",
+    "        self.min_delta = min_delta\n",
+    "        self.save_path = save_path\n",
+    "        self.counter = 0\n",
+    "        self.best_loss = float('inf')\n",
+    "        self.early_stop = False\n",
+    "\n",
+    "    def __call__(self, val_loss):\n",
+    "        if self.best_loss - val_loss > self.min_delta:\n",
+    "            self.best_loss = val_loss\n",
+    "            self.counter = 0\n",
+    "            print(f\"Validation loss decreased to {self.best_loss:.4f}. Saving model to {self.save_path}\")\n",
+    "            # Save the encoder state dictionary\n",
+    "            torch.save(self.encoder.state_dict(), self.save_path)\n",
+    "        else:\n",
+    "            self.counter += 1\n",
+    "            print(f\"EarlyStopping counter: {self.counter} out of {self.patience}\")\n",
+    "            if self.counter >= self.patience:\n",
+    "                print(\"Early stopping triggered.\")\n",
+    "                self.early_stop = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b4e66b98",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-25T12:24:50.472465Z",
+     "iopub.status.busy": "2025-07-25T12:24:50.472282Z",
+     "iopub.status.idle": "2025-07-25T12:24:50.478731Z",
+     "shell.execute_reply": "2025-07-25T12:24:50.478183Z"
+    },
+    "papermill": {
+     "duration": 0.010316,
+     "end_time": "2025-07-25T12:24:50.479710",
+     "exception": false,
+     "start_time": "2025-07-25T12:24:50.469394",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# In your resnet15.ipynb (pre-training) notebook\n",
+    "\n",
+    "class LARS(optim.Optimizer):\n",
+    "    \"\"\"\n",
+    "    LARS optimizer, implementation from PyTorch Lightning Bolts.\n",
+    "    https://github.com/Lightning-AI/lightning-bolts/blob/master/pl_bolts/optimizers/lars.py\n",
+    "    \"\"\"\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        params,\n",
+    "        lr,\n",
+    "        momentum=0.9,\n",
+    "        weight_decay=1e-6,\n",
+    "        trust_coefficient=0.001,\n",
+    "        eps=1e-8,\n",
+    "    ):\n",
+    "        defaults = dict(\n",
+    "            lr=lr,\n",
+    "            momentum=momentum,\n",
+    "            weight_decay=weight_decay,\n",
+    "            trust_coefficient=trust_coefficient,\n",
+    "            eps=eps,\n",
+    "        )\n",
+    "        super().__init__(params, defaults)\n",
+    "\n",
+    "    @torch.no_grad()\n",
+    "    def step(self, closure=None):\n",
+    "        loss = None\n",
+    "        if closure is not None:\n",
+    "            with torch.enable_grad():\n",
+    "                loss = closure()\n",
+    "\n",
+    "        for group in self.param_groups:\n",
+    "            weight_decay = group[\"weight_decay\"]\n",
+    "            momentum = group[\"momentum\"]\n",
+    "            lr = group[\"lr\"]\n",
+    "            trust_coefficient = group[\"trust_coefficient\"]\n",
+    "            eps = group[\"eps\"]\n",
+    "\n",
+    "            for p in group[\"params\"]:\n",
+    "                if p.grad is None:\n",
+    "                    continue\n",
+    "\n",
+    "                grad = p.grad\n",
+    "                \n",
+    "                if weight_decay != 0:\n",
+    "                    grad = grad.add(p, alpha=weight_decay)\n",
+    "\n",
+    "                param_norm = torch.norm(p)\n",
+    "                grad_norm = torch.norm(grad)\n",
+    "                \n",
+    "                # Compute the local learning rate for this layer\n",
+    "                local_lr = lr * trust_coefficient * param_norm / (grad_norm + eps)\n",
+    "\n",
+    "                # Update the momentum term\n",
+    "                if momentum != 0:\n",
+    "                    param_state = self.state[p]\n",
+    "                    if \"momentum_buffer\" not in param_state:\n",
+    "                        buf = param_state[\"momentum_buffer\"] = torch.clone(grad).detach()\n",
+    "                    else:\n",
+    "                        buf = param_state[\"momentum_buffer\"]\n",
+    "                        buf.mul_(momentum).add_(grad)\n",
+    "                    grad = buf\n",
+    "\n",
+    "                p.add_(grad, alpha=-local_lr)\n",
+    "        return loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6c8ce6b7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-25T12:24:50.484850Z",
+     "iopub.status.busy": "2025-07-25T12:24:50.484678Z",
+     "iopub.status.idle": "2025-07-25T12:24:50.647652Z",
+     "shell.execute_reply": "2025-07-25T12:24:50.646844Z"
+    },
+    "papermill": {
+     "duration": 0.167186,
+     "end_time": "2025-07-25T12:24:50.649001",
+     "exception": false,
+     "start_time": "2025-07-25T12:24:50.481815",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "def main():\n",
+    "    # GPU-based augmentation\n",
+    "    train_transform = nn.Sequential(\n",
+    "        T.RandomHorizontalFlip(),\n",
+    "        T.RandomVerticalFlip(),\n",
+    "        T.RandomRotation(degrees=Config.ROTATION_DEGREES),\n",
+    "        T.GaussianBlur(kernel_size=5, sigma=Config.GAUSSIAN_BLUR_SIGMA)\n",
+    "    ).to(Config.DEVICE)\n",
+    "\n",
+    "    # Load dataset from latent feature file\n",
+    "    full_dataset = InMemoryPretrainDataset(Config.FILE_PATH)\n",
+    "    \n",
+    "    # Split the 70,000 samples into 90% train and 10% validation\n",
+    "    train_size = int(0.95 * len(full_dataset))\n",
+    "    val_size = len(full_dataset) - train_size\n",
+    "    train_dataset, val_dataset = torch.utils.data.random_split(full_dataset, [train_size, val_size])\n",
+    "    \n",
+    "    print(f\"Data split into {len(train_dataset)} training samples and {len(val_dataset)} validation samples.\")\n",
+    "    \n",
+    "    train_loader = DataLoader(\n",
+    "        train_dataset, batch_size=Config.BATCH_SIZE, shuffle=True,\n",
+    "        num_workers=0, drop_last=True\n",
+    "    )\n",
+    "    val_loader = DataLoader(\n",
+    "        val_dataset, batch_size=Config.BATCH_SIZE, shuffle=False,\n",
+    "        num_workers=0, drop_last=False\n",
+    "    )\n",
+    "\n",
+    "    # --- Model Setup ---\n",
+    "    encoder = Encoder().to(Config.DEVICE)\n",
+    "    with torch.no_grad():\n",
+    "        dummy_input = torch.randn(1, 3, Config.IMAGE_SIZE, Config.IMAGE_SIZE).to(Config.DEVICE)\n",
+    "        feature_size = encoder(dummy_input).shape[1]\n",
+    "    \n",
+    "    projection_head = ProjectionHead(in_features=feature_size).to(Config.DEVICE)\n",
+    "    \n",
+    "    optimizer = LARS(\n",
+    "        params=list(encoder.parameters()) + list(projection_head.parameters()),\n",
+    "        lr=Config.LEARNING_RATE,\n",
+    "        weight_decay=1e-4\n",
+    "    )\n",
+    "    \n",
+    "    # --- NEW: Add a learning rate scheduler ---\n",
+    "    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=Config.EPOCHS)\n",
+    "    \n",
+    "    loss_fn = VICRegLoss(\n",
+    "        sim_coeff=Config.SIM_COEFF,\n",
+    "        std_coeff=Config.STD_COEFF,\n",
+    "        cov_coeff=Config.COV_COEFF\n",
+    "    )\n",
+    "    \n",
+    "    # Initialize Early Stopping to save the best model\n",
+    "    save_path = os.path.join(Config.MODEL_SAVE_DIR, \"best_encoder.pth\")\n",
+    "    early_stopper = EarlyStopping(encoder, patience=10, min_delta=0.001, save_path=save_path)\n",
+    "\n",
+    "    print(\"Starting VICReg pre-training with PyTorch...\")\n",
+    "    for epoch in range(Config.EPOCHS):\n",
+    "        # --- Training Phase ---\n",
+    "        encoder.train()\n",
+    "        projection_head.train()\n",
+    "        train_loss = 0\n",
+    "        train_pbar = tqdm(train_loader, desc=f\"Epoch {epoch+1}/{Config.EPOCHS} [Training]\")\n",
+    "        \n",
+    "        # --- CORRECTED LOOP ---\n",
+    "        for images in train_pbar: # The dataloader only yields images\n",
+    "            images = images.to(Config.DEVICE)\n",
+    "            \n",
+    "            images = images / 255.0\n",
+    "            view1 = train_transform(images)\n",
+    "            view2 = train_transform(images)\n",
+    "            \n",
+    "            z1 = projection_head(encoder(view1))\n",
+    "            z2 = projection_head(encoder(view2))\n",
+    "            \n",
+    "            loss = loss_fn(z1, z2)\n",
+    "            \n",
+    "            optimizer.zero_grad()\n",
+    "            loss.backward()\n",
+    "            optimizer.step()\n",
+    "            \n",
+    "            train_loss += loss.item()\n",
+    "            train_pbar.set_postfix({\"Loss\": loss.item()})\n",
+    "        \n",
+    "        avg_train_loss = train_loss / len(train_loader)\n",
+    "\n",
+    "        # --- Validation Phase ---\n",
+    "        encoder.eval()\n",
+    "        projection_head.eval()\n",
+    "        val_loss = 0\n",
+    "        val_pbar = tqdm(val_loader, desc=f\"Epoch {epoch+1}/{Config.EPOCHS} [Validation]\")\n",
+    "        with torch.no_grad():\n",
+    "            # --- CORRECTED LOOP ---\n",
+    "            for images in val_pbar: # The dataloader only yields images\n",
+    "                images = images.to(Config.DEVICE)\n",
+    "                \n",
+    "                images = images / 255.0\n",
+    "                view1 = train_transform(images)\n",
+    "                view2 = train_transform(images)\n",
+    "\n",
+    "                z1 = projection_head(encoder(view1))\n",
+    "                z2 = projection_head(encoder(view2))\n",
+    "                \n",
+    "                loss = loss_fn(z1, z2)\n",
+    "                val_loss += loss.item()\n",
+    "\n",
+    "        avg_val_loss = val_loss / len(val_loader)\n",
+    "        print(f\"Epoch {epoch+1}/{Config.EPOCHS} -> Avg Train Loss: {avg_train_loss:.4f}, Avg Val Loss: {avg_val_loss:.4f}\")\n",
+    "        \n",
+    "        early_stopper(avg_val_loss)\n",
+    "        if early_stopper.early_stop:\n",
+    "            break\n",
+    "            \n",
+    "    print(f\"\\nFinished pre-training. Best model saved at {save_path}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "66bfc3e7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-07-25T12:24:50.655277Z",
+     "iopub.status.busy": "2025-07-25T12:24:50.655034Z",
+     "iopub.status.idle": "2025-07-25T13:21:13.424250Z",
+     "shell.execute_reply": "2025-07-25T13:21:13.423581Z"
+    },
+    "papermill": {
+     "duration": 3382.773372,
+     "end_time": "2025-07-25T13:21:13.425375",
+     "exception": false,
+     "start_time": "2025-07-25T12:24:50.652003",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading pre-training data into memory... (This may take a few minutes)\n",
+      "Data loaded into RAM.\n",
+      "Data split into 66500 training samples and 3500 validation samples.\n",
+      "Starting VICReg pre-training with PyTorch...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "818129ef35284572be502f44eb5b83a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 1/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c05ce27464074445957acd52f6e0e0e7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 1/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/100 -> Avg Train Loss: 30.1576, Avg Val Loss: 76.3634\n",
+      "Validation loss decreased to 76.3634. Saving model to ./simclr_pytorch_model/best_encoder.pth\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "83d80b7038b74af09eab2181271762b8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 2/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "753c3e0145024419bb6d8904f1aa5d16",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 2/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2/100 -> Avg Train Loss: 23.0745, Avg Val Loss: 64.9266\n",
+      "Validation loss decreased to 64.9266. Saving model to ./simclr_pytorch_model/best_encoder.pth\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6cde2954cc2047fba025144f856d3929",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 3/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5dccdd04a9a54b3192c9ec11a4941d95",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 3/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 3/100 -> Avg Train Loss: 19.5320, Avg Val Loss: 45.2517\n",
+      "Validation loss decreased to 45.2517. Saving model to ./simclr_pytorch_model/best_encoder.pth\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c97605e59cff4d40bb627a927e4791e7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 4/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "049b62888389446fb23b198404523ab9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 4/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 4/100 -> Avg Train Loss: 17.2512, Avg Val Loss: 47.7367\n",
+      "EarlyStopping counter: 1 out of 10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fa2d880db3d04c069b6c84d13efbda39",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 5/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "360fdaec1d4c49c193cdcd0974eca4e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 5/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 5/100 -> Avg Train Loss: 16.0760, Avg Val Loss: 66.5827\n",
+      "EarlyStopping counter: 2 out of 10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "98b22071e6d944229f3bed4cfb11f4ba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 6/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4fa783db4764682b58c59f6800ec300",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 6/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 6/100 -> Avg Train Loss: 15.2521, Avg Val Loss: 57.3127\n",
+      "EarlyStopping counter: 3 out of 10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6a05b53fef9b4767a448859e37973c57",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 7/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d2c2eebbf02b43ebb22ccf8523d9b17a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 7/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 7/100 -> Avg Train Loss: 14.4394, Avg Val Loss: 54.7377\n",
+      "EarlyStopping counter: 4 out of 10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6d17f2df47c44ee2a1c5715e942b232a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 8/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "46a39dc5cc4740fd9367b6c4e67ab2e2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 8/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 8/100 -> Avg Train Loss: 14.0132, Avg Val Loss: 48.9394\n",
