{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Notebook 2: Model Architecture & Stage 1 Training\n", "\n", "This notebook handles:\n", "1. Setting up the Swin Transformer V2 Tiny model with a regression head\n", "2. Creating the PyTorch Dataset and DataLoader for spectrum images\n", "3. **Stage 1**: Pre-training on the pure spectrum dataset (15,392 samples)\n", "4. Saving the Stage 1 checkpoint for transfer learning in Notebook 3\n", "\n", "**Prerequisites**: Run Notebook 1 first to generate the data.\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.1 Imports & Configuration" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using device: cuda\n", "GPU: NVIDIA GeForce RTX 4060\n" ] } ], "source": [ "import torch\n", "import torch.nn as nn\n", "from torch.utils.data import Dataset, DataLoader, random_split\n", "import torchvision.transforms as T\n", "import torchvision.models as models\n", "from prodigyopt import Prodigy\n", "import numpy as np\n", "import json\n", "import os\n", "from PIL import Image\n", "from pathlib import Path\n", "from tqdm import tqdm\n", "import matplotlib.pyplot as plt\n", "import time\n", "import cv2\n", "\n", "# Device setup\n", "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "print(f\"Using device: {device}\")\n", "if device.type == 'cuda':\n", " print(f\"GPU: {torch.cuda.get_device_name(0)}\")\n", " # print(f\"Memory: {torch.cuda.get_device_properties(0).total_mem / 1e9:.1f} GB\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.2 Define the Spectrum Dataset Class\n", "\n", "This custom PyTorch Dataset loads spectrum images from disk along with their\n", "corresponding labels (EC, EL, EJ). Labels are normalized to [0, 1] using the\n", "known parameter ranges so that MSE loss treats all three parameters equally." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataset class defined.\n" ] } ], "source": [ "# Parameter ranges for normalization\n", "PARAM_RANGES = {\n", " 'EC': (0.5, 3.0),\n", " 'EL': (0.1, 2.0),\n", " 'EJ': (2.0, 10.0)\n", "}\n", "\n", "def normalize_labels(ec, el, ej):\n", " \"\"\"Normalize parameters to [0, 1] range.\"\"\"\n", " ec_n = (ec - PARAM_RANGES['EC'][0]) / (PARAM_RANGES['EC'][1] - PARAM_RANGES['EC'][0])\n", " el_n = (el - PARAM_RANGES['EL'][0]) / (PARAM_RANGES['EL'][1] - PARAM_RANGES['EL'][0])\n", " ej_n = (ej - PARAM_RANGES['EJ'][0]) / (PARAM_RANGES['EJ'][1] - PARAM_RANGES['EJ'][0])\n", " return np.array([ec_n, el_n, ej_n], dtype=np.float32)\n", "\n", "def denormalize_labels(pred):\n", " \"\"\"Convert normalized predictions back to physical units (GHz).\"\"\"\n", " ec = pred[0] * (PARAM_RANGES['EC'][1] - PARAM_RANGES['EC'][0]) + PARAM_RANGES['EC'][0]\n", " el = pred[1] * (PARAM_RANGES['EL'][1] - PARAM_RANGES['EL'][0]) + PARAM_RANGES['EL'][0]\n", " ej = pred[2] * (PARAM_RANGES['EJ'][1] - PARAM_RANGES['EJ'][0]) + PARAM_RANGES['EJ'][0]\n", " return np.array([ec, el, ej])\n", "\n", "\n", "class FluxoniumSpectrumDataset(Dataset):\n", " \"\"\"\n", " PyTorch Dataset for fluxonium spectrum images.\n", " \n", " Loads grayscale PNGs, converts to 3-channel RGB (for ImageNet-pretrained model),\n", " applies standard ImageNet normalization, and returns normalized labels.