+      "EarlyStopping counter: 5 out of 10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "acb7ed9bc83245dba5002c006e29bb89",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 9/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aaa3c78acd2e4f558a6d01f985aeceec",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 9/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 9/100 -> Avg Train Loss: 13.7423, Avg Val Loss: 54.9800\n",
+      "EarlyStopping counter: 6 out of 10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c6a05559d00b4e98be0bbed411e6a464",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 10/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fe783c4af1d04f8e9bb50ba4037be0bd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 10/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 10/100 -> Avg Train Loss: 13.5199, Avg Val Loss: 40.1729\n",
+      "Validation loss decreased to 40.1729. Saving model to ./simclr_pytorch_model/best_encoder.pth\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0292d6e7562e470d8db088d4e240271f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 11/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc4e80ac80014292a6a46c52a214fc0f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 11/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 11/100 -> Avg Train Loss: 13.0240, Avg Val Loss: 36.7064\n",
+      "Validation loss decreased to 36.7064. Saving model to ./simclr_pytorch_model/best_encoder.pth\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2b6172e46d20473aa216ac086dbad7b7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 12/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c47f94c3ff7d4b69a5cff721fcd039ac",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 12/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 12/100 -> Avg Train Loss: 12.9461, Avg Val Loss: 40.9629\n",
+      "EarlyStopping counter: 1 out of 10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ebffabdb1094eef8f0a0f31d68e9c76",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 13/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a836f4d84c6c4a0f9f1119f3a27fb852",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 13/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 13/100 -> Avg Train Loss: 12.7929, Avg Val Loss: 35.8587\n",
+      "Validation loss decreased to 35.8587. Saving model to ./simclr_pytorch_model/best_encoder.pth\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f809a61be9545fea9b21933310fdb9c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 14/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d7be21e1dff84470ae9cf19f0c4311d3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 14/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 14/100 -> Avg Train Loss: 12.7592, Avg Val Loss: 43.5610\n",
+      "EarlyStopping counter: 1 out of 10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 15/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 15/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 15/100 -> Avg Train Loss: 12.3692, Avg Val Loss: 43.7226\n",
+      "EarlyStopping counter: 2 out of 10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 16/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f2cc938a2f5449039b3de49972bded04",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 16/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 16/100 -> Avg Train Loss: 12.4654, Avg Val Loss: 48.5037\n",
+      "EarlyStopping counter: 3 out of 10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5ea4bbb15c414ab3a746adbd77db54a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 17/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bddcfdde7ee74a1ab10096c1e48644d8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 17/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 17/100 -> Avg Train Loss: 12.1632, Avg Val Loss: 47.1910\n",
+      "EarlyStopping counter: 4 out of 10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33a261047fee4fceb426fe96a839a70a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 18/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0e442ebb50b54d709e2bbdf33ff3d6d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 18/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 18/100 -> Avg Train Loss: 12.0173, Avg Val Loss: 43.1988\n",
+      "EarlyStopping counter: 5 out of 10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ed3386daf204788adf2475978682a3f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 19/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "45a141624dbe4baa87f9fd4082264841",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 19/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 19/100 -> Avg Train Loss: 11.8932, Avg Val Loss: 37.5795\n",
+      "EarlyStopping counter: 6 out of 10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "108a64ff0831405eb23d0c9baf2664fd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 20/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "426dc8e2a43c4bfba38e54f15bebcd72",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 20/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 20/100 -> Avg Train Loss: 11.9234, Avg Val Loss: 38.5253\n",
+      "EarlyStopping counter: 7 out of 10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f6c27a25aa5408c87614437ab4fbb60",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 21/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b0a3af23061481a92dbde28c1956738",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 21/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 21/100 -> Avg Train Loss: 11.9778, Avg Val Loss: 36.6173\n",
+      "EarlyStopping counter: 8 out of 10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e22e86905977484cb819c7c7c1e13ccc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 22/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3434df444db342a1927bfa0908b4192f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 22/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 22/100 -> Avg Train Loss: 11.7051, Avg Val Loss: 38.7309\n",
+      "EarlyStopping counter: 9 out of 10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fc175bd67424467499929ba354aab2bc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 23/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "87940267d9224127939cacf45f64bb87",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 23/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 23/100 -> Avg Train Loss: 11.8684, Avg Val Loss: 31.2537\n",
+      "Validation loss decreased to 31.2537. Saving model to ./simclr_pytorch_model/best_encoder.pth\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "206b311e4d794d5b87ccfd721ad3f46b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 24/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "12eb7608407949b9a9032927863fe191",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 24/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 24/100 -> Avg Train Loss: 11.5277, Avg Val Loss: 38.5015\n",
+      "EarlyStopping counter: 1 out of 10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "26fd1fc5c051423ca026e8ea811d9870",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 25/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b5b00148f8f54c4eb444ee0201ba2119",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 25/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 25/100 -> Avg Train Loss: 11.6157, Avg Val Loss: 32.5555\n",
+      "EarlyStopping counter: 2 out of 10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f6a265aa61394429a2fb1a2ed8a558fd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 26/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d980756255f4cff9f338e060ec60a73",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 26/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 26/100 -> Avg Train Loss: 11.4493, Avg Val Loss: 41.5951\n",
+      "EarlyStopping counter: 3 out of 10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ec7b0c3df02d4cc386a653ad4faf28b1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 27/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "03aa47fe9f314e82aac731c20e19dd12",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 27/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 27/100 -> Avg Train Loss: 11.3351, Avg Val Loss: 37.6677\n",
+      "EarlyStopping counter: 4 out of 10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4befdae6494c41e49004baaebb92c26c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 28/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f5ed242b1af642b9a64f0049d58e6b88",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 28/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 28/100 -> Avg Train Loss: 11.3283, Avg Val Loss: 40.2617\n",
+      "EarlyStopping counter: 5 out of 10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "631b89ab5b2543bcaef1a5ca0acc8cb5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 29/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9cd145a456374959b89b5c65d5c6b291",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 29/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 29/100 -> Avg Train Loss: 11.2616, Avg Val Loss: 27.1831\n",
+      "Validation loss decreased to 27.1831. Saving model to ./simclr_pytorch_model/best_encoder.pth\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6464b48c84d64abfb481cf3bdb8cf3f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 30/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0177975c9acf43b79f028ac44bc1d569",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 30/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 30/100 -> Avg Train Loss: 11.1917, Avg Val Loss: 38.1742\n",
+      "EarlyStopping counter: 1 out of 10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f1a8d6edc1040d0809755604d06edb9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 31/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78110e57a3814b1cbc6aeb264b1d9127",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 31/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 31/100 -> Avg Train Loss: 11.1791, Avg Val Loss: 28.9329\n",
+      "EarlyStopping counter: 2 out of 10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "35f393792ad24c07b7440af77aaab07a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 32/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1175ef63c7dd41259735876f33e1d7c8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 32/100 [Validation]:   0%|          | 0/14 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 32/100 -> Avg Train Loss: 11.1104, Avg Val Loss: 34.1022\n",
+      "EarlyStopping counter: 3 out of 10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2354f3ea92ad452cad36a4922ae7d2b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Epoch 33/100 [Training]:   0%|          | 0/259 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
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diff --git a/E2E_Foundation_Model_Aaditya_Porwal/NoteBooks/Regression/SSL_Regression.ipynb b/E2E_Foundation_Model_Aaditya_Porwal/NoteBooks/Regression/SSL_Regression.ipynb
new file mode 100644
index 0000000..8afb866
--- /dev/null
+++ b/E2E_Foundation_Model_Aaditya_Porwal/NoteBooks/Regression/SSL_Regression.ipynb
@@ -0,0 +1 @@
+{"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":"gpu","dataSources":[{"sourceId":12447031,"sourceType":"datasetVersion","datasetId":7851613},{"sourceId":12497952,"sourceType":"datasetVersion","datasetId":7887522},{"sourceId":12540315,"sourceType":"datasetVersion","datasetId":7916922},{"sourceId":12567791,"sourceType":"datasetVersion","datasetId":7936657},{"sourceId":12610458,"sourceType":"datasetVersion","datasetId":7941414},{"sourceId":253122923,"sourceType":"kernelVersion"},{"sourceId":484220,"sourceType":"modelInstanceVersion","modelInstanceId":386984,"modelId":406073}],"dockerImageVersionId":31089,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import Dataset, DataLoader, Subset\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_squared_error, f1_score, roc_auc_score, mean_absolute_error\nimport torchvision.transforms as T\nimport matplotlib.pyplot as plt\nimport h5py\nimport numpy as np\nfrom tqdm.auto import tqdm\nfrom sklearn.preprocessing import StandardScaler\nimport os","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-19T14:52:01.584688Z","iopub.execute_input":"2025-08-19T14:52:01.584961Z","iopub.status.idle":"2025-08-19T14:52:09.534103Z","shell.execute_reply.started":"2025-08-19T14:52:01.584935Z","shell.execute_reply":"2025-08-19T14:52:09.533514Z"}},"outputs":[],"execution_count":2},{"cell_type":"code","source":"class Config:\n    # --- Paths ---\n    DATA_FILE_PATH = \"/kaggle/input/qc0-1million/train_jet_train_meta_100k.h5\"\n    PRETRAINED_ENCODER_PATH = \"/kaggle/input/lars-and-normalized/simclr_pytorch_model/final_encoder.pth\"\n\n    # --- Data ---\n    IMAGE_SIZE = 125\n    EVAL_START_IDX = 70000\n    EVAL_END_IDX = 100000\n    TEST_SET_FRACTION = 0.5\n\n    # --- Training (same as 3rd supervised) ---\n    BATCH_SIZE = 128\n    EPOCHS = 25\n    LEARNING_RATE = 1e-4\n    WEIGHT_DECAY = 1e-4\n    DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-19T14:53:40.030590Z","iopub.execute_input":"2025-08-19T14:53:40.030872Z","iopub.status.idle":"2025-08-19T14:53:40.035499Z","shell.execute_reply.started":"2025-08-19T14:53:40.030850Z","shell.execute_reply":"2025-08-19T14:53:40.034643Z"}},"outputs":[],"execution_count":17},{"cell_type":"code","source":"\nprint(f\"Using device: {Config.DEVICE}\")\n\n# Check if files exist before proceeding\nif not os.path.exists(Config.DATA_FILE_PATH):\n    print(f\"ERROR: Data file not found at '{Config.DATA_FILE_PATH}'\")\n    print(\"Please download the dataset and update the path in the Config class.\")\nif not os.path.exists(Config.PRETRAINED_ENCODER_PATH):\n    print(f\"ERROR: Pre-trained encoder not found at '{Config.PRETRAINED_ENCODER_PATH}'\")\n    print(\"Please make sure you have run the pre-training script and the path is correct.\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-19T14:52:10.950184Z","iopub.execute_input":"2025-08-19T14:52:10.950843Z","iopub.status.idle":"2025-08-19T14:52:10.962613Z","shell.execute_reply.started":"2025-08-19T14:52:10.950816Z","shell.execute_reply":"2025-08-19T14:52:10.961850Z"}},"outputs":[{"name":"stdout","text":"Using device: cuda\n","output_type":"stream"}],"execution_count":4},{"cell_type":"code","source":"\nclass ResidualBlock(nn.Module):\n    def __init__(self, in_channels, out_channels, stride=1):\n        super().__init__()\n        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)\n        self.bn1 = nn.BatchNorm2d(out_channels)\n        self.relu = nn.ReLU(inplace=True)\n        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)\n        self.bn2 = nn.BatchNorm2d(out_channels)\n        self.shortcut = nn.Sequential()\n        if stride != 1 or in_channels != out_channels:\n            self.shortcut = nn.Sequential(\n                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),\n                nn.BatchNorm2d(out_channels)\n            )\n    def forward(self, x):\n        out = self.relu(self.bn1(self.conv1(x)))\n        out = self.bn2(self.conv2(out))\n        out += self.shortcut(x)\n        out = self.relu(out)\n        return out","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-19T14:52:11.506122Z","iopub.execute_input":"2025-08-19T14:52:11.506373Z","iopub.status.idle":"2025-08-19T14:52:11.512111Z","shell.execute_reply.started":"2025-08-19T14:52:11.506331Z","shell.execute_reply":"2025-08-19T14:52:11.511419Z"}},"outputs":[],"execution_count":5},{"cell_type":"code","source":"class Encoder(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.in_channels = 64\n        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)\n        self.bn1 = nn.BatchNorm2d(64)\n        self.relu = nn.ReLU(inplace=True)\n        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n        self.layer1 = self._make_layer(64, 2, stride=1)\n        self.layer2 = self._make_layer(128, 2, stride=2)\n        self.layer3 = self._make_layer(256, 2, stride=2)\n        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n    def _make_layer(self, out_channels, num_blocks, stride):\n        strides = [stride] + [1]*(num_blocks-1)\n        layers = []\n        for s in strides:\n            layers.append(ResidualBlock(self.in_channels, out_channels, s))\n            self.in_channels = out_channels\n        return nn.Sequential(*layers)\n    def forward(self, x):\n        x = self.relu(self.bn1(self.conv1(x)))\n        x = self.maxpool(x)\n        x = self.layer1(x)\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.avgpool(x)\n        x = torch.flatten(x, 1)\n        return x\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-19T14:52:11.513295Z","iopub.execute_input":"2025-08-19T14:52:11.513536Z","iopub.status.idle":"2025-08-19T14:52:11.530946Z","shell.execute_reply.started":"2025-08-19T14:52:11.513519Z","shell.execute_reply":"2025-08-19T14:52:11.530339Z"}},"outputs":[],"execution_count":6},{"cell_type":"code","source":"class SSLRegressionModel(nn.Module):\n    def __init__(self, feature_size=256, out_features=1, dropout_p=0.5, ckpt_path=None, device=\"cpu\"):\n        super().__init__()\n        self.encoder = Encoder()\n        if ckpt_path is not None and os.path.exists(ckpt_path):\n            state = torch.load(ckpt_path, map_location=device)\n            # Attempt strict load; if keys are nested, user can adjust mapping\n            self.encoder.load_state_dict(state, strict=False)\n        for p in self.encoder.parameters():\n            p.requires_grad = False\n        self.dropout = nn.Dropout(p=dropout_p)\n        self.regressor = nn.Linear(feature_size, out_features)\n    def forward(self, x):\n        feats = self.encoder(x)\n        feats = self.dropout(feats)\n        return self.regressor(feats)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-19T14:52:11.607689Z","iopub.execute_input":"2025-08-19T14:52:11.607861Z","iopub.status.idle":"2025-08-19T14:52:11.613098Z","shell.execute_reply.started":"2025-08-19T14:52:11.607847Z","shell.execute_reply":"2025-08-19T14:52:11.612404Z"}},"outputs":[],"execution_count":7},{"cell_type":"code","source":"class InMemoryJetDataset(Dataset):\n    def __init__(self, h5_path, start_idx, end_idx, target_idx=0, scaler=None):\n        self.target_idx = target_idx\n        with h5py.File(h5_path, 'r') as f:\n            self.images = f['train_jet'][start_idx:end_idx]\n            self.labels = f['train_meta'][start_idx:end_idx, self.target_idx].reshape(-1, 1)\n        if scaler:\n            self.labels = scaler.transform(self.labels)\n    def __len__(self):\n        return len(self.images)\n    def __getitem__(self, idx):\n        image = self.images[idx]\n        label = self.labels[idx]\n        image_tensor = torch.from_numpy(image).permute(2, 0, 1).float()\n        label_tensor = torch.tensor(label, dtype=torch.float32)\n        return image_tensor, label_tensor","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-19T14:52:11.620425Z","iopub.execute_input":"2025-08-19T14:52:11.620638Z","iopub.status.idle":"2025-08-19T14:52:11.629607Z","shell.execute_reply.started":"2025-08-19T14:52:11.620621Z","shell.execute_reply":"2025-08-19T14:52:11.628960Z"}},"outputs":[],"execution_count":8},{"cell_type":"code","source":"class EarlyStopping:\n    def __init__(self, patience=5, min_delta=0.001, verbose=True):\n        self.patience, self.min_delta, self.verbose = patience, min_delta, verbose\n        self.counter, self.best_mae, self.early_stop = 0, float('inf'), False\n    def __call__(self, current_mae):\n        if self.best_mae - current_mae > self.min_delta:\n            if self.verbose: print(f\"  -> Val MAE improved ({self.best_mae:.4f} --> {current_mae:.4f}).\")\n            self.best_mae = current_mae\n            self.counter = 0\n        else:\n            self.counter += 1\n            if self.verbose: print(f\"  -> EarlyStopping counter: {self.counter} out of {self.patience}\")\n            if self.counter >= self.patience:\n                print(\"  -> Early stopping triggered.\")\n                self.early_stop = True","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-19T14:52:11.710698Z","iopub.execute_input":"2025-08-19T14:52:11.710893Z","iopub.status.idle":"2025-08-19T14:52:11.715816Z","shell.execute_reply.started":"2025-08-19T14:52:11.710878Z","shell.execute_reply":"2025-08-19T14:52:11.715271Z"}},"outputs":[],"execution_count":9},{"cell_type":"code","source":"def run_supervised_training(label_percentage, tuning_pool_indices, full_eval_dataset, test_dataset, target_name, scaler):\n    print(f\"\\n--- Running Supervised Baseline for {target_name.upper()} ({label_percentage*100:.0f}% Labels) ---\")\n\n    train_indices, _ = train_test_split(tuning_pool_indices, train_size=label_percentage, random_state=42) if label_percentage < 1.0 else (tuning_pool_indices, None)\n\n    train_subset = Subset(full_eval_dataset, train_indices)\n    print(f\"Training on {len(train_subset)} samples, testing on {len(test_dataset)} samples.\")\n    train_loader = DataLoader(train_subset, batch_size=Config.BATCH_SIZE, shuffle=True, num_workers=0)\n    test_loader = DataLoader(test_dataset, batch_size=Config.BATCH_SIZE, shuffle=False, num_workers=0)\n\n    # SSL model: frozen pretrained encoder + dropout + linear\n    model = SSLRegressionModel(\n        feature_size=256,\n        out_features=1,\n        dropout_p=0.5,\n        ckpt_path=Config.PRETRAINED_ENCODER_PATH,\n        device=Config.DEVICE\n    ).to(Config.DEVICE)\n\n    optimizer = optim.Adam(model.parameters(), lr=Config.LEARNING_RATE, weight_decay=Config.WEIGHT_DECAY)\n    scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', patience=3, factor=0.5, verbose=True)\n    criterion = nn.L1Loss()\n    early_stopper = EarlyStopping(patience=5)\n\n    best_mae = float('inf')\n    history = {'train_loss': [], 'val_mae': []}\n    best_model_path = f\"best_model_{target_name}_{int(label_percentage*100)}.pth\"\n\n    for epoch in range(Config.EPOCHS):\n        model.train()\n        train_loss = 0\n        for images, labels_scaled in train_loader:\n            images, labels_scaled = images.to(Config.DEVICE), labels_scaled.to(Config.DEVICE)\n            images = (images / 255.0)\n            images = train_transform(images)  # Identity (to match 3rd code)\n            optimizer.zero_grad()\n            outputs_scaled = model(images)\n            loss = criterion(outputs_scaled, labels_scaled)\n            loss.backward()\n            optimizer.step()\n            train_loss += loss.item()\n        history['train_loss'].append(train_loss / len(train_loader))\n\n        model.eval()\n        all_labels_scaled, all_preds_scaled = [], []\n        with torch.no_grad():\n            for images, labels_scaled in test_loader:\n                images, labels_scaled = images.to(Config.DEVICE), labels_scaled.to(Config.DEVICE)\n                images = (images / 255.0)\n                images = test_transform(images)  # Identity\n                preds_scaled = model(images)\n                all_labels_scaled.append(labels_scaled.cpu().numpy())\n                all_preds_scaled.append(preds_scaled.cpu().numpy())\n\n        all_labels_orig = scaler.inverse_transform(np.concatenate(all_labels_scaled))\n        all_preds_orig = scaler.inverse_transform(np.concatenate(all_preds_scaled))\n        val_mae = mean_absolute_error(all_labels_orig, all_preds_orig)\n        history['val_mae'].append(val_mae)\n\n        print(f\"Epoch [{epoch+1}/{Config.EPOCHS}] -> Train MAE Loss: {history['train_loss'][-1]:.4f} | Val MAE: {val_mae:.4f}\")\n\n        scheduler.step(val_mae)\n\n        if val_mae < best_mae:\n            best_mae = val_mae\n            torch.save(model.state_dict(), best_model_path)\n\n        early_stopper(val_mae)\n        if early_stopper.early_stop:\n            break\n\n    print(f\"--- Loading best model with MAE: {best_mae:.4f} for plotting ---\")\n    model.load_state_dict(torch.load(best_model_path, map_location=Config.DEVICE))\n    model.eval()\n\n    best_labels_scaled, best_preds_scaled = [], []\n    with torch.no_grad():\n        for images, labels_scaled in test_loader:\n            images, labels_scaled = images.to(Config.DEVICE), labels_scaled.to(Config.DEVICE)\n            images = (images / 255.0)\n            images = test_transform(images)\n            preds_scaled = model(images)\n            best_labels_scaled.append(labels_scaled.cpu().numpy())\n            best_preds_scaled.append(preds_scaled.cpu().numpy())\n\n    best_labels_orig = scaler.inverse_transform(np.concatenate(best_labels_scaled))\n    best_preds_orig = scaler.inverse_transform(np.concatenate(best_preds_scaled))\n\n    print(f\"Best Test MAE for {label_percentage*100:.0f}%: {best_mae:.4f}\")\n    return best_mae, history, best_labels_orig, best_preds_orig\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-19T14:52:11.716862Z","iopub.execute_input":"2025-08-19T14:52:11.717475Z","iopub.status.idle":"2025-08-19T14:52:11.732624Z","shell.execute_reply.started":"2025-08-19T14:52:11.717457Z","shell.execute_reply":"2025-08-19T14:52:11.732098Z"}},"outputs":[],"execution_count":10},{"cell_type":"code","source":"def plot_metrics_vs_epochs(history_train_loss, history_val_mae, target_name):\n    plt.style.use('seaborn-v0_8-whitegrid')\n    fig, ax = plt.subplots(2, 1, figsize=(12, 10), sharex=True)\n\n    for percent_str, losses in history_train_loss.items():\n        ax[0].plot(range(1, len(losses) + 1), losses, marker='o', linestyle='-', label=f'{percent_str} Train Loss')\n    ax[0].set_ylabel('MAE Loss (Scaled Data)', fontsize=12)\n    ax[0].set_title(f'Training Loss for {target_name.upper()}', fontsize=14, weight='bold')\n    ax[0].legend()\n    ax[0].grid(True)\n\n    for percent_str, maes in history_val_mae.items():\n        ax[1].plot(range(1, len(maes) + 1), maes, marker='x', linestyle='--', label=f'{percent_str} Val MAE')\n    ax[1].set_ylabel('MAE (Original Scale)', fontsize=12)\n    ax[1].set_title(f'Validation MAE for {target_name.upper()}', fontsize=14, weight='bold')\n    ax[1].set_xlabel('Epoch', fontsize=14)\n    ax[1].legend()\n    ax[1].grid(True)\n\n    fig.suptitle(f'Supervised {target_name.upper()} Training Metrics vs. Epochs', fontsize=16, weight='bold')\n    plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n    plt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-19T14:52:11.813586Z","iopub.execute_input":"2025-08-19T14:52:11.813972Z","iopub.status.idle":"2025-08-19T14:52:11.819808Z","shell.execute_reply.started":"2025-08-19T14:52:11.813955Z","shell.execute_reply":"2025-08-19T14:52:11.819250Z"}},"outputs":[],"execution_count":11},{"cell_type":"code","source":"def plot_results(results_mae, target_name):\n    print(f\"\\n--- Final Results for {target_name.upper()} ---\")\n    for percent, mae in results_mae.items():\n        print(f\"Best MAE with {percent} labels: {mae:.4f}\")\n    plt.style.use('seaborn-v0_8-whitegrid')\n    fig, ax = plt.subplots(figsize=(10, 6))\n    bars = ax.bar(results_mae.keys(), results_mae.values(),\n                  color=['#ff9999','#66b3ff','#99ff99','#ffcc99', '#c2c2f0'])\n    ax.set_ylabel('Mean Absolute Error (MAE)', fontsize=14)\n    ax.set_xlabel('Percentage of Labeled Data Used', fontsize=14)\n    ax.set_title(f'Supervised Baseline {target_name.upper()} Performance (MAE)', fontsize=16, weight='bold')\n    for bar in bars:\n        yval = bar.get_height()\n        ax.text(bar.get_x() + bar.get_width()/2.0, yval, f'{yval:.4f}',\n                ha='center', va='bottom', fontsize=12)\n    plt.show()\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-19T14:52:11.820803Z","iopub.execute_input":"2025-08-19T14:52:11.821337Z","iopub.status.idle":"2025-08-19T14:52:11.835961Z","shell.execute_reply.started":"2025-08-19T14:52:11.821318Z","shell.execute_reply":"2025-08-19T14:52:11.835398Z"}},"outputs":[],"execution_count":12},{"cell_type":"code","source":"def plot_true_vs_predicted_heatmap(true_labels, predictions, target_name, label_percentage):\n    plt.style.use('default')\n    fig, ax = plt.subplots(figsize=(8, 7))\n    mae = mean_absolute_error(true_labels, predictions)\n    min_val = min(np.min(true_labels), np.min(predictions))\n    max_val = max(np.max(true_labels), np.max(predictions))\n    bins = np.linspace(min_val, max_val, 51)\n    h = ax.hist2d(true_labels.flatten(), predictions.flatten(),\n                  bins=[bins, bins], cmap='gist_heat')\n    fig.colorbar(h[3], ax=ax, label='Frequency')\n    ax.plot([min_val, max_val], [min_val, max_val], 'b-', alpha=0.6, lw=2, label='Ideal Correlation')\n    mean_true, mean_pred = np.mean(true_labels), np.mean(predictions)\n    ax.scatter(mean_true, mean_pred, s=250, marker='+', color='white', lw=3, label='Mean Value', zorder=10)\n    ax.set_xlabel(f'True {target_name.capitalize()}', fontsize=12)\n    ax.set_ylabel(f'Predicted {target_name.capitalize()}', fontsize=12)\n    ax.set_title(f'Frequency Heatmap ({label_percentage*100:.0f}% Labels) for {target_name.upper()}\\nBest MAE: {mae:.4f}', fontsize=14)\n    ax.legend()\n    ax.set_aspect('equal', adjustable='box')\n    plt.tight_layout()\n    plt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-19T14:52:11.836822Z","iopub.execute_input":"2025-08-19T14:52:11.837056Z","iopub.status.idle":"2025-08-19T14:52:11.849511Z","shell.execute_reply.started":"2025-08-19T14:52:11.837039Z","shell.execute_reply":"2025-08-19T14:52:11.848859Z"}},"outputs":[],"execution_count":13},{"cell_type":"code","source":"if __name__ == '__main__':\n    if not os.path.exists(Config.DATA_FILE_PATH) or not os.path.exists(Config.PRETRAINED_ENCODER_PATH):\n        print(\"\\nExiting. Please check data file paths.