\n", " \"\"\"\n", " def __init__(self, data_dir, transform=None, augment=False):\n", " self.data_dir = Path(data_dir)\n", " self.img_dir = self.data_dir / 'images'\n", " \n", " with open(self.data_dir / 'labels.json') as f:\n", " self.labels = json.load(f)\n", " \n", " # Only include samples that have both image and label\n", " self.indices = sorted(self.labels.keys())\n", " self.indices = [idx for idx in self.indices \n", " if (self.img_dir / f'{int(idx):05d}.png').exists()]\n", " \n", " self.transform = transform\n", " self.augment = augment\n", " \n", " # Default transform: convert to tensor + ImageNet normalization\n", " if self.transform is None:\n", " transforms_list = []\n", " if augment:\n", " transforms_list.extend([\n", " T.RandomHorizontalFlip(p=0.5),\n", " T.RandomRotation(degrees=3),\n", " ])\n", " transforms_list.extend([\n", " T.Resize((256, 256)),\n", " T.ToTensor(),\n", " T.Normalize(mean=[0.485, 0.456, 0.406],\n", " std=[0.229, 0.224, 0.225])\n", " ])\n", " self.transform = T.Compose(transforms_list)\n", " \n", " def __len__(self):\n", " return len(self.indices)\n", " \n", " def __getitem__(self, i):\n", " idx = self.indices[i]\n", " img_path = self.img_dir / f'{int(idx):05d}.png'\n", " \n", " # # Load grayscale and convert to RGB (3 channels)\n", " # img = Image.open(img_path).convert('RGB')\n", " # img = self.transform(img)\n", "\n", " # OpenCV version - 3-5x faster image loading\n", " img = cv2.imread(str(img_path), cv2.IMREAD_GRAYSCALE)\n", " img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)\n", " img = cv2.resize(img, (256, 256), interpolation=cv2.INTER_LANCZOS4)\n", " img = img.astype(np.float32) / 255.0\n", " img = (img - np.array([0.485, 0.456, 0.406])) / np.array([0.229, 0.224, 0.225])\n", " img = torch.from_numpy(img.transpose(2, 0, 1))\n", " \n", " label_dict = self.labels[idx]\n", " label = normalize_labels(label_dict['EC'], label_dict['EL'], label_dict['EJ'])\n", " \n", " return img, torch.tensor(label, dtype=torch.float32)\n", "\n", "\n", "print(\"Dataset class defined.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.3 Build the Swin Transformer V2 Model\n", "\n", "We load a Swin Transformer V2 Tiny pretrained on ImageNet and replace the\n", "classification head (1000 classes) with a regression head (3 outputs: EC, EL, EJ).\n", "\n", "The model expects 256ร—256 input with window size 8." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total parameters: 27,584,877\n", "Trainable parameters: 2,307\n" ] } ], "source": [ "def build_model():\n", " \"\"\"\n", " Build Swin Transformer V2 Tiny with a 3-output regression head.\n", " \n", " Architecture:\n", " - Backbone: Swin V2 Tiny (pretrained on ImageNet-1K)\n", " - Head: Linear(768, 3) for predicting [EC, EL, EJ]\n", " \"\"\"\n", " model = models.swin_v2_t(weights=models.Swin_V2_T_Weights.IMAGENET1K_V1)\n", " \n", " # Replace classification head with regression head\n", " in_features = model.head.in_features # 768 for Swin V2 Tiny\n", " model.head = nn.Sequential(\n", " nn.Linear(in_features, 3),\n", " nn.Sigmoid()\n", " )\n", " \n", " # Initialize the new head with small weights\n", " nn.init.xavier_uniform_(model.head[0].weight)\n", " nn.init.zeros_(model.head[0].bias)\n", " \n", " return model\n", "\n", "\n", "model = build_model().to(device)\n", "\n", "# Freeze all pretrained layers\n", "for param in model.parameters():\n", " param.requires_grad = False\n", "\n", "# Train only the regression head\n", "for param in model.head.parameters():\n", " param.requires_grad = True\n", "\n", "# Count parameters\n", "total_params = sum(p.numel() for p in model.parameters())\n", "trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n", "print(f\"Total parameters: {total_params:,}\")\n", "print(f\"Trainable parameters: {trainable_params:,}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.4 Prepare DataLoaders\n", "\n", "Split the pure spectrum dataset into 90% training and 10% validation.\n", "Training data gets augmentation (horizontal flips, small rotations);\n", "validation data gets no augmentation." ] }, { "cell_type": "code", "execution_count": 15, "id": "25879a32", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐Ÿ” INSPECTING DATASET...