\")\n    else:\n        # Identity transforms (exactly like the 3rd code)\n        train_transform = nn.Identity().to(Config.DEVICE)\n        test_transform = nn.Identity().to(Config.DEVICE)\n\n        label_percentages = [0.01, 0.10, 0.25, 0.50, 1.0]\n\n        for target_idx, target_name in enumerate([\"mass\", \"pt\"]):\n            print(\"\\n\" + \"=\"*50 + f\"\\nRUNNING SUPERVISED BASELINE FOR {target_name.upper()}\\n\" + \"=\"*50)\n\n            with h5py.File(Config.DATA_FILE_PATH, 'r') as f:\n                raw_labels = f['train_meta'][Config.EVAL_START_IDX:Config.EVAL_END_IDX, target_idx].reshape(-1, 1)\n\n            all_indices = list(range(len(raw_labels)))\n            tuning_pool_indices, test_indices, _, _ = train_test_split(\n                all_indices, raw_labels, test_size=0.5, random_state=42\n            )\n\n            scaler = StandardScaler()\n            scaler.fit(raw_labels[tuning_pool_indices])\n\n            full_eval_dataset = InMemoryJetDataset(\n                Config.DATA_FILE_PATH, Config.EVAL_START_IDX, Config.EVAL_END_IDX,\n                target_idx=target_idx, scaler=scaler\n            )\n            test_dataset = Subset(full_eval_dataset, test_indices)\n\n            results_mae, history_train_loss, history_val_mae = {}, {}, {}\n            final_true, final_pred = None, None\n\n            for percent in label_percentages:\n                best_mae, history, true_labels, preds = run_supervised_training(\n                    percent, tuning_pool_indices, full_eval_dataset, test_dataset, target_name, scaler\n                )\n                results_mae[f\"{int(percent*100)}%\"] = best_mae\n                history_train_loss[f\"{int(percent*100)}%\"] = history['train_loss']\n                history_val_mae[f\"{int(percent*100)}%\"] = history['val_mae']\n\n                final_true, final_pred = true_labels, preds  # keep last run (100%) for heatmap\n\n                # plot the heatmap for this run (matches 3rd code behavior)\n                plot_true_vs_predicted_heatmap(\n                    true_labels.flatten(),\n                    preds.flatten(),\n                    target_name,\n                    percent\n                )\n\n            plot_results(results_mae, target_name)\n            plot_metrics_vs_epochs(history_train_loss, history_val_mae, target_name)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-19T14:53:42.899891Z","iopub.execute_input":"2025-08-19T14:53:42.900141Z","iopub.status.idle":"2025-08-19T15:17:22.704317Z","shell.execute_reply.started":"2025-08-19T14:53:42.900123Z","shell.execute_reply":"2025-08-19T15:17:22.703549Z"}},"outputs":[{"name":"stdout","text":"\n==================================================\nRUNNING SUPERVISED BASELINE FOR MASS\n==================================================\n\n--- Running Supervised Baseline for MASS (1% Labels) ---\nTraining on 150 samples, testing on 15000 samples.\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/torch/optim/lr_scheduler.py:62: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Epoch [1/25] -> Train MAE Loss: 0.8592 | Val MAE: 20.4374\n  -> Val MAE improved (inf --> 20.4374).\nEpoch [2/25] -> Train MAE Loss: 0.7956 | Val MAE: 20.4238\n  -> Val MAE improved (20.4374 --> 20.4238).\nEpoch [3/25] -> Train MAE Loss: 0.8349 | Val MAE: 20.4062\n  -> Val MAE improved (20.4238 --> 20.4062).\nEpoch [4/25] -> Train MAE Loss: 0.8358 | Val MAE: 20.3963\n  -> Val MAE improved (20.4062 --> 20.3963).\nEpoch [5/25] -> Train MAE Loss: 0.8698 | Val MAE: 20.3844\n  -> Val MAE improved (20.3963 --> 20.3844).\nEpoch [6/25] -> Train MAE Loss: 0.8438 | Val MAE: 20.3760\n  -> Val MAE improved (20.3844 --> 20.3760).\nEpoch [7/25] -> Train MAE Loss: 0.7723 | Val MAE: 20.3668\n  -> Val MAE improved (20.3760 --> 20.3668).\nEpoch [8/25] -> Train MAE Loss: 0.6921 | Val MAE: 20.3507\n  -> Val MAE improved (20.3668 --> 20.3507).\nEpoch [9/25] -> Train MAE Loss: 0.8780 | Val MAE: 20.3478\n  -> Val MAE improved (20.3507 --> 20.3478).\nEpoch [10/25] -> Train MAE Loss: 0.8128 | Val MAE: 20.3441\n  -> Val MAE improved (20.3478 --> 20.3441).\nEpoch [11/25] -> Train MAE Loss: 0.8723 | Val MAE: 20.3294\n  -> Val MAE improved (20.3441 --> 20.3294).\nEpoch [12/25] -> Train MAE Loss: 0.8028 | Val MAE: 20.3165\n  -> Val MAE improved (20.3294 --> 20.3165).\nEpoch [13/25] -> Train MAE Loss: 0.8085 | Val MAE: 20.2928\n  -> Val MAE improved (20.3165 --> 20.2928).\nEpoch [14/25] -> Train MAE Loss: 0.8319 | Val MAE: 20.2910\n  -> Val MAE improved (20.2928 --> 20.2910).\nEpoch [15/25] -> Train MAE Loss: 0.8933 | Val MAE: 20.2893\n  -> Val MAE improved (20.2910 --> 20.2893).\nEpoch [16/25] -> Train MAE Loss: 0.8275 | Val MAE: 20.2828\n  -> Val MAE improved (20.2893 --> 20.2828).\nEpoch [17/25] -> Train MAE Loss: 0.8849 | Val MAE: 20.2738\n  -> Val MAE improved (20.2828 --> 20.2738).\nEpoch [18/25] -> Train MAE Loss: 0.8172 | Val MAE: 20.2714\n  -> Val MAE improved (20.2738 --> 20.2714).\nEpoch [19/25] -> Train MAE Loss: 0.8723 | Val MAE: 20.2682\n  -> Val MAE improved (20.2714 --> 20.2682).\nEpoch [20/25] -> Train MAE Loss: 0.8712 | Val MAE: 20.2599\n  -> Val MAE improved (20.2682 --> 20.2599).\nEpoch [21/25] -> Train MAE Loss: 0.8076 | Val MAE: 20.2583\n  -> Val MAE improved (20.2599 --> 20.2583).\nEpoch [22/25] -> Train MAE Loss: 0.7575 | Val MAE: 20.2460\n  -> Val MAE improved (20.2583 --> 20.2460).\nEpoch [23/25] -> Train MAE Loss: 0.7049 | Val MAE: 20.2485\n  -> EarlyStopping counter: 1 out of 5\nEpoch [24/25] -> Train MAE Loss: 0.8044 | Val MAE: 20.2439\n  -> Val MAE improved (20.2460 --> 20.2439).\nEpoch [25/25] -> Train MAE Loss: 0.8258 | Val MAE: 20.2417\n  -> Val MAE improved (20.2439 --> 20.2417).\n--- Loading best model with MAE: 20.2417 for plotting ---\nBest Test MAE for 1%: 20.2417\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x700 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"\n--- Running Supervised Baseline for MASS (10% Labels) ---\nTraining on 1500 samples, testing on 15000 samples.\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/torch/optim/lr_scheduler.py:62: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Epoch [1/25] -> Train MAE Loss: 0.7734 | Val MAE: 19.9621\n  -> Val MAE improved (inf --> 19.9621).\nEpoch [2/25] -> Train MAE Loss: 0.7711 | Val MAE: 19.9442\n  -> Val MAE improved (19.9621 --> 19.9442).\nEpoch [3/25] -> Train MAE Loss: 0.7730 | Val MAE: 19.9049\n  -> Val MAE improved (19.9442 --> 19.9049).\nEpoch [4/25] -> Train MAE Loss: 0.7717 | Val MAE: 19.8700\n  -> Val MAE improved (19.9049 --> 19.8700).\nEpoch [5/25] -> Train MAE Loss: 0.7672 | Val MAE: 19.8367\n  -> Val MAE improved (19.8700 --> 19.8367).\nEpoch [6/25] -> Train MAE Loss: 0.7675 | Val MAE: 19.8044\n  -> Val MAE improved (19.8367 --> 19.8044).\nEpoch [7/25] -> Train MAE Loss: 0.7668 | Val MAE: 19.7719\n  -> Val MAE improved (19.8044 --> 19.7719).\nEpoch [8/25] -> Train MAE Loss: 0.7665 | Val MAE: 19.7414\n  -> Val MAE improved (19.7719 --> 19.7414).\nEpoch [9/25] -> Train MAE Loss: 0.7639 | Val MAE: 19.7103\n  -> Val MAE improved (19.7414 --> 19.7103).\nEpoch [10/25] -> Train MAE Loss: 0.7625 | Val MAE: 19.6770\n  -> Val MAE improved (19.7103 --> 19.6770).\nEpoch [11/25] -> Train MAE Loss: 0.7583 | Val MAE: 19.6463\n  -> Val MAE improved (19.6770 --> 19.6463).\nEpoch [12/25] -> Train MAE Loss: 0.7559 | Val MAE: 19.6241\n  -> Val MAE improved (19.6463 --> 19.6241).\nEpoch [13/25] -> Train MAE Loss: 0.7606 | Val MAE: 19.5980\n  -> Val MAE improved (19.6241 --> 19.5980).\nEpoch [14/25] -> Train MAE Loss: 0.7559 | Val MAE: 19.5760\n  -> Val MAE improved (19.5980 --> 19.5760).\nEpoch [15/25] -> Train MAE Loss: 0.7617 | Val MAE: 19.5551\n  -> Val MAE improved (19.5760 --> 19.5551).\nEpoch [16/25] -> Train MAE Loss: 0.7558 | Val MAE: 19.5326\n  -> Val MAE improved (19.5551 --> 19.5326).\nEpoch [17/25] -> Train MAE Loss: 0.7567 | Val MAE: 19.5118\n  -> Val MAE improved (19.5326 --> 19.5118).\nEpoch [18/25] -> Train MAE Loss: 0.7521 | Val MAE: 19.4885\n  -> Val MAE improved (19.5118 --> 19.4885).\nEpoch [19/25] -> Train MAE Loss: 0.7583 | Val MAE: 19.4693\n  -> Val MAE improved (19.4885 --> 19.4693).\nEpoch [20/25] -> Train MAE Loss: 0.7483 | Val MAE: 19.4488\n  -> Val MAE improved (19.4693 --> 19.4488).\nEpoch [21/25] -> Train MAE Loss: 0.7543 | Val MAE: 19.4270\n  -> Val MAE improved (19.4488 --> 19.4270).\nEpoch [22/25] -> Train MAE Loss: 0.7537 | Val MAE: 19.4109\n  -> Val MAE improved (19.4270 --> 19.4109).\nEpoch [23/25] -> Train MAE Loss: 0.7476 | Val MAE: 19.3942\n  -> Val MAE improved (19.4109 --> 19.3942).\nEpoch [24/25] -> Train MAE Loss: 0.7509 | Val MAE: 19.3718\n  -> Val MAE improved (19.3942 --> 19.3718).\nEpoch [25/25] -> Train MAE Loss: 0.7478 | Val MAE: 19.3553\n  -> Val MAE improved (19.3718 --> 19.3553).\n--- Loading best model with MAE: 19.3553 for plotting ---\nBest Test MAE for 10%: 19.3553\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x700 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"\n--- Running Supervised Baseline for MASS (25% Labels) ---\nTraining on 3750 samples, testing on 15000 samples.\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/torch/optim/lr_scheduler.py:62: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Epoch [1/25] -> Train MAE Loss: 0.7803 | Val MAE: 19.9665\n  -> Val MAE improved (inf --> 19.9665).\nEpoch [2/25] -> Train MAE Loss: 0.7607 | Val MAE: 19.8162\n  -> Val MAE improved (19.9665 --> 19.8162).\nEpoch [3/25] -> Train MAE Loss: 0.7591 | Val MAE: 19.7023\n  -> Val MAE improved (19.8162 --> 19.7023).\nEpoch [4/25] -> Train MAE Loss: 0.7556 | Val MAE: 19.6317\n  -> Val MAE improved (19.7023 --> 19.6317).\nEpoch [5/25] -> Train MAE Loss: 0.7532 | Val MAE: 19.5577\n  -> Val MAE improved (19.6317 --> 19.5577).\nEpoch [6/25] -> Train MAE Loss: 0.7449 | Val MAE: 19.5078\n  -> Val MAE improved (19.5577 --> 19.5078).\nEpoch [7/25] -> Train MAE Loss: 0.7440 | Val MAE: 19.4558\n  -> Val MAE improved (19.5078 --> 19.4558).\nEpoch [8/25] -> Train MAE Loss: 0.7399 | Val MAE: 19.4139\n  -> Val MAE improved (19.4558 --> 19.4139).\nEpoch [9/25] -> Train MAE Loss: 0.7394 | Val MAE: 19.3697\n  -> Val MAE improved (19.4139 --> 19.3697).\nEpoch [10/25] -> Train MAE Loss: 0.7448 | Val MAE: 19.3255\n  -> Val MAE improved (19.3697 --> 19.3255).\nEpoch [11/25] -> Train MAE Loss: 0.7384 | Val MAE: 19.2883\n  -> Val MAE improved (19.3255 --> 19.2883).\nEpoch [12/25] -> Train MAE Loss: 0.7421 | Val MAE: 19.2432\n  -> Val MAE improved (19.2883 --> 19.2432).\nEpoch [13/25] -> Train MAE Loss: 0.7323 | Val MAE: 19.2197\n  -> Val MAE improved (19.2432 --> 19.2197).\nEpoch [14/25] -> Train MAE Loss: 0.7316 | Val MAE: 19.1817\n  -> Val MAE improved (19.2197 --> 19.1817).\nEpoch [15/25] -> Train MAE Loss: 0.7420 | Val MAE: 19.1554\n  -> Val MAE improved (19.1817 --> 19.1554).\nEpoch [16/25] -> Train MAE Loss: 0.7307 | Val MAE: 19.1269\n  -> Val MAE improved (19.1554 --> 19.1269).\nEpoch [17/25] -> Train MAE Loss: 0.7296 | Val MAE: 19.0947\n  -> Val MAE improved (19.1269 --> 19.0947).\nEpoch [18/25] -> Train MAE Loss: 0.7288 | Val MAE: 19.0775\n  -> Val MAE improved (19.0947 --> 19.0775).\nEpoch [19/25] -> Train MAE Loss: 0.7287 | Val MAE: 19.0586\n  -> Val MAE improved (19.0775 --> 19.0586).\nEpoch [20/25] -> Train MAE Loss: 0.7288 | Val MAE: 19.0340\n  -> Val MAE improved (19.0586 --> 19.0340).\nEpoch [21/25] -> Train MAE Loss: 0.7340 | Val MAE: 19.0145\n  -> Val MAE improved (19.0340 --> 19.0145).\nEpoch [22/25] -> Train MAE Loss: 0.7275 | Val MAE: 18.9972\n  -> Val MAE improved (19.0145 --> 18.9972).\nEpoch [23/25] -> Train MAE Loss: 0.7299 | Val MAE: 18.9748\n  -> Val MAE improved (18.9972 --> 18.9748).\nEpoch [24/25] -> Train MAE Loss: 0.7284 | Val MAE: 18.9576\n  -> Val MAE improved (18.9748 --> 18.9576).\nEpoch [25/25] -> Train MAE Loss: 0.7305 | Val MAE: 18.9364\n  -> Val MAE improved (18.9576 --> 18.9364).\n--- Loading best model with MAE: 18.9364 for plotting ---\nBest Test MAE for 25%: 18.9364\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x700 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"\n--- Running Supervised Baseline for MASS (50% Labels) ---\nTraining on 7500 samples, testing on 15000 samples.\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/torch/optim/lr_scheduler.py:62: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Epoch [1/25] -> Train MAE Loss: 0.7866 | Val MAE: 20.1056\n  -> Val MAE improved (inf --> 20.1056).\nEpoch [2/25] -> Train MAE Loss: 0.7739 | Val MAE: 19.8894\n  -> Val MAE improved (20.1056 --> 19.8894).\nEpoch [3/25] -> Train MAE Loss: 0.7661 | Val MAE: 19.7276\n  -> Val MAE improved (19.8894 --> 19.7276).\nEpoch [4/25] -> Train MAE Loss: 0.7610 | Val MAE: 19.5980\n  -> Val MAE improved (19.7276 --> 19.5980).\nEpoch [5/25] -> Train MAE Loss: 0.7547 | Val MAE: 19.4836\n  -> Val MAE improved (19.5980 --> 19.4836).\nEpoch [6/25] -> Train MAE Loss: 0.7511 | Val MAE: 19.3788\n  -> Val MAE improved (19.4836 --> 19.3788).\nEpoch [7/25] -> Train MAE Loss: 0.7476 | Val MAE: 19.2935\n  -> Val MAE improved (19.3788 --> 19.2935).\nEpoch [8/25] -> Train MAE Loss: 0.7438 | Val MAE: 19.2100\n  -> Val MAE improved (19.2935 --> 19.2100).\nEpoch [9/25] -> Train MAE Loss: 0.7406 | Val MAE: 19.1430\n  -> Val MAE improved (19.2100 --> 19.1430).\nEpoch [10/25] -> Train MAE Loss: 0.7381 | Val MAE: 19.0838\n  -> Val MAE improved (19.1430 --> 19.0838).\nEpoch [11/25] -> Train MAE Loss: 0.7397 | Val MAE: 19.0321\n  -> Val MAE improved (19.0838 --> 19.0321).\nEpoch [12/25] -> Train MAE Loss: 0.7360 | Val MAE: 18.9866\n  -> Val MAE improved (19.0321 --> 18.9866).\nEpoch [13/25] -> Train MAE Loss: 0.7345 | Val MAE: 18.9490\n  -> Val MAE improved (18.9866 --> 18.9490).\nEpoch [14/25] -> Train MAE Loss: 0.7333 | Val MAE: 18.9095\n  -> Val MAE improved (18.9490 --> 18.9095).\nEpoch [15/25] -> Train MAE Loss: 0.7333 | Val MAE: 18.8764\n  -> Val MAE improved (18.9095 --> 18.8764).\nEpoch [16/25] -> Train MAE Loss: 0.7333 | Val MAE: 18.8475\n  -> Val MAE improved (18.8764 --> 18.8475).\nEpoch [17/25] -> Train MAE Loss: 0.7308 | Val MAE: 18.8220\n  -> Val MAE improved (18.8475 --> 18.8220).\nEpoch [18/25] -> Train MAE Loss: 0.7281 | Val MAE: 18.7955\n  -> Val MAE improved (18.8220 --> 18.7955).\nEpoch [19/25] -> Train MAE Loss: 0.7293 | Val MAE: 18.7771\n  -> Val MAE improved (18.7955 --> 18.7771).\nEpoch [20/25] -> Train MAE Loss: 0.7290 | Val MAE: 18.7645\n  -> Val MAE improved (18.7771 --> 18.7645).\nEpoch [21/25] -> Train MAE Loss: 0.7276 | Val MAE: 18.7385\n  -> Val MAE improved (18.7645 --> 18.7385).\nEpoch [22/25] -> Train MAE Loss: 0.7281 | Val MAE: 18.7370\n  -> Val MAE improved (18.7385 --> 18.7370).\nEpoch [23/25] -> Train MAE Loss: 0.7253 | Val MAE: 18.7195\n  -> Val MAE improved (18.7370 --> 18.7195).