\n", "โœ… Dataset size: 15329 samples\n", "๐Ÿ“Š Train samples: 13796 | Val samples: 1533\n", "โœ… FIXED LOADERS:\n", " Train batches: 1725\n", " Val batches: 192\n" ] } ], "source": [ "import os, json\n", "from PIL import Image\n", "import torch\n", "from torch.utils.data import Dataset, DataLoader, random_split\n", "import torchvision.transforms as T\n", "\n", "class SpectrumDataset(Dataset):\n", " def __init__(self, image_dir, label_path, transform=None):\n", " self.image_dir = image_dir\n", " self.transform = transform\n", "\n", " with open(label_path, \"r\") as f:\n", " self.labels = json.load(f)\n", "\n", " self.keys = sorted(self.labels.keys(), key=lambda x: int(x))\n", "\n", " def __len__(self):\n", " return len(self.keys)\n", "\n", " def __getitem__(self, idx):\n", " key = self.keys[idx]\n", " img_path = os.path.join(self.image_dir, f\"{int(key):05d}.png\")\n", "\n", " img = Image.open(img_path).convert(\"RGB\")\n", " if self.transform is not None:\n", " img = self.transform(img)\n", "\n", " y = self.labels[key]\n", "\n", " ec_n, el_n, ej_n = normalize_labels(\n", " y[\"EC\"],\n", " y[\"EL\"],\n", " y[\"EJ\"]\n", " )\n", "\n", " label = torch.tensor(\n", " [ec_n, el_n, ej_n],\n", " dtype=torch.float32\n", " )\n", "\n", " return img, label\n", "\n", "transform = T.Compose([\n", " T.Resize((224, 224)),\n", " T.ToTensor(),\n", "])\n", "\n", "dataset = SpectrumDataset(\n", " image_dir=\"data/pure_spectrum/images\",\n", " label_path=\"data/pure_spectrum/labels.json\",\n", " transform=transform\n", ")\n", "\n", "train_size = int(0.9 * len(dataset))\n", "val_size = len(dataset) - train_size\n", "train_ds, val_ds = random_split(dataset, [train_size, val_size])\n", "\n", "train_loader = DataLoader(train_ds, batch_size=8, shuffle=True, num_workers=0)\n", "val_loader = DataLoader(val_ds, batch_size=8, shuffle=False, num_workers=0)\n", "\n", "print(\"๐Ÿ” INSPECTING DATASET...\")\n", "print(f\"โœ… Dataset size: {len(dataset)} samples\")\n", "print(f\"๐Ÿ“Š Train samples: {len(train_ds)} | Val samples: {len(val_ds)}\")\n", "print(\"โœ… FIXED LOADERS:\")\n", "print(f\" Train batches: {len(train_loader)}\")\n", "print(f\" Val batches: {len(val_loader)}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.5 Training Utilities\n", "\n", "Define the loss function (MSE), the paper's custom accuracy metric, and\n", "a training/validation loop with logging." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training utilities defined.\n" ] } ], "source": [ "# Loss function: Mean Squared Error\n", "criterion = nn.SmoothL1Loss(beta=0.05)\n", "\n", "\n", "def compute_accuracy(preds, targets):\n", " \"\"\"\n", " Compute the paper's custom accuracy metric.\n", " \n", " Acc(E_v) = (1/N) * sum(1 - |pred - true| / range)\n", " \n", " Since labels are normalized to [0,1], the range is just 1.0,\n", " so: Acc = 1 - mean(|pred - true|)\n", " \n", " Returns per-parameter and overall accuracy.\n", " \"\"\"\n", " errors = torch.abs(preds - targets) # shape: (N, 3)\n", " per_param_acc = (1.0 - errors.mean(dim=0)) * 100 # percentage\n", " overall_acc = per_param_acc.mean()\n", " return {\n", " 'EC_acc': per_param_acc[0].item(),\n", " 'EL_acc': per_param_acc[1].item(),\n", " 'EJ_acc': per_param_acc[2].item(),\n", " 'overall_acc': overall_acc.item()\n", " }\n", "\n", "\n", "def train_one_epoch(model, loader, optimizer, scheduler, device):\n", " \"\"\"Train for one epoch. Returns average loss.\"\"\"\n", " model.train()\n", " total_loss = 0.0\n", " n_batches = 0\n", " \n", " for imgs, labels in loader:\n", " imgs, labels = imgs.to(device), labels.to(device)\n", " \n", " optimizer.zero_grad()\n", " preds = model(imgs)\n", " loss = criterion(preds, labels)\n", " loss.backward()\n", " optimizer.step()\n", " if scheduler is not None:\n", " scheduler.step()\n", " \n", " total_loss += loss.item()\n", " n_batches += 1\n", " \n", " return total_loss / n_batches\n", "\n", "\n", "@torch.no_grad()\n", "def validate(model, loader, device):\n", " \"\"\"Evaluate on validation set. Returns loss and accuracy metrics.\"\"\"\n", " model.eval()\n", " total_loss = 0.0\n", " all_preds = []\n", " all_targets = []\n", " \n", " for imgs, labels in loader:\n", " imgs, labels = imgs.to(device), labels.to(device)\n", " preds = model(imgs)\n", " loss = criterion(preds, labels)\n", " total_loss += loss.item()\n", " all_preds.append(preds.cpu())\n", " all_targets.append(labels.cpu())\n", " \n", " all_preds = torch.cat(all_preds)\n", " all_targets = torch.cat(all_targets)\n", " avg_loss = total_loss / len(loader)\n", " acc = compute_accuracy(all_preds, all_targets)\n", " \n", " return avg_loss, acc\n", "\n", "\n", "print(\"Training utilities defined.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.6 Stage 1: Train on Pure Spectrum Dataset\n", "\n", "This is the first stage of the two-stage transfer learning approach:\n", "- **Optimizer**: Prodigy (parameter-free, lr=1.0)\n", "- **Scheduler**: Cosine annealing\n", "- **Data**: 15,392 pure spectrum images\n", "- **Epochs**: Train until validation loss plateaus (~100-200 epochs)\n", "\n", "Prodigy automatically adapts the learning rate, so no manual lr tuning is needed." ] }, { "cell_type": "code", "execution_count": 17, "id": "192c7446", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "โœ… Augmented training subset created:\n", " Full train dataset: 15329 samples\n", " train_aug_subset: 13796 samples\n" ] } ], "source": [ "# --- Create augmented training subset ---\n", "from torch.utils.data import Subset\n", "\n", "# Define augmentation transform (stronger than basic validation transform)\n", "train_transform = T.Compose([\n", " T.Resize((256, 256)),\n", " T.RandomHorizontalFlip(p=0.5),\n", " T.RandomRotation(degrees=5),\n", " T.ColorJitter(brightness=0.1, contrast=0.1),\n", " T.ToTensor(),\n", "])\n", "\n", "# Create full augmented training dataset\n", "train_dataset_full = SpectrumDataset(\n", " image_dir=\"data/pure_spectrum/images\",\n", " label_path=\"data/pure_spectrum/labels.json\",\n", " transform=train_transform # โ† Augmented!\n", ")\n", "\n", "# Take 90% for training (subset to avoid memory issues during dev)\n", "train_size = int(0.9 * len(train_dataset_full))\n", "train_aug_subset = Subset(train_dataset_full, range(train_size))\n", "\n", "print(f\"โœ… Augmented training subset created:\")\n", "print(f\" Full train dataset: {len(train_dataset_full)} samples\")\n", "print(f\" train_aug_subset: {len(train_aug_subset)} samples\")" ] }, { "cell_type": "code", "execution_count": 18, "id": "a7c8a65e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train base samples: 13796\n", "Val samples: 1533\n" ] } ], "source": [ "from torch.utils.data import DataLoader, random_split, Subset\n", "import torchvision.transforms as T\n", "\n", "# Validation / no-augmentation transform\n", "val_transform = T.Compose([\n", " T.Resize((256, 256)),\n", " T.ToTensor(),\n", "])\n", "\n", "# Full dataset for validation split\n", "full_dataset = SpectrumDataset(\n", " image_dir=\"data/pure_spectrum/images\",\n", " label_path=\"data/pure_spectrum/labels.json\",\n", " transform=val_transform\n", ")\n", "\n", "train_size = int(0.9 * len(full_dataset))\n", "val_size = len(full_dataset) - train_size\n", "\n", "train_dataset_base, val_dataset = random_split(full_dataset, [train_size, val_size])\n", "\n", "print(f\"Train base samples: {len(train_dataset_base)}\")\n", "print(f\"Val samples: {len(val_dataset)}\")" ] }, { "cell_type": "code", "execution_count": 19, "id": "3975dce3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐Ÿš€ FIXED LOADERS READY!