\nEpoch [24/25] -> Train MAE Loss: 0.7264 | Val MAE: 18.7005\n  -> Val MAE improved (18.7195 --> 18.7005).\nEpoch [25/25] -> Train MAE Loss: 0.7257 | Val MAE: 18.6915\n  -> Val MAE improved (18.7005 --> 18.6915).\n--- Loading best model with MAE: 18.6915 for plotting ---\nBest Test MAE for 50%: 18.6915\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x700 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"\n--- Running Supervised Baseline for MASS (100% Labels) ---\nTraining on 15000 samples, testing on 15000 samples.\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/torch/optim/lr_scheduler.py:62: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Epoch [1/25] -> Train MAE Loss: 0.7752 | Val MAE: 19.9181\n  -> Val MAE improved (inf --> 19.9181).\nEpoch [2/25] -> Train MAE Loss: 0.7593 | Val MAE: 19.6187\n  -> Val MAE improved (19.9181 --> 19.6187).\nEpoch [3/25] -> Train MAE Loss: 0.7496 | Val MAE: 19.4014\n  -> Val MAE improved (19.6187 --> 19.4014).\nEpoch [4/25] -> Train MAE Loss: 0.7416 | Val MAE: 19.2332\n  -> Val MAE improved (19.4014 --> 19.2332).\nEpoch [5/25] -> Train MAE Loss: 0.7369 | Val MAE: 19.1027\n  -> Val MAE improved (19.2332 --> 19.1027).\nEpoch [6/25] -> Train MAE Loss: 0.7332 | Val MAE: 18.9908\n  -> Val MAE improved (19.1027 --> 18.9908).\nEpoch [7/25] -> Train MAE Loss: 0.7305 | Val MAE: 18.9261\n  -> Val MAE improved (18.9908 --> 18.9261).\nEpoch [8/25] -> Train MAE Loss: 0.7280 | Val MAE: 18.8477\n  -> Val MAE improved (18.9261 --> 18.8477).\nEpoch [9/25] -> Train MAE Loss: 0.7273 | Val MAE: 18.8076\n  -> Val MAE improved (18.8477 --> 18.8076).\nEpoch [10/25] -> Train MAE Loss: 0.7259 | Val MAE: 18.7670\n  -> Val MAE improved (18.8076 --> 18.7670).\nEpoch [11/25] -> Train MAE Loss: 0.7257 | Val MAE: 18.7375\n  -> Val MAE improved (18.7670 --> 18.7375).\nEpoch [12/25] -> Train MAE Loss: 0.7242 | Val MAE: 18.7108\n  -> Val MAE improved (18.7375 --> 18.7108).\nEpoch [13/25] -> Train MAE Loss: 0.7246 | Val MAE: 18.6899\n  -> Val MAE improved (18.7108 --> 18.6899).\nEpoch [14/25] -> Train MAE Loss: 0.7226 | Val MAE: 18.6765\n  -> Val MAE improved (18.6899 --> 18.6765).\nEpoch [15/25] -> Train MAE Loss: 0.7225 | Val MAE: 18.6552\n  -> Val MAE improved (18.6765 --> 18.6552).\nEpoch [16/25] -> Train MAE Loss: 0.7215 | Val MAE: 18.6418\n  -> Val MAE improved (18.6552 --> 18.6418).\nEpoch [17/25] -> Train MAE Loss: 0.7205 | Val MAE: 18.6228\n  -> Val MAE improved (18.6418 --> 18.6228).\nEpoch [18/25] -> Train MAE Loss: 0.7232 | Val MAE: 18.6220\n  -> EarlyStopping counter: 1 out of 5\nEpoch [19/25] -> Train MAE Loss: 0.7188 | Val MAE: 18.6057\n  -> Val MAE improved (18.6228 --> 18.6057).\nEpoch [20/25] -> Train MAE Loss: 0.7204 | Val MAE: 18.6002\n  -> Val MAE improved (18.6057 --> 18.6002).\nEpoch [21/25] -> Train MAE Loss: 0.7194 | Val MAE: 18.5827\n  -> Val MAE improved (18.6002 --> 18.5827).\nEpoch [22/25] -> Train MAE Loss: 0.7205 | Val MAE: 18.5844\n  -> EarlyStopping counter: 1 out of 5\nEpoch [23/25] -> Train MAE Loss: 0.7207 | Val MAE: 18.5716\n  -> Val MAE improved (18.5827 --> 18.5716).\nEpoch [24/25] -> Train MAE Loss: 0.7179 | Val MAE: 18.5619\n  -> Val MAE improved (18.5716 --> 18.5619).\nEpoch [25/25] -> Train MAE Loss: 0.7175 | Val MAE: 18.5610\n  -> EarlyStopping counter: 1 out of 5\n--- Loading best model with MAE: 18.5610 for plotting ---\nBest Test MAE for 100%: 18.5610\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x700 with 2 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\n"},"metadata":{}},{"name":"stdout","text":"\n--- Final Results for MASS ---\nBest MAE with 1% labels: 20.2417\nBest MAE with 10% labels: 19.3553\nBest MAE with 25% labels: 18.9364\nBest MAE with 50% labels: 18.6915\nBest MAE with 100% labels: 18.5610\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x600 with 1 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{}},{"name":"stdout","text":"\n==================================================\nRUNNING SUPERVISED BASELINE FOR PT\n==================================================\n\n--- Running Supervised Baseline for PT (1% Labels) ---\nTraining on 150 samples, testing on 15000 samples.\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/torch/optim/lr_scheduler.py:62: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Epoch [1/25] -> Train MAE Loss: 0.9111 | Val MAE: 5.0148\n  -> Val MAE improved (inf --> 5.0148).\nEpoch [2/25] -> Train MAE Loss: 0.8184 | Val MAE: 5.0017\n  -> Val MAE improved (5.0148 --> 5.0017).\nEpoch [3/25] -> Train MAE Loss: 0.9025 | Val MAE: 4.9909\n  -> Val MAE improved (5.0017 --> 4.9909).\nEpoch [4/25] -> Train MAE Loss: 0.8749 | Val MAE: 4.9823\n  -> Val MAE improved (4.9909 --> 4.9823).\nEpoch [5/25] -> Train MAE Loss: 0.8268 | Val MAE: 4.9757\n  -> Val MAE improved (4.9823 --> 4.9757).\nEpoch [6/25] -> Train MAE Loss: 0.9393 | Val MAE: 4.9688\n  -> Val MAE improved (4.9757 --> 4.9688).\nEpoch [7/25] -> Train MAE Loss: 0.7967 | Val MAE: 4.9631\n  -> Val MAE improved (4.9688 --> 4.9631).\nEpoch [8/25] -> Train MAE Loss: 0.9412 | Val MAE: 4.9576\n  -> Val MAE improved (4.9631 --> 4.9576).\nEpoch [9/25] -> Train MAE Loss: 0.8922 | Val MAE: 4.9538\n  -> Val MAE improved (4.9576 --> 4.9538).\nEpoch [10/25] -> Train MAE Loss: 0.8938 | Val MAE: 4.9476\n  -> Val MAE improved (4.9538 --> 4.9476).\nEpoch [11/25] -> Train MAE Loss: 0.9342 | Val MAE: 4.9445\n  -> Val MAE improved (4.9476 --> 4.9445).\nEpoch [12/25] -> Train MAE Loss: 0.8346 | Val MAE: 4.9393\n  -> Val MAE improved (4.9445 --> 4.9393).\nEpoch [13/25] -> Train MAE Loss: 0.8689 | Val MAE: 4.9351\n  -> Val MAE improved (4.9393 --> 4.9351).\nEpoch [14/25] -> Train MAE Loss: 0.8975 | Val MAE: 4.9310\n  -> Val MAE improved (4.9351 --> 4.9310).\nEpoch [15/25] -> Train MAE Loss: 0.8854 | Val MAE: 4.9271\n  -> Val MAE improved (4.9310 --> 4.9271).\nEpoch [16/25] -> Train MAE Loss: 0.8397 | Val MAE: 4.9196\n  -> Val MAE improved (4.9271 --> 4.9196).\nEpoch [17/25] -> Train MAE Loss: 0.8729 | Val MAE: 4.9190\n  -> EarlyStopping counter: 1 out of 5\nEpoch [18/25] -> Train MAE Loss: 0.8583 | Val MAE: 4.9145\n  -> Val MAE improved (4.9196 --> 4.9145).\nEpoch [19/25] -> Train MAE Loss: 0.9447 | Val MAE: 4.9112\n  -> Val MAE improved (4.9145 --> 4.9112).\nEpoch [20/25] -> Train MAE Loss: 0.9980 | Val MAE: 4.9054\n  -> Val MAE improved (4.9112 --> 4.9054).\nEpoch [21/25] -> Train MAE Loss: 0.8317 | Val MAE: 4.9018\n  -> Val MAE improved (4.9054 --> 4.9018).\nEpoch [22/25] -> Train MAE Loss: 0.8541 | Val MAE: 4.8985\n  -> Val MAE improved (4.9018 --> 4.8985).\nEpoch [23/25] -> Train MAE Loss: 0.8310 | Val MAE: 4.8942\n  -> Val MAE improved (4.8985 --> 4.8942).\nEpoch [24/25] -> Train MAE Loss: 0.9856 | Val MAE: 4.8927\n  -> Val MAE improved (4.8942 --> 4.8927).\nEpoch [25/25] -> Train MAE Loss: 0.8265 | Val MAE: 4.8885\n  -> Val MAE improved (4.8927 --> 4.8885).\n--- Loading best model with MAE: 4.8885 for plotting ---\nBest Test MAE for 1%: 4.8885\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x700 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"\n--- Running Supervised Baseline for PT (10% Labels) ---\nTraining on 1500 samples, testing on 15000 samples.\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/torch/optim/lr_scheduler.py:62: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Epoch [1/25] -> Train MAE Loss: 0.7911 | Val MAE: 4.8926\n  -> Val MAE improved (inf --> 4.8926).\nEpoch [2/25] -> Train MAE Loss: 0.7869 | Val MAE: 4.8726\n  -> Val MAE improved (4.8926 --> 4.8726).\nEpoch [3/25] -> Train MAE Loss: 0.7905 | Val MAE: 4.8559\n  -> Val MAE improved (4.8726 --> 4.8559).\nEpoch [4/25] -> Train MAE Loss: 0.7814 | Val MAE: 4.8391\n  -> Val MAE improved (4.8559 --> 4.8391).\nEpoch [5/25] -> Train MAE Loss: 0.7762 | Val MAE: 4.8230\n  -> Val MAE improved (4.8391 --> 4.8230).\nEpoch [6/25] -> Train MAE Loss: 0.7739 | Val MAE: 4.8089\n  -> Val MAE improved (4.8230 --> 4.8089).\nEpoch [7/25] -> Train MAE Loss: 0.7807 | Val MAE: 4.7934\n  -> Val MAE improved (4.8089 --> 4.7934).\nEpoch [8/25] -> Train MAE Loss: 0.7750 | Val MAE: 4.7791\n  -> Val MAE improved (4.7934 --> 4.7791).\nEpoch [9/25] -> Train MAE Loss: 0.7664 | Val MAE: 4.7648\n  -> Val MAE improved (4.7791 --> 4.7648).\nEpoch [10/25] -> Train MAE Loss: 0.7680 | Val MAE: 4.7507\n  -> Val MAE improved (4.7648 --> 4.7507).\nEpoch [11/25] -> Train MAE Loss: 0.7659 | Val MAE: 4.7368\n  -> Val MAE improved (4.7507 --> 4.7368).\nEpoch [12/25] -> Train MAE Loss: 0.7666 | Val MAE: 4.7244\n  -> Val MAE improved (4.7368 --> 4.7244).\nEpoch [13/25] -> Train MAE Loss: 0.7626 | Val MAE: 4.7104\n  -> Val MAE improved (4.7244 --> 4.7104).\nEpoch [14/25] -> Train MAE Loss: 0.7599 | Val MAE: 4.6971\n  -> Val MAE improved (4.7104 --> 4.6971).\nEpoch [15/25] -> Train MAE Loss: 0.7511 | Val MAE: 4.6857\n  -> Val MAE improved (4.6971 --> 4.6857).\nEpoch [16/25] -> Train MAE Loss: 0.7516 | Val MAE: 4.6739\n  -> Val MAE improved (4.6857 --> 4.6739).\nEpoch [17/25] -> Train MAE Loss: 0.7521 | Val MAE: 4.6624\n  -> Val MAE improved (4.6739 --> 4.6624).\nEpoch [18/25] -> Train MAE Loss: 0.7467 | Val MAE: 4.6495\n  -> Val MAE improved (4.6624 --> 4.6495).\nEpoch [19/25] -> Train MAE Loss: 0.7493 | Val MAE: 4.6387\n  -> Val MAE improved (4.6495 --> 4.6387).\nEpoch [20/25] -> Train MAE Loss: 0.7445 | Val MAE: 4.6279\n  -> Val MAE improved (4.6387 --> 4.6279).\nEpoch [21/25] -> Train MAE Loss: 0.7451 | Val MAE: 4.6173\n  -> Val MAE improved (4.6279 --> 4.6173).\nEpoch [22/25] -> Train MAE Loss: 0.7426 | Val MAE: 4.6057\n  -> Val MAE improved (4.6173 --> 4.6057).\nEpoch [23/25] -> Train MAE Loss: 0.7461 | Val MAE: 4.5969\n  -> Val MAE improved (4.6057 --> 4.5969).\nEpoch [24/25] -> Train MAE Loss: 0.7377 | Val MAE: 4.5868\n  -> Val MAE improved (4.5969 --> 4.5868).\nEpoch [25/25] -> Train MAE Loss: 0.7413 | Val MAE: 4.5764\n  -> Val MAE improved (4.5868 --> 4.5764).\n--- Loading best model with MAE: 4.5764 for plotting ---\nBest Test MAE for 10%: 4.5764\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x700 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"\n--- Running Supervised Baseline for PT (25% Labels) ---\nTraining on 3750 samples, testing on 15000 samples.\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/torch/optim/lr_scheduler.py:62: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Epoch [1/25] -> Train MAE Loss: 0.7690 | Val MAE: 4.8624\n  -> Val MAE improved (inf --> 4.8624).\nEpoch [2/25] -> Train MAE Loss: 0.7662 | Val MAE: 4.8231\n  -> Val MAE improved (4.8624 --> 4.8231).\nEpoch [3/25] -> Train MAE Loss: 0.7552 | Val MAE: 4.7848\n  -> Val MAE improved (4.8231 --> 4.7848).\nEpoch [4/25] -> Train MAE Loss: 0.7533 | Val MAE: 4.7459\n  -> Val MAE improved (4.7848 --> 4.7459).\nEpoch [5/25] -> Train MAE Loss: 0.7450 | Val MAE: 4.7158\n  -> Val MAE improved (4.7459 --> 4.7158).\nEpoch [6/25] -> Train MAE Loss: 0.7397 | Val MAE: 4.6888\n  -> Val MAE improved (4.7158 --> 4.6888).\nEpoch [7/25] -> Train MAE Loss: 0.7377 | Val MAE: 4.6581\n  -> Val MAE improved (4.6888 --> 4.6581).\nEpoch [8/25] -> Train MAE Loss: 0.7382 | Val MAE: 4.6319\n  -> Val MAE improved (4.6581 --> 4.6319).\nEpoch [9/25] -> Train MAE Loss: 0.7281 | Val MAE: 4.6063\n  -> Val MAE improved (4.6319 --> 4.6063).\nEpoch [10/25] -> Train MAE Loss: 0.7235 | Val MAE: 4.5832\n  -> Val MAE improved (4.6063 --> 4.5832).\nEpoch [11/25] -> Train MAE Loss: 0.7230 | Val MAE: 4.5566\n  -> Val MAE improved (4.5832 --> 4.5566).\nEpoch [12/25] -> Train MAE Loss: 0.7193 | Val MAE: 4.5368\n  -> Val MAE improved (4.5566 --> 4.5368).\nEpoch [13/25] -> Train MAE Loss: 0.7132 | Val MAE: 4.5165\n  -> Val MAE improved (4.5368 --> 4.5165).\nEpoch [14/25] -> Train MAE Loss: 0.7139 | Val MAE: 4.4955\n  -> Val MAE improved (4.5165 --> 4.4955).\nEpoch [15/25] -> Train MAE Loss: 0.7089 | Val MAE: 4.4801\n  -> Val MAE improved (4.4955 --> 4.4801).\nEpoch [16/25] -> Train MAE Loss: 0.7107 | Val MAE: 4.4621\n  -> Val MAE improved (4.4801 --> 4.4621).\nEpoch [17/25] -> Train MAE Loss: 0.7049 | Val MAE: 4.4442\n  -> Val MAE improved (4.4621 --> 4.4442).\nEpoch [18/25] -> Train MAE Loss: 0.7014 | Val MAE: 4.4291\n  -> Val MAE improved (4.4442 --> 4.4291).\nEpoch [19/25] -> Train MAE Loss: 0.7006 | Val MAE: 4.4155\n  -> Val MAE improved (4.4291 --> 4.4155).\nEpoch [20/25] -> Train MAE Loss: 0.7012 | Val MAE: 4.4014\n  -> Val MAE improved (4.4155 --> 4.4014).\nEpoch [21/25] -> Train MAE Loss: 0.6948 | Val MAE: 4.3882\n  -> Val MAE improved (4.4014 --> 4.3882).\nEpoch [22/25] -> Train MAE Loss: 0.6949 | Val MAE: 4.3750\n  -> Val MAE improved (4.3882 --> 4.3750).\nEpoch [23/25] -> Train MAE Loss: 0.6929 | Val MAE: 4.3621\n  -> Val MAE improved (4.3750 --> 4.3621).\nEpoch [24/25] -> Train MAE Loss: 0.6914 | Val MAE: 4.3484\n  -> Val MAE improved (4.3621 --> 4.3484).\nEpoch [25/25] -> Train MAE Loss: 0.6895 | Val MAE: 4.3403\n  -> Val MAE improved (4.3484 --> 4.3403).\n--- Loading best model with MAE: 4.3403 for plotting ---\nBest Test MAE for 25%: 4.3403\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x700 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"\n--- Running Supervised Baseline for PT (50% Labels) ---\nTraining on 7500 samples, testing on 15000 samples.\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/torch/optim/lr_scheduler.py:62: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Epoch [1/25] -> Train MAE Loss: 0.7494 | Val MAE: 4.7345\n  -> Val MAE improved (inf --> 4.7345).\nEpoch [2/25] -> Train MAE Loss: 0.7422 | Val MAE: 4.6713\n  -> Val MAE improved (4.7345 --> 4.6713).\nEpoch [3/25] -> Train MAE Loss: 0.7342 | Val MAE: 4.6178\n  -> Val MAE improved (4.6713 --> 4.6178).\nEpoch [4/25] -> Train MAE Loss: 0.7242 | Val MAE: 4.5654\n  -> Val MAE improved (4.6178 --> 4.5654).\nEpoch [5/25] -> Train MAE Loss: 0.7179 | Val MAE: 4.5194\n  -> Val MAE improved (4.5654 --> 4.5194).\nEpoch [6/25] -> Train MAE Loss: 0.7103 | Val MAE: 4.4771\n  -> Val MAE improved (4.5194 --> 4.4771).\nEpoch [7/25] -> Train MAE Loss: 0.7038 | Val MAE: 4.4417\n  -> Val MAE improved (4.4771 --> 4.4417).\nEpoch [8/25] -> Train MAE Loss: 0.7025 | Val MAE: 4.4055\n  -> Val MAE improved (4.4417 --> 4.4055).\nEpoch [9/25] -> Train MAE Loss: 0.6954 | Val MAE: 4.3780\n  -> Val MAE improved (4.4055 --> 4.3780).\nEpoch [10/25] -> Train MAE Loss: 0.6943 | Val MAE: 4.3483\n  -> Val MAE improved (4.3780 --> 4.3483).\nEpoch [11/25] -> Train MAE Loss: 0.6882 | Val MAE: 4.3240\n  -> Val MAE improved (4.3483 --> 4.3240).\nEpoch [12/25] -> Train MAE Loss: 0.6851 | Val MAE: 4.3050\n  -> Val MAE improved (4.3240 --> 4.3050).\nEpoch [13/25] -> Train MAE Loss: 0.6830 | Val MAE: 4.2836\n  -> Val MAE improved (4.3050 --> 4.2836).\nEpoch [14/25] -> Train MAE Loss: 0.6779 | Val MAE: 4.2668\n  -> Val MAE improved (4.2836 --> 4.2668).\nEpoch [15/25] -> Train MAE Loss: 0.6789 | Val MAE: 4.2494\n  -> Val MAE improved (4.2668 --> 4.2494).\nEpoch [16/25] -> Train MAE Loss: 0.6745 | Val MAE: 4.2348\n  -> Val MAE improved (4.2494 --> 4.2348).\nEpoch [17/25] -> Train MAE Loss: 0.6748 | Val MAE: 4.2215\n  -> Val MAE improved (4.2348 --> 4.2215).\nEpoch [18/25] -> Train MAE Loss: 0.6719 | Val MAE: 4.2084\n  -> Val MAE improved (4.2215 --> 4.2084).\nEpoch [19/25] -> Train MAE Loss: 0.6728 | Val MAE: 4.1960\n  -> Val MAE improved (4.2084 --> 4.1960).\nEpoch [20/25] -> Train MAE Loss: 0.6697 | Val MAE: 4.1865\n  -> Val MAE improved (4.1960 --> 4.1865).\nEpoch [21/25] -> Train MAE Loss: 0.6676 | Val MAE: 4.1781\n  -> Val MAE improved (4.1865 --> 4.1781).\nEpoch [22/25] -> Train MAE Loss: 0.6655 | Val MAE: 4.1726\n  -> Val MAE improved (4.1781 --> 4.1726).\nEpoch [23/25] -> Train MAE Loss: 0.6669 | Val MAE: 4.1632\n  -> Val MAE improved (4.1726 --> 4.1632).\nEpoch [24/25] -> Train MAE Loss: 0.6630 | Val MAE: 4.1575\n  -> Val MAE improved (4.1632 --> 4.1575).\nEpoch [25/25] -> Train MAE Loss: 0.6657 | Val MAE: 4.1481\n  -> Val MAE improved (4.1575 --> 4.1481).\n--- Loading best model with MAE: 4.1481 for plotting ---\nBest Test MAE for 50%: 4.1481\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x700 with 2 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\n"},"metadata":{}},{"name":"stdout","text":"\n--- Running Supervised Baseline for PT (100% Labels) ---\nTraining on 15000 samples, testing on 15000 samples.\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/torch/optim/lr_scheduler.py:62: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Epoch [1/25] -> Train MAE Loss: 0.7517 | Val MAE: 4.7302\n  -> Val MAE improved (inf --> 4.7302).\nEpoch [2/25] -> Train MAE Loss: 0.7314 | Val MAE: 4.6128\n  -> Val MAE improved (4.7302 --> 4.6128).\nEpoch [3/25] -> Train MAE Loss: 0.7170 | Val MAE: 4.5152\n  -> Val MAE improved (4.6128 --> 4.5152).\nEpoch [4/25] -> Train MAE Loss: 0.7035 | Val MAE: 4.4416\n  -> Val MAE improved (4.5152 --> 4.4416).\nEpoch [5/25] -> Train MAE Loss: 0.6952 | Val MAE: 4.3785\n  -> Val MAE improved (4.4416 --> 4.3785).\nEpoch [6/25] -> Train MAE Loss: 0.6873 | Val MAE: 4.3346\n  -> Val MAE improved (4.3785 --> 4.3346).\nEpoch [7/25] -> Train MAE Loss: 0.6794 | Val MAE: 4.2859\n  -> Val MAE improved (4.3346 --> 4.2859).\nEpoch [8/25] -> Train MAE Loss: 0.6743 | Val MAE: 4.2545\n  -> Val MAE improved (4.2859 --> 4.2545).\nEpoch [9/25] -> Train MAE Loss: 0.6701 | Val MAE: 4.2253\n  -> Val MAE improved (4.2545 --> 4.2253).\nEpoch [10/25] -> Train MAE Loss: 0.6668 | Val MAE: 4.2023\n  -> Val MAE improved (4.2253 --> 4.2023).\nEpoch [11/25] -> Train MAE Loss: 0.6656 | Val MAE: 4.1860\n  -> Val MAE improved (4.2023 --> 4.1860).\nEpoch [12/25] -> Train MAE Loss: 0.6618 | Val MAE: 4.1717\n  -> Val MAE improved (4.1860 --> 4.1717).\nEpoch [13/25] -> Train MAE Loss: 0.6631 | Val MAE: 4.1535\n  -> Val MAE improved (4.1717 --> 4.1535).\nEpoch [14/25] -> Train MAE Loss: 0.6579 | Val MAE: 4.1451\n  -> Val MAE improved (4.1535 --> 4.1451).\nEpoch [15/25] -> Train MAE Loss: 0.6605 | Val MAE: 4.1367\n  -> Val MAE improved (4.1451 --> 4.1367).\nEpoch [16/25] -> Train MAE Loss: 0.6574 | Val MAE: 4.1283\n  -> Val MAE improved (4.1367 --> 4.1283).\nEpoch [17/25] -> Train MAE Loss: 0.6572 | Val MAE: 4.1214\n  -> Val MAE improved (4.1283 --> 4.1214).\nEpoch [18/25] -> Train MAE Loss: 0.6564 | Val MAE: 4.1116\n  -> Val MAE improved (4.1214 --> 4.1116).\nEpoch [19/25] -> Train MAE Loss: 0.6551 | Val MAE: 4.1067\n  -> Val MAE improved (4.1116 --> 4.1067).\nEpoch [20/25] -> Train MAE Loss: 0.6563 | Val MAE: 4.1024\n  -> Val MAE improved (4.1067 --> 4.1024).\nEpoch [21/25] -> Train MAE Loss: 0.6566 | Val MAE: 4.0977\n  -> Val MAE improved (4.1024 --> 4.0977).\nEpoch [22/25] -> Train MAE Loss: 0.6533 | Val MAE: 4.0953\n  -> Val MAE improved (4.0977 --> 4.0953).\nEpoch [23/25] -> Train MAE Loss: 0.6536 | Val MAE: 4.0924\n  -> Val MAE improved (4.0953 --> 4.0924).\nEpoch [24/25] -> Train MAE Loss: 0.6525 | Val MAE: 4.0879\n  -> Val MAE improved (4.0924 --> 4.0879).\nEpoch [25/25] -> Train MAE Loss: 0.6518 | Val MAE: 4.0841\n  -> Val MAE improved (4.0879 --> 4.0841).\n--- Loading best model with MAE: 4.0841 for plotting ---\nBest Test MAE for 100%: 4.0841\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x700 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"\n--- Final Results for PT ---\nBest MAE with 1% labels: 4.8885\nBest MAE with 10% labels: 4.5764\nBest MAE with 25% labels: 4.3403\nBest MAE with 50% labels: 4.1481\nBest MAE with 100% labels: 4.0841\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x600 with 1 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{}}],"execution_count":18},{"cell_type":"code","source":"\n","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}
\ No newline at end of file
diff --git a/E2E_Foundation_Model_Aaditya_Porwal/NoteBooks/Regression/Supervised_Baseline_Regression.ipynb b/E2E_Foundation_Model_Aaditya_Porwal/NoteBooks/Regression/Supervised_Baseline_Regression.ipynb
new file mode 100644
index 0000000..8abbbaf
--- /dev/null
+++ b/E2E_Foundation_Model_Aaditya_Porwal/NoteBooks/Regression/Supervised_Baseline_Regression.ipynb
@@ -0,0 +1 @@
+{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","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":"gpu","dataSources":[{"sourceId":12447031,"sourceType":"datasetVersion","datasetId":7851613},{"sourceId":12557654,"sourceType":"datasetVersion","datasetId":7929400}],"dockerImageVersionId":31090,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true},"papermill":{"default_parameters":{},"duration":6862.311693,"end_time":"2025-04-02T16:22:17.104023","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2025-04-02T14:27:54.792330","version":"2.6.0"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import Dataset, DataLoader, Subset\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_absolute_error, f1_score\nimport torchvision.transforms as T\nimport matplotlib.pyplot as plt\nimport h5py\nimport numpy as np\nfrom sklearn.preprocessing import StandardScaler\nimport os\n\nclass Config:\n    # --- Paths ---\n    DATA_FILE_PATH = \"/kaggle/input/qc0-1million/train_jet_train_meta_100k.h5\"\n\n    # --- Data ---\n    IMAGE_SIZE = 125\n    EVAL_START_IDX = 70000\n    EVAL_END_IDX = 100000\n    TEST_SET_FRACTION = 0.5\n\n    # --- Augmentation ---\n    ROTATION_DEGREES = 60.0\n    GAUSSIAN_BLUR_SIGMA = (0.1, 2.0)\n\n    # --- Training ---\n    BATCH_SIZE = 128\n    EPOCHS = 25\n    # MODIFICATION: Reduced learning rate for more stable training\n    LEARNING_RATE = 1e-4\n    WEIGHT_DECAY = 1e-4\n\n    # --- System ---\n    DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\nprint(f\"Using device: {Config.DEVICE}\")\n\nif not os.path.exists(Config.DATA_FILE_PATH):\n    print(f\"ERROR: Data file not found at '{Config.DATA_FILE_PATH}'\")\n\nclass ResidualBlock(nn.Module):\n    def __init__(self, in_channels, out_channels, stride=1):\n        super().__init__()\n        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)\n        self.bn1 = nn.BatchNorm2d(out_channels)\n        self.relu = nn.ReLU(inplace=True)\n        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)\n        self.bn2 = nn.BatchNorm2d(out_channels)\n        self.shortcut = nn.Sequential()\n        if stride != 1 or in_channels != out_channels:\n            self.shortcut = nn.Sequential(\n                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),\n                nn.BatchNorm2d(out_channels)\n            )\n    def forward(self, x):\n        out = self.relu(self.bn1(self.conv1(x)))\n        out = self.bn2(self.conv2(out))\n        out += self.shortcut(x)\n        out = self.relu(out)\n        return out\n\nclass Encoder(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.in_channels = 64\n        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)\n        self.bn1 = nn.BatchNorm2d(64)\n        self.relu = nn.ReLU(inplace=True)\n        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n        self.layer1 = self._make_layer(64, 2, stride=1)\n        self.layer2 = self._make_layer(128, 2, stride=2)\n        self.layer3 = self._make_layer(256, 2, stride=2)\n        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n\n    def _make_layer(self, out_channels, num_blocks, stride):\n        strides = [stride] + [1]*(num_blocks-1)\n        layers = []\n        for s in strides:\n            layers.append(ResidualBlock(self.in_channels, out_channels, s))\n            self.in_channels = out_channels\n        return nn.Sequential(*layers)\n\n    def forward(self, x):\n        x = self.relu(self.bn1(self.conv1(x)))\n        x = self.maxpool(x)\n        x = self.layer1(x)\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.avgpool(x)\n        x = torch.flatten(x, 1)\n        return x\n\nclass SupervisedRegressionModel(nn.Module):\n    def __init__(self, feature_size=256, out_features=1, dropout_p=0.5):\n        super().__init__()\n        self.encoder = Encoder()\n        # MODIFICATION: Added a Dropout layer for regularization\n        self.dropout = nn.Dropout(p=dropout_p)\n        self.regressor = nn.Linear(feature_size, out_features)\n\n    def forward(self, x):\n        features = self.encoder(x)\n        # MODIFICATION: Apply dropout during the forward pass\n        features = self.dropout(features)\n        return self.regressor(features)\n\nclass InMemoryJetDataset(Dataset):\n    def __init__(self, h5_path, start_idx, end_idx, target_idx=0, scaler=None):\n        self.target_idx = target_idx\n        with h5py.File(h5_path, 'r') as f:\n            self.images = f['train_jet'][start_idx:end_idx]\n            self.labels = f['train_meta'][start_idx:end_idx, self.target_idx].reshape(-1, 1)\n        if scaler:\n            self.labels = scaler.transform(self.labels)\n    def __len__(self):\n        return len(self.images)\n    def __getitem__(self, idx):\n        image = self.images[idx]\n        label = self.labels[idx]\n        image_tensor = torch.from_numpy(image).permute(2, 0, 1).float()\n        label_tensor = torch.tensor(label, dtype=torch.float32)\n        return image_tensor, label_tensor\n\nclass EarlyStopping:\n    def __init__(self, patience=15, min_delta=0.001, verbose=True):\n        self.patience, self.min_delta, self.verbose = patience, min_delta, verbose\n        self.counter, self.best_mae, self.early_stop = 0, float('inf'), False\n    def __call__(self, current_mae):\n        if self.best_mae - current_mae > self.min_delta:\n            if self.verbose: print(f\"  -> Val MAE improved ({self.best_mae:.4f} --> {current_mae:.4f}).\")\n            self.best_mae = current_mae\n            self.counter = 0\n        else:\n            self.counter += 1\n            if self.verbose: print(f\"  -> EarlyStopping counter: {self.counter} out of {self.patience}\")\n            if self.counter >= self.patience:\n                print(\"  -> Early stopping triggered.\")\n                self.early_stop = True\n\ndef run_supervised_training(label_percentage, tuning_pool_indices, full_eval_dataset, test_dataset, target_name, scaler):\n    print(f\"\\n--- Running Supervised Baseline for {target_name.upper()} ({label_percentage*100:.0f}% Labels) ---\")\n\n    train_indices, _ = train_test_split(tuning_pool_indices, train_size=label_percentage, random_state=42) if label_percentage < 1.0 else (tuning_pool_indices, None)\n\n    train_subset = Subset(full_eval_dataset, train_indices)\n    print(f\"Training on {len(train_subset)} samples, testing on {len(test_dataset)} samples.\")\n    train_loader = DataLoader(train_subset, batch_size=Config.BATCH_SIZE, shuffle=True, num_workers=0)\n    test_loader = DataLoader(test_dataset, batch_size=Config.BATCH_SIZE, shuffle=False, num_workers=0)\n\n    model = SupervisedRegressionModel().to(Config.DEVICE)\n    optimizer = optim.Adam(model.parameters(), lr=Config.LEARNING_RATE, weight_decay=Config.WEIGHT_DECAY)\n    # MODIFICATION: Increased scheduler patience for more stability\n    scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', patience=3, factor=0.5, verbose=True)\n    criterion = nn.L1Loss()\n    early_stopper = EarlyStopping(patience=5)\n\n    best_mae = float('inf')\n    history = {'train_loss': [], 'val_mae': []}\n    best_model_path = f\"best_model_{target_name}_{int(label_percentage*100)}.pth\"\n\n    for epoch in range(Config.EPOCHS):\n        model.train()\n        train_loss = 0\n        for images, labels_scaled in train_loader:\n            images, labels_scaled = images.to(Config.DEVICE), labels_scaled.to(Config.DEVICE)\n            images = (images / 255.0)\n            images = train_transform(images)\n            optimizer.zero_grad()\n            outputs_scaled = model(images)\n            loss = criterion(outputs_scaled, labels_scaled)\n            loss.backward()\n            optimizer.step()\n            train_loss += loss.item()\n        history['train_loss'].append(train_loss / len(train_loader))\n\n        model.eval()\n        all_labels_scaled, all_preds_scaled = [], []\n        with torch.no_grad():\n            for images, labels_scaled in test_loader:\n                images, labels_scaled = images.to(Config.DEVICE), labels_scaled.to(Config.DEVICE)\n                images = (images / 255.0)\n                images = test_transform(images)\n                preds_scaled = model(images)\n                all_labels_scaled.append(labels_scaled.cpu().numpy())\n                all_preds_scaled.append(preds_scaled.cpu().numpy())\n\n        all_labels_orig = scaler.inverse_transform(np.concatenate(all_labels_scaled))\n        all_preds_orig = scaler.inverse_transform(np.concatenate(all_preds_scaled))\n        val_mae = mean_absolute_error(all_labels_orig, all_preds_orig)\n        history['val_mae'].append(val_mae)\n        \n        print(f\"Epoch [{epoch+1}/{Config.EPOCHS}] -> Train MAE Loss: {history['train_loss'][-1]:.4f} | Val MAE: {val_mae:.4f}\")\n        \n        scheduler.step(val_mae)\n        \n        if val_mae < best_mae:\n            best_mae = val_mae\n            torch.save(model.state_dict(), best_model_path)\n\n        early_stopper(val_mae)\n        if early_stopper.early_stop:\n            break\n\n    print(f\"--- Loading best model with MAE: {best_mae:.4f} for plotting ---\")\n    model.load_state_dict(torch.load(best_model_path))\n    model.eval()\n    \n    best_labels_scaled, best_preds_scaled = [], []\n    with torch.no_grad():\n        for images, labels_scaled in test_loader:\n            images, labels_scaled = images.to(Config.DEVICE), labels_scaled.to(Config.DEVICE)\n            images = (images / 255.0)\n            images = test_transform(images)\n            preds_scaled = model(images)\n            best_labels_scaled.append(labels_scaled.cpu().numpy())\n            best_preds_scaled.append(preds_scaled.cpu().numpy())\n\n    best_labels_orig = scaler.inverse_transform(np.concatenate(best_labels_scaled))\n    best_preds_orig = scaler.inverse_transform(np.concatenate(best_preds_scaled))\n\n    print(f\"Best Test MAE for {label_percentage*100:.0f}%: {best_mae:.4f}\")\n    return best_mae, history, best_labels_orig, best_preds_orig\n\ndef plot_metrics_vs_epochs(history_train_loss, history_val_mae, target_name):\n    plt.style.use('seaborn-v0_8-whitegrid')\n    fig, ax = plt.subplots(2, 1, figsize=(12, 10), sharex=True)\n    for percent_str in history_train_loss.keys():\n        ax[0].plot(range(1, len(history_train_loss[percent_str]) + 1), history_train_loss[percent_str], marker='o', linestyle='-', label=f'{percent_str} Train Loss')\n    ax[0].set_ylabel('MAE Loss (Scaled Data)', fontsize=12)\n    ax[0].set_title(f'Training Loss for {target_name.upper()}', fontsize=14, weight='bold')\n    ax[0].legend()\n    ax[0].grid(True)\n    for percent_str, maes in history_val_mae.items():\n        ax[1].plot(range(1, len(maes) + 1), maes, marker='x', linestyle='--', label=f'{percent_str} Val MAE')\n    ax[1].set_ylabel('MAE (Original Scale)', fontsize=12)\n    ax[1].set_title(f'Validation MAE for {target_name.upper()}', fontsize=14, weight='bold')\n    ax[1].set_xlabel('Epoch', fontsize=14)\n    ax[1].legend()\n    ax[1].grid(True)\n    fig.suptitle(f'Supervised {target_name.upper()} Training Metrics vs. Epochs', fontsize=16, weight='bold')\n    plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n    plt.show()\n\ndef plot_results(results_mae, target_name):\n    print(f\"\\n--- Final Results for {target_name.upper()} ---\")\n    for percent, mae in results_mae.items(): print(f\"Best MAE with {percent} labels: {mae:.4f}\")\n    plt.style.use('seaborn-v0_8-whitegrid')\n    fig, ax = plt.subplots(figsize=(10, 6))\n    bars = ax.bar(results_mae.keys(), results_mae.values(), color=['#ff9999','#66b3ff','#99ff99','#ffcc99', '#c2c2f0'])\n    ax.set_ylabel('Mean Absolute Error (MAE)', fontsize=14)\n    ax.set_xlabel('Percentage of Labeled Data Used', fontsize=14)\n    ax.set_title(f'Supervised Baseline {target_name.upper()} Performance (MAE)', fontsize=16, weight='bold')\n    for bar in bars:\n        yval = bar.get_height()\n        ax.text(bar.get_x() + bar.get_width()/2.0, yval, f'{yval:.4f}', ha='center', va='bottom', fontsize=12)\n    plt.show()\n\ndef plot_true_vs_predicted_heatmap(true_labels, predictions, target_name, label_percentage):\n    plt.style.use('default')\n    fig, ax = plt.subplots(figsize=(8, 7))\n    mae = mean_absolute_error(true_labels, predictions)\n    min_val, max_val = min(np.min(true_labels), np.min(predictions)), max(np.max(true_labels), np.max(predictions))\n    bins = np.linspace(min_val, max_val, 51)\n    h = ax.hist2d(true_labels.flatten(), predictions.flatten(), bins=[bins, bins], cmap='gist_heat')\n    fig.colorbar(h[3], ax=ax, label='Frequency')\n    ax.plot([min_val, max_val], [min_val, max_val], 'b-', alpha=0.6, lw=2, label='Ideal Correlation')\n    mean_true, mean_pred = np.mean(true_labels), np.mean(predictions)\n    ax.scatter(mean_true, mean_pred, s=250, marker='+', color='white', lw=3, label='Mean Value', zorder=10)\n    ax.set_xlabel(f'True {target_name.capitalize()}', fontsize=12)\n    ax.set_ylabel(f'Predicted {target_name.capitalize()}', fontsize=12)\n    ax.set_title(f'Frequency Heatmap ({label_percentage*100:.0f}% Labels) for {target_name.upper()}\\nBest MAE: {mae:.4f}', fontsize=14)\n    ax.legend()\n    ax.set_aspect('equal', adjustable='box')\n    plt.tight_layout()\n    plt.show()\n\nif __name__ == '__main__':\n    if not os.path.exists(Config.DATA_FILE_PATH):\n        print(\"\\nExiting. Please check data file path.\")\n    else:\n        train_transform= nn.Identity().to(Config.DEVICE)\n        test_transform = nn.Identity().to(Config.DEVICE)\n\n        label_percentages = [0.01, 0.10, 0.25, 0.50, 1.0]\n\n        for target_idx, target_name in enumerate([\"mass\", \"pt\"]):\n            print(\"\\n\" + \"=\"*50 + f\"\\nRUNNING SUPERVISED BASELINE FOR {target_name.upper()}\\n\" + \"=\"*50)\n\n            with h5py.File(Config.DATA_FILE_PATH, 'r') as f:\n                raw_labels = f['train_meta'][Config.EVAL_START_IDX:Config.EVAL_END_IDX, target_idx].reshape(-1, 1)\n\n            all_indices = list(range(len(raw_labels)))\n            tuning_pool_indices, test_indices, _, _ = train_test_split(all_indices, raw_labels, test_size=0.5, random_state=42)\n\n            scaler = StandardScaler()\n            scaler.fit(raw_labels[tuning_pool_indices])\n\n            full_eval_dataset = InMemoryJetDataset(Config.DATA_FILE_PATH, Config.EVAL_START_IDX, Config.EVAL_END_IDX, target_idx=target_idx, scaler=scaler)\n            test_dataset = Subset(full_eval_dataset, test_indices)\n\n            results_mae, history_train_loss, history_val_mae = {}, {}, {}\n            \n            for percent in label_percentages:\n                best_mae, history, true_labels, preds = run_supervised_training(percent, tuning_pool_indices, full_eval_dataset, test_dataset, target_name, scaler)\n                results_mae[f\"{int(percent*100)}%\"] = best_mae\n                history_train_loss[f\"{int(percent*100)}%\"] = history['train_loss']\n                history_val_mae[f\"{int(percent*100)}%\"] = history['val_mae']\n                \n                plot_true_vs_predicted_heatmap(\n                    true_labels.flatten(),\n                    preds.flatten(),\n                    target_name,\n                    percent\n                )\n\n            plot_results(results_mae, target_name)\n            plot_metrics_vs_epochs(history_train_loss, history_val_mae, target_name)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-17T05:17:31.836851Z","iopub.execute_input":"2025-08-17T05:17:31.837096Z","iopub.status.idle":"2025-08-17T05:35:23.857695Z","shell.execute_reply.started":"2025-08-17T05:17:31.837074Z","shell.execute_reply":"2025-08-17T05:35:23.856966Z"}},"outputs":[{"name":"stdout","text":"Using device: cuda\n\n==================================================\nRUNNING SUPERVISED BASELINE FOR MASS\n==================================================\n\n--- Running Supervised Baseline for MASS (1% Labels) ---\nTraining on 150 samples, testing on 15000 samples.\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/torch/optim/lr_scheduler.py:62: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Epoch [1/25] -> Train MAE Loss: 0.8880 | Val MAE: 20.5199\n  -> Val MAE improved (inf --> 20.5199).\nEpoch [2/25] -> Train MAE Loss: 0.8750 | Val MAE: 20.4243\n  -> Val MAE improved (20.5199 --> 20.4243).\nEpoch [3/25] -> Train MAE Loss: 0.7918 | Val MAE: 20.3302\n  -> Val MAE improved (20.4243 --> 20.3302).\nEpoch [4/25] -> Train MAE Loss: 0.7101 | Val MAE: 20.4649\n  -> EarlyStopping counter: 1 out of 5\nEpoch [5/25] -> Train MAE Loss: 0.6863 | Val MAE: 20.8144\n  -> EarlyStopping counter: 2 out of 5\nEpoch [6/25] -> Train MAE Loss: 0.7415 | Val MAE: 21.2054\n  -> EarlyStopping counter: 3 out of 5\nEpoch [7/25] -> Train MAE Loss: 0.7304 | Val MAE: 21.4893\n  -> EarlyStopping counter: 4 out of 5\nEpoch [8/25] -> Train MAE Loss: 0.7807 | Val MAE: 21.6453\n  -> EarlyStopping counter: 5 out of 5\n  -> Early stopping triggered.\n--- Loading best model with MAE: 20.3302 for plotting ---\nBest Test MAE for 1%: 20.3302\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x700 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"\n--- Running Supervised Baseline for MASS (10% Labels) ---\nTraining on 1500 samples, testing on 15000 samples.\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/torch/optim/lr_scheduler.py:62: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Epoch [1/25] -> Train MAE Loss: 0.8482 | Val MAE: 21.3425\n  -> Val MAE improved (inf --> 21.3425).\nEpoch [2/25] -> Train MAE Loss: 0.7941 | Val MAE: 22.1620\n  -> EarlyStopping counter: 1 out of 5\nEpoch [3/25] -> Train MAE Loss: 0.7712 | Val MAE: 25.1742\n  -> EarlyStopping counter: 2 out of 5\nEpoch [4/25] -> Train MAE Loss: 0.7224 | Val MAE: 19.6206\n  -> Val MAE improved (21.3425 --> 19.6206).\nEpoch [5/25] -> Train MAE Loss: 0.7074 | Val MAE: 19.7542\n  -> EarlyStopping counter: 1 out of 5\nEpoch [6/25] -> Train MAE Loss: 0.6358 | Val MAE: 19.5291\n  -> Val MAE improved (19.6206 --> 19.5291).\nEpoch [7/25] -> Train MAE Loss: 0.5945 | Val MAE: 22.4494\n  -> EarlyStopping counter: 1 out of 5\nEpoch [8/25] -> Train MAE Loss: 0.5765 | Val MAE: 20.8314\n  -> EarlyStopping counter: 2 out of 5\nEpoch [9/25] -> Train MAE Loss: 0.5564 | Val MAE: 21.5146\n  -> EarlyStopping counter: 3 out of 5\nEpoch [10/25] -> Train MAE Loss: 0.5107 | Val MAE: 21.1819\n  -> EarlyStopping counter: 4 out of 5\nEpoch [11/25] -> Train MAE Loss: 0.4859 | Val MAE: 22.8038\n  -> EarlyStopping counter: 5 out of 5\n  -> Early stopping triggered.\n--- Loading best model with MAE: 19.5291 for plotting ---\nBest Test MAE for 10%: 19.5291\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x700 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"\n--- Running Supervised Baseline for MASS (25% Labels) ---\nTraining on 3750 samples, testing on 15000 samples.\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/torch/optim/lr_scheduler.py:62: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Epoch [1/25] -> Train MAE Loss: 0.8165 | Val MAE: 21.2124\n  -> Val MAE improved (inf --> 21.2124).\nEpoch [2/25] -> Train MAE Loss: 0.7761 | Val MAE: 20.1484\n  -> Val MAE improved (21.2124 --> 20.1484).\nEpoch [3/25] -> Train MAE Loss: 0.7631 | Val MAE: 19.2113\n  -> Val MAE improved (20.1484 --> 19.2113).\nEpoch [4/25] -> Train MAE Loss: 0.7245 | Val MAE: 18.9880\n  -> Val MAE improved (19.2113 --> 18.9880).\nEpoch [5/25] -> Train MAE Loss: 0.7203 | Val MAE: 20.6227\n  -> EarlyStopping counter: 1 out of 5\nEpoch [6/25] -> Train MAE Loss: 0.6917 | Val MAE: 20.0585\n  -> EarlyStopping counter: 2 out of 5\nEpoch [7/25] -> Train MAE Loss: 0.6681 | Val MAE: 22.3417\n  -> EarlyStopping counter: 3 out of 5\nEpoch [8/25] -> Train MAE Loss: 0.6500 | Val MAE: 20.0045\n  -> EarlyStopping counter: 4 out of 5\nEpoch [9/25] -> Train MAE Loss: 0.6007 | Val MAE: 19.9171\n  -> EarlyStopping counter: 5 out of 5\n  -> Early stopping triggered.\n--- Loading best model with MAE: 18.9880 for plotting ---\nBest Test MAE for 25%: 18.9880\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x700 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"\n--- Running Supervised Baseline for MASS (50% Labels) ---\nTraining on 7500 samples, testing on 15000 samples.\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/torch/optim/lr_scheduler.py:62: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Epoch [1/25] -> Train MAE Loss: 0.8011 | Val MAE: 19.8644\n  -> Val MAE improved (inf --> 19.8644).\nEpoch [2/25] -> Train MAE Loss: 0.7537 | Val MAE: 18.6226\n  -> Val MAE improved (19.8644 --> 18.6226).\nEpoch [3/25] -> Train MAE Loss: 0.7286 | Val MAE: 27.1876\n  -> EarlyStopping counter: 1 out of 5\nEpoch [4/25] -> Train MAE Loss: 0.7075 | Val MAE: 20.7302\n  -> EarlyStopping counter: 2 out of 5\nEpoch [5/25] -> Train MAE Loss: 0.6800 | Val MAE: 17.9698\n  -> Val MAE improved (18.6226 --> 17.9698).\nEpoch [6/25] -> Train MAE Loss: 0.6561 | Val MAE: 17.8018\n  -> Val MAE improved (17.9698 --> 17.8018).\nEpoch [7/25] -> Train MAE Loss: 0.6273 | Val MAE: 25.0687\n  -> EarlyStopping counter: 1 out of 5\nEpoch [8/25] -> Train MAE Loss: 0.6271 | Val MAE: 18.9126\n  -> EarlyStopping counter: 2 out of 5\nEpoch [9/25] -> Train MAE Loss: 0.5878 | Val MAE: 19.3016\n  -> EarlyStopping counter: 3 out of 5\nEpoch [10/25] -> Train MAE Loss: 0.5747 | Val MAE: 19.8646\n  -> EarlyStopping counter: 4 out of 5\nEpoch [11/25] -> Train MAE Loss: 0.5167 | Val MAE: 18.8811\n  -> EarlyStopping counter: 5 out of 5\n  -> Early stopping triggered.\n--- Loading best model with MAE: 17.8018 for plotting ---\nBest Test MAE for 50%: 17.8018\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x700 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"\n--- Running Supervised Baseline for MASS (100% Labels) ---\nTraining on 15000 samples, testing on 15000 samples.\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/torch/optim/lr_scheduler.py:62: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Epoch [1/25] -> Train MAE Loss: 0.7856 | Val MAE: 20.9288\n  -> Val MAE improved (inf --> 20.9288).\nEpoch [2/25] -> Train MAE Loss: 0.7351 | Val MAE: 21.7583\n  -> EarlyStopping counter: 1 out of 5\nEpoch [3/25] -> Train MAE Loss: 0.7012 | Val MAE: 27.5634\n  -> EarlyStopping counter: 2 out of 5\nEpoch [4/25] -> Train MAE Loss: 0.6810 | Val MAE: 17.9186\n  -> Val MAE improved (20.9288 --> 17.9186).\nEpoch [5/25] -> Train MAE Loss: 0.6578 | Val MAE: 18.2337\n  -> EarlyStopping counter: 1 out of 5\nEpoch [6/25] -> Train MAE Loss: 0.6450 | Val MAE: 17.3500\n  -> Val MAE improved (17.9186 --> 17.3500).\nEpoch [7/25] -> Train MAE Loss: 0.6205 | Val MAE: 17.2157\n  -> Val MAE improved (17.3500 --> 17.2157).\nEpoch [8/25] -> Train MAE Loss: 0.6109 | Val MAE: 17.4566\n  -> EarlyStopping counter: 1 out of 5\nEpoch [9/25] -> Train MAE Loss: 0.5992 | Val MAE: 17.3296\n  -> EarlyStopping counter: 2 out of 5\nEpoch [10/25] -> Train MAE Loss: 0.5732 | Val MAE: 19.1864\n  -> EarlyStopping counter: 3 out of 5\nEpoch [11/25] -> Train MAE Loss: 0.5537 | Val MAE: 18.5679\n  -> EarlyStopping counter: 4 out of 5\nEpoch [12/25] -> Train MAE Loss: 0.5069 | Val MAE: 17.5129\n  -> EarlyStopping counter: 5 out of 5\n  -> Early stopping triggered.\n--- Loading best model with MAE: 17.2157 for plotting ---\nBest Test MAE for 100%: 17.2157\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x700 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"\n--- Final Results for MASS ---\nBest MAE with 1% labels: 20.3302\nBest MAE with 10% labels: 19.5291\nBest MAE with 25% labels: 18.9880\nBest MAE with 50% labels: 17.8018\nBest MAE with 100% labels: 17.2157\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x600 with 1 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{}},{"name":"stdout","text":"\n==================================================\nRUNNING SUPERVISED BASELINE FOR PT\n==================================================\n\n--- Running Supervised Baseline for PT (1% Labels) ---\nTraining on 150 samples, testing on 15000 samples.\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/torch/optim/lr_scheduler.py:62: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Epoch [1/25] -> Train MAE Loss: 0.9405 | Val MAE: 4.9260\n  -> Val MAE improved (inf --> 4.9260).\nEpoch [2/25] -> Train MAE Loss: 0.8792 | Val MAE: 4.8736\n  -> Val MAE improved (4.9260 --> 4.8736).\nEpoch [3/25] -> Train MAE Loss: 0.7935 | Val MAE: 4.9234\n  -> EarlyStopping counter: 1 out of 5\nEpoch [4/25] -> Train MAE Loss: 0.7592 | Val MAE: 5.0228\n  -> EarlyStopping counter: 2 out of 5\nEpoch [5/25] -> Train MAE Loss: 0.8144 | Val MAE: 5.1531\n  -> EarlyStopping counter: 3 out of 5\nEpoch [6/25] -> Train MAE Loss: 0.6818 | Val MAE: 5.3465\n  -> EarlyStopping counter: 4 out of 5\nEpoch [7/25] -> Train MAE Loss: 0.6506 | Val MAE: 5.5462\n  -> EarlyStopping counter: 5 out of 5\n  -> Early stopping triggered.\n--- Loading best model with MAE: 4.8736 for plotting ---\nBest Test MAE for 1%: 4.8736\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x700 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"\n--- Running Supervised Baseline for PT (10% Labels) ---\nTraining on 1500 samples, testing on 15000 samples.\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/torch/optim/lr_scheduler.py:62: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Epoch [1/25] -> Train MAE Loss: 0.8269 | Val MAE: 5.1965\n  -> Val MAE improved (inf --> 5.1965).\nEpoch [2/25] -> Train MAE Loss: 0.7576 | Val MAE: 6.8197\n  -> EarlyStopping counter: 1 out of 5\nEpoch [3/25] -> Train MAE Loss: 0.6719 | Val MAE: 6.0698\n  -> EarlyStopping counter: 2 out of 5\nEpoch [4/25] -> Train MAE Loss: 0.6343 | Val MAE: 13.8110\n  -> EarlyStopping counter: 3 out of 5\nEpoch [5/25] -> Train MAE Loss: 0.6041 | Val MAE: 5.0034\n  -> Val MAE improved (5.1965 --> 5.0034).\nEpoch [6/25] -> Train MAE Loss: 0.5415 | Val MAE: 5.9774\n  -> EarlyStopping counter: 1 out of 5\nEpoch [7/25] -> Train MAE Loss: 0.5080 | Val MAE: 4.1730\n  -> Val MAE improved (5.0034 --> 4.1730).\nEpoch [8/25] -> Train MAE Loss: 0.4892 | Val MAE: 4.7647\n  -> EarlyStopping counter: 1 out of 5\nEpoch [9/25] -> Train MAE Loss: 0.5014 | Val MAE: 5.2573\n  -> EarlyStopping counter: 2 out of 5\nEpoch [10/25] -> Train MAE Loss: 0.4355 | Val MAE: 4.9075\n  -> EarlyStopping counter: 3 out of 5\nEpoch [11/25] -> Train MAE Loss: 0.4168 | Val MAE: 5.3471\n  -> EarlyStopping counter: 4 out of 5\nEpoch [12/25] -> Train MAE Loss: 0.4096 | Val MAE: 4.2611\n  -> EarlyStopping counter: 5 out of 5\n  -> Early stopping triggered.\n--- Loading best model with MAE: 4.1730 for plotting ---\nBest Test MAE for 10%: 4.1730\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x700 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"\n--- Running Supervised Baseline for PT (25% Labels) ---\nTraining on 3750 samples, testing on 15000 samples.\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/torch/optim/lr_scheduler.py:62: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Epoch [1/25] -> Train MAE Loss: 0.7801 | Val MAE: 8.4455\n  -> Val MAE improved (inf --> 8.4455).\nEpoch [2/25] -> Train MAE Loss: 0.7096 | Val MAE: 15.7794\n  -> EarlyStopping counter: 1 out of 5\nEpoch [3/25] -> Train MAE Loss: 0.6553 | Val MAE: 4.4683\n  -> Val MAE improved (8.4455 --> 4.4683).\nEpoch [4/25] -> Train MAE Loss: 0.6498 | Val MAE: 4.4571\n  -> Val MAE improved (4.4683 --> 4.4571).\nEpoch [5/25] -> Train MAE Loss: 0.6031 | Val MAE: 5.1728\n  -> EarlyStopping counter: 1 out of 5\nEpoch [6/25] -> Train MAE Loss: 0.5787 | Val MAE: 4.0935\n  -> Val MAE improved (4.4571 --> 4.0935).\nEpoch [7/25] -> Train MAE Loss: 0.5363 | Val MAE: 4.2153\n  -> EarlyStopping counter: 1 out of 5\nEpoch [8/25] -> Train MAE Loss: 0.5275 | Val MAE: 4.1204\n  -> EarlyStopping counter: 2 out of 5\nEpoch [9/25] -> Train MAE Loss: 0.5090 | Val MAE: 3.9876\n  -> Val MAE improved (4.0935 --> 3.9876).\nEpoch [10/25] -> Train MAE Loss: 0.4722 | Val MAE: 5.0480\n  -> EarlyStopping counter: 1 out of 5\nEpoch [11/25] -> Train MAE Loss: 0.4423 | Val MAE: 4.5309\n  -> EarlyStopping counter: 2 out of 5\nEpoch [12/25] -> Train MAE Loss: 0.4353 | Val MAE: 4.4908\n  -> EarlyStopping counter: 3 out of 5\nEpoch [13/25] -> Train MAE Loss: 0.4079 | Val MAE: 7.3358\n  -> EarlyStopping counter: 4 out of 5\nEpoch [14/25] -> Train MAE Loss: 0.3571 | Val MAE: 4.0096\n  -> EarlyStopping counter: 5 out of 5\n  -> Early stopping triggered.\n--- Loading best model with MAE: 3.9876 for plotting ---\nBest Test MAE for 25%: 3.9876\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x700 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"\n--- Running Supervised Baseline for PT (50% Labels) ---\nTraining on 7500 samples, testing on 15000 samples.\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/torch/optim/lr_scheduler.py:62: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Epoch [1/25] -> Train MAE Loss: 0.7462 | Val MAE: 14.0686\n  -> Val MAE improved (inf --> 14.0686).\nEpoch [2/25] -> Train MAE Loss: 0.6640 | Val MAE: 4.4471\n  -> Val MAE improved (14.0686 --> 4.4471).\nEpoch [3/25] -> Train MAE Loss: 0.6310 | Val MAE: 4.0463\n  -> Val MAE improved (4.4471 --> 4.0463).\nEpoch [4/25] -> Train MAE Loss: 0.5937 | Val MAE: 3.8157\n  -> Val MAE improved (4.0463 --> 3.8157).\nEpoch [5/25] -> Train MAE Loss: 0.5624 | Val MAE: 4.8252\n  -> EarlyStopping counter: 1 out of 5\nEpoch [6/25] -> Train MAE Loss: 0.5388 | Val MAE: 3.6660\n  -> Val MAE improved (3.8157 --> 3.6660).\nEpoch [7/25] -> Train MAE Loss: 0.5230 | Val MAE: 3.6352\n  -> Val MAE improved (3.6660 --> 3.6352).\nEpoch [8/25] -> Train MAE Loss: 0.4974 | Val MAE: 5.3554\n  -> EarlyStopping counter: 1 out of 5\nEpoch [9/25] -> Train MAE Loss: 0.4687 | Val MAE: 3.9419\n  -> EarlyStopping counter: 2 out of 5\nEpoch [10/25] -> Train MAE Loss: 0.4354 | Val MAE: 5.5514\n  -> EarlyStopping counter: 3 out of 5\nEpoch [11/25] -> Train MAE Loss: 0.4249 | Val MAE: 3.7879\n  -> EarlyStopping counter: 4 out of 5\nEpoch [12/25] -> Train MAE Loss: 0.3766 | Val MAE: 3.5491\n  -> Val MAE improved (3.6352 --> 3.5491).\nEpoch [13/25] -> Train MAE Loss: 0.3366 | Val MAE: 3.5868\n  -> EarlyStopping counter: 1 out of 5\nEpoch [14/25] -> Train MAE Loss: 0.3210 | Val MAE: 3.6579\n  -> EarlyStopping counter: 2 out of 5\nEpoch [15/25] -> Train MAE Loss: 0.3231 | Val MAE: 3.5584\n  -> EarlyStopping counter: 3 out of 5\nEpoch [16/25] -> Train MAE Loss: 0.2926 | Val MAE: 3.8397\n  -> EarlyStopping counter: 4 out of 5\nEpoch [17/25] -> Train MAE Loss: 0.2735 | Val MAE: 3.5416\n  -> Val MAE improved (3.5491 --> 3.5416).\nEpoch [18/25] -> Train MAE Loss: 0.2507 | Val MAE: 3.6012\n  -> EarlyStopping counter: 1 out of 5\nEpoch [19/25] -> Train MAE Loss: 0.2377 | Val MAE: 3.8221\n  -> EarlyStopping counter: 2 out of 5\nEpoch [20/25] -> Train MAE Loss: 0.2363 | Val MAE: 3.5584\n  -> EarlyStopping counter: 3 out of 5\nEpoch [21/25] -> Train MAE Loss: 0.2258 | Val MAE: 3.5571\n  -> EarlyStopping counter: 4 out of 5\nEpoch [22/25] -> Train MAE Loss: 0.2136 | Val MAE: 3.5456\n  -> EarlyStopping counter: 5 out of 5\n  -> Early stopping triggered.\n--- Loading best model with MAE: 3.5416 for plotting ---\nBest Test MAE for 50%: 3.5416\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x700 with 2 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\n"},"metadata":{}},{"name":"stdout","text":"\n--- Running Supervised Baseline for PT (100% Labels) ---\nTraining on 15000 samples, testing on 15000 samples.\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/torch/optim/lr_scheduler.py:62: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Epoch [1/25] -> Train MAE Loss: 0.7074 | Val MAE: 3.9624\n  -> Val MAE improved (inf --> 3.9624).\nEpoch [2/25] -> Train MAE Loss: 0.6298 | Val MAE: 4.0375\n  -> EarlyStopping counter: 1 out of 5\nEpoch [3/25] -> Train MAE Loss: 0.5864 | Val MAE: 3.6355\n  -> Val MAE improved (3.9624 --> 3.6355).\nEpoch [4/25] -> Train MAE Loss: 0.5624 | Val MAE: 3.5667\n  -> Val MAE improved (3.6355 --> 3.5667).\nEpoch [5/25] -> Train MAE Loss: 0.5336 | Val MAE: 4.6056\n  -> EarlyStopping counter: 1 out of 5\nEpoch [6/25] -> Train MAE Loss: 0.5047 | Val MAE: 5.2330\n  -> EarlyStopping counter: 2 out of 5\nEpoch [7/25] -> Train MAE Loss: 0.4777 | Val MAE: 4.6689\n  -> EarlyStopping counter: 3 out of 5\nEpoch [8/25] -> Train MAE Loss: 0.4677 | Val MAE: 3.5136\n  -> Val MAE improved (3.5667 --> 3.5136).\nEpoch [9/25] -> Train MAE Loss: 0.4437 | Val MAE: 3.1744\n  -> Val MAE improved (3.5136 --> 3.1744).\nEpoch [10/25] -> Train MAE Loss: 0.4254 | Val MAE: 3.6965\n  -> EarlyStopping counter: 1 out of 5\nEpoch [11/25] -> Train MAE Loss: 0.4108 | Val MAE: 3.0968\n  -> Val MAE improved (3.1744 --> 3.0968).\nEpoch [12/25] -> Train MAE Loss: 0.3948 | Val MAE: 4.0047\n  -> EarlyStopping counter: 1 out of 5\nEpoch [13/25] -> Train MAE Loss: 0.3780 | Val MAE: 3.3222\n  -> EarlyStopping counter: 2 out of 5\nEpoch [14/25] -> Train MAE Loss: 0.3722 | Val MAE: 3.0765\n  -> Val MAE improved (3.0968 --> 3.0765).\nEpoch [15/25] -> Train MAE Loss: 0.3500 | Val MAE: 3.0928\n  -> EarlyStopping counter: 1 out of 5\nEpoch [16/25] -> Train MAE Loss: 0.3374 | Val MAE: 3.8860\n  -> EarlyStopping counter: 2 out of 5\nEpoch [17/25] -> Train MAE Loss: 0.3233 | Val MAE: 3.1539\n  -> EarlyStopping counter: 3 out of 5\nEpoch [18/25] -> Train MAE Loss: 0.3108 | Val MAE: 3.1592\n  -> EarlyStopping counter: 4 out of 5\nEpoch [19/25] -> Train MAE Loss: 0.2741 | Val MAE: 3.4200\n  -> EarlyStopping counter: 5 out of 5\n  -> Early stopping triggered.\n--- Loading best model with MAE: 3.0765 for plotting ---\nBest Test MAE for 100%: 3.0765\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 800x700 with 2 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\n"},"metadata":{}},{"name":"stdout","text":"\n--- Final Results for PT ---\nBest MAE with 1% labels: 4.8736\nBest MAE with 10% labels: 4.1730\nBest MAE with 25% labels: 3.9876\nBest MAE with 50% labels: 3.5416\nBest MAE with 100% labels: 3.0765\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x600 with 1 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{}}],"execution_count":1},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}
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diff --git a/E2E_Foundation_Model_Aaditya_Porwal/README.md b/E2E_Foundation_Model_Aaditya_Porwal/README.md
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@@ -0,0 +1,84 @@
+# Foundation Models for End-to-End Event Reconstruction
+
+![GSoC 2025](https://img.shields.io/badge/GSoC-2025-orange)
+![ML4Sci](https://img.shields.io/badge/ML4Sci-blue)
+![License](https://img.shields.io/badge/License-MIT-green.svg)
+
+<p align="center">
+  <img src="images/image-1.png" width="800" alt="Project Banner" />
+</p>
+
+**GSoC 2025 Project for ML4SCI | [Official Project Link](https://ml4sci.org/gsoc/2025/proposal_E2E7.html)**
+
+This repository contains the official code for my [Google Summer of Code 2025 project](https://summerofcode.withgoogle.com/programs/2025/projects/qZDekrLE) with the Machine Learning for Science (ML4SCI) organization. The goal is to develop and benchmark self-supervised foundation models for end-to-end particle reconstruction in the CMS experiment.
+
+**Author:** Aaditya Porwal  
+**Mentors:** Sergei Gleyzer, Diptarko Choudhury, Eric Reinhardt, Ruchi Chudasama  
+**Read my Progress on Medium:** **[Foundation Models for End-to-End Event Reconstruction](https://medium.com/@aadityaporwal234/foundation-models-for-end-to-end-event-reconstruction-for-the-cms-experiment-08f2e1a45487)**
+
+---
+
+## 📝 About The Project
+
+At the Large Hadron Collider (LHC), the accurate identification and reconstruction of particles and jets are crucial for new physics searches. This project contributes to the **End-to-End Deep Learning (E2E) initiative** within the CMS experiment by evaluating a modern **self-supervised foundation model** against a traditional **supervised baseline**. The project covers both classification (Quark vs. Gluon jets) and regression (jet mass and pT) tasks to analyze the effectiveness of these approaches in data-limited scenarios.
+
+## 🔬 Methodology Overview
+
+The core of this project is a comparative study between two training paradigms using a ResNet-15 architecture on the Quark-Gluon dataset:
+
+1.  **Supervised Baseline:** The model is trained from scratch using only the available labeled data. This serves as the performance benchmark.
+2.  **Self-Supervised Learning (SSL):** A foundation model is first **pre-trained** on unlabeled data using contrastive learning frameworks (SimCLR with NTXent loss, and VICReg). The resulting powerful feature encoder is then evaluated via **linear probing** on downstream tasks using a small fraction of labeled data.
+
+## 💻 Notebooks and Code
+
+This repository provides a set of Jupyter notebooks to reproduce the experiments for pre-training, classification, and regression. The notebooks are organized by task and training strategy.
+
+-----
+
+## 📊 Key Results
+
+Performance was evaluated by training on varying percentages of labeled data and testing on a fixed, held-out set.
+
+### **Regression (Mean Absolute Error)**
+
+The MAE metric shows the model's average error in predicting jet mass and pT. Lower is better.
+
+| Target Variable | Labeled Data | Self-Supervised MAE | Supervised Baseline MAE |
+| :--- | :--- | ---:| ---:|
+| **MASS** | 1% | 20.2417 | 20.3302 |
+| **MASS** | 10% | 19.3553 | 19.5291 |
+| **MASS** | 25% | 18.9364 | 18.9880 |
+| **MASS** | 50% | 18.6915 | 17.8018 |
+| **MASS** | 100% | 18.5610 | 17.2157 |
+| **PT** | 1% | 4.8885 | 4.8736 |
+| **PT** | 10% | 4.5764 | 4.1730 |
+| **PT** | 25% | 4.3403 | 3.9876 |
+| **PT** | 50% | 4.1481 | 3.5416 |
+| **PT** | 100% | 4.0841 | 3.0765 |
+
+### **Classification (ROC AUC Score)**
+
+The model's ability to distinguish between Quark and Gluon jets is quantified by the ROC AUC score, where higher is better. The current self-supervised results for SimCLR and VICReg were generated via linear probing. Full fine-tuning will be implemented later, in conjunction with a larger dataset.
+
+| Labeled Data | Supervised (Baseline) AUC | SimCLR (NTXent) AUC | VICReg (3-Layer Head) AUC |
+| :--- | :--- | :--- | :--- |
+| **1%** | 0.6881 | 0.6378 | 0.6231 |
+| **10%** | 0.7522 | 0.7262 | 0.7206 |
+| **25%** | 0.7687 | 0.7328 | 0.7044 |
+| **50%** | 0.7734 | 0.7361 | 0.7111 |
+| **100%** | 0.7753 | 0.7375 | 0.7222 |
+
+-----
+
+## 💡 Conclusion
+
+The results from this initial phase have successfully established a robust training and evaluation pipeline.
+
+  * **For Regression:** The supervised baseline currently shows a clear advantage, especially on the **pT task**, where its performance scales effectively with more data. The self-supervised model is competitive on the more challenging **MASS task**, particularly in low-data scenarios.
+  * **For Classification:** The supervised model again sets a strong benchmark.
+
+These findings are expected for initial experiments on a smaller pre-training dataset. The next steps will focus on scaling up the self-supervised approach by using larger datasets and deeper model architectures (like ResNet-50), where the benefits of pre-training are expected to become much more significant.
+
+## 📄 License
+
+This project is licensed under the MIT License.
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