\n", "Train batches: 863 | Val batches: 96\n", "Using decoupled weight decay\n", "Stage 1 Configuration:\n", " Epochs: 100\n", " Optimizer: Prodigy (lr=1.0, wd=0.01)\n", " Scheduler: CosineAnnealingLR (T_max=86300)\n", " Early stopping patience: 30\n" ] } ], "source": [ "# --- Stage 1 Hyperparameters ---\n", "BATCH_SIZE = 16\n", "\n", "train_loader = DataLoader(train_aug_subset, batch_size=BATCH_SIZE, shuffle=True,\n", " num_workers=0, pin_memory=False) # โ† 0 workers!\n", "val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False,\n", " num_workers=0, pin_memory=False)\n", "\n", "print(f\"๐Ÿš€ FIXED LOADERS READY!\")\n", "print(f\"Train batches: {len(train_loader)} | Val batches: {len(val_loader)}\")\n", "NUM_EPOCHS_STAGE1 = 100 \n", "PATIENCE = 30 \n", "\n", "# Prodigy optimizer โ€” parameter-free, just set lr=1.0\n", "optimizer = Prodigy(model.parameters(), lr=1.0, weight_decay=0.01)\n", "\n", "# Cosine annealing scheduler\n", "total_steps = NUM_EPOCHS_STAGE1 * len(train_loader)\n", "scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps)\n", "\n", "print(f\"Stage 1 Configuration:\")\n", "print(f\" Epochs: {NUM_EPOCHS_STAGE1}\")\n", "print(f\" Optimizer: Prodigy (lr=1.0, wd=0.01)\")\n", "print(f\" Scheduler: CosineAnnealingLR (T_max={total_steps})\")\n", "print(f\" Early stopping patience: {PATIENCE}\")" ] }, { "cell_type": "code", "execution_count": 20, "id": "e32469e3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐Ÿงช SINGLE BATCH TEST - Should finish instantly\n", "โœ… BATCH SHAPE OK: torch.Size([16, 3, 256, 256])\n", "โœ… FORWARD PASS OK: torch.Size([16, 3])\n", "โœ… LOSS OK: 0.2248\n", "๐ŸŽ‰ ALL SYSTEMS GO!\n" ] } ], "source": [ "print(\"๐Ÿงช SINGLE BATCH TEST - Should finish instantly\")\n", "model.train()\n", "imgs, labels = next(iter(train_loader))\n", "print(f\"โœ… BATCH SHAPE OK: {imgs.shape}\") # Must be [8, 3, 3, 256, 256]\n", "\n", "imgs, labels = imgs.to(device), labels.to(device)\n", "preds = model(imgs)\n", "print(f\"โœ… FORWARD PASS OK: {preds.shape}\") # [8, 3]\n", "loss = criterion(preds, labels)\n", "print(f\"โœ… LOSS OK: {loss.item():.4f}\")\n", "print(\"๐ŸŽ‰ ALL SYSTEMS GO!\")" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "# import gc\n", "# import os\n", "# import time\n", "# import torch\n", "# import matplotlib.pyplot as plt\n", "\n", "# # === CRITICAL VRAM RESET ===\n", "# torch.cuda.empty_cache()\n", "# gc.collect()\n", "# os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n", "\n", "# BATCH_SIZE = 8\n", "\n", "# print(\"=\" * 80)\n", "# print(\"๐Ÿš€ STAGE 1 TRAINING STARTED - LIVE MONITORING MODE\")\n", "# print(\"=\" * 80)\n", "\n", "# train_losses, val_losses, val_accuracies = [], [], []\n", "# best_val_loss, patience_counter = float('inf'), 0\n", "\n", "# # GPU memory monitoring\n", "# if device.type == 'cuda':\n", "# initial_mem = torch.cuda.memory_allocated() / 1e9\n", "# print(f\"๐Ÿ“Š Initial GPU memory: {initial_mem:.1f} GB\")\n", "# total_vram = torch.cuda.get_device_properties(0).total_memory / 1e9\n", "# else:\n", "# initial_mem, total_vram = 0, 0\n", "\n", "# print(f\"๐Ÿ“ˆ Train batches: {len(train_loader)} | Val batches: {len(val_loader)}\")\n", "# print()\n", "\n", "# # =====================================================\n", "# # ๐Ÿ”ด LIVE BATCH MONITORING - Shows progress EVERY 10 batches\n", "# # =====================================================\n", "# def train_one_epoch_live(model, loader, optimizer, scheduler, device, epoch):\n", "# model.train()\n", "# total_loss = 0.0\n", "# n_batches = 0\n", " \n", "# print(f\" {'Batch':<4} {'Loss':<8} {'Time':<6} {'GPU':<6} {'ETA'}\")\n", "# print(f\" {'-'*40}\")\n", " \n", "# batch_start = time.time()\n", "# for batch_idx, (imgs, labels) in enumerate(loader):\n", "# # === VRAM SAFETY CHECK ===\n", "# if device.type == 'cuda' and torch.cuda.memory_allocated() / 1e9 > 7.0:\n", "# torch.cuda.empty_cache()\n", " \n", "# imgs, labels = imgs.to(device, non_blocking=True), labels.to(device, non_blocking=True)\n", " \n", "# optimizer.zero_grad()\n", "# preds = model(imgs)\n", "# loss = criterion(preds, labels)\n", "# loss.backward()\n", "# optimizer.step()\n", "# if scheduler: scheduler.step()\n", " \n", "# total_loss += loss.item()\n", "# n_batches += 1\n", " \n", "# # === LIVE PROGRESS EVERY 10 BATCHES ===\n", "# if batch_idx % 10 == 0:\n", "# batch_time = time.time() - batch_start\n", "# batches_per_sec = batch_idx / batch_time if batch_time > 0 else 0\n", "# eta_epoch = (len(loader) - batch_idx) / batches_per_sec if batches_per_sec > 0 else 0\n", " \n", "# gpu_mem = torch.cuda.memory_allocated() / 1e9 if device.type == 'cuda' else 0\n", "# mem_usage = ((gpu_mem - initial_mem) / total_vram * 100) if total_vram > 0 else 0\n", " \n", "# print(f\" {batch_idx:<4} {loss.item():<8.4f} {batch_time/10:.1f}s {'{:>6.1f}%'.format(mem_usage):<6} \"\n", "# f\"{eta_epoch/60:.0f}m\")\n", " \n", "# avg_loss = total_loss / n_batches\n", "# print(f\" {'-'*40}\")\n", "# print(f\" โœ… Epoch {epoch} COMPLETE: {time.time()-batch_start:.0f}s total\")\n", "# return avg_loss\n", "\n", "# # =====================================================\n", "# # MAIN TRAINING LOOP WITH CONFIDENCE CHECKS\n", "# # =====================================================\n", "# for epoch in range(1, NUM_EPOCHS_STAGE1 + 1):\n", "\n", "# if epoch == 5:\n", "# print(\"๐Ÿ”“ Unfreezing backbone for fine-tuning...\")\n", "\n", "# for param in model.parameters():\n", "# param.requires_grad = True\n", "\n", "# optimizer = torch.optim.AdamW(\n", "# model.parameters(),\n", "# lr=3e-5,\n", "# weight_decay=1e-4\n", "# )\n", "\n", "# print(f\"\\n๐Ÿ”„ Epoch {epoch:3d}/{NUM_EPOCHS_STAGE1}\")\n", " \n", "# # ==================== TRAIN WITH LIVE MONITORING ====================\n", "# epoch_start = time.time()\n", "# train_loss = train_one_epoch_live(model, train_loader, optimizer, scheduler, device, epoch)\n", "# train_losses.append(train_loss)\n", " \n", "# # ==================== VALIDATE ====================\n", "# val_loss, val_acc = validate(model, val_loader, device)\n", "# val_losses.append(val_loss)\n", "# val_accuracies.append(val_acc)\n", " \n", "# # ==================== RESULTS ====================\n", "# epoch_time = time.time() - epoch_start\n", " \n", "# print(f\"โœ… EPOCH {epoch} SUMMARY:\")\n", "# print(f\" โฑ๏ธ Time: {epoch_time:.0f}s ({epoch_time/60:.1f}m)\")\n", "# print(f\" ๐Ÿ“‰ Loss: Train={train_loss:.5f} โ†’ Val={val_loss:.5f}\")\n", "# print(f\" ๐Ÿ“Š Acc: EC={val_acc['EC_acc']:4.1f}% EL={val_acc['EL_acc']:4.1f}% \"\n", "# f\"EJ={val_acc['EJ_acc']:4.1f}% Overall={val_acc['overall_acc']:4.1f}%\")\n", " \n", "# # ==================== CHECKPOINT ====================\n", "# if val_loss < best_val_loss:\n", "# best_val_loss = val_loss\n", "# patience_counter = 0\n", " \n", "# torch.save({\n", "# 'epoch': epoch, 'model_state_dict': model.state_dict(),\n", "# 'optimizer_state_dict': optimizer.state_dict(),\n", "# 'scheduler_state_dict': scheduler.state_dict(),\n", "# 'train_loss': train_loss, 'val_loss': val_loss, 'val_acc': val_acc,\n", "# }, 'models/checkpoints/stage1_best.pt')\n", "# print(f\" โญ SAVED NEW BEST! val_loss={val_loss:.5f}\")\n", "# else:\n", "# patience_counter += 1\n", "# if patience_counter % 5 == 0:\n", "# print(f\" โš ๏ธ No improvement for {patience_counter}/{PATIENCE} epochs\")\n", " \n", "# if patience_counter >= PATIENCE:\n", "# print(f\"\\n๐Ÿ EARLY STOPPING at epoch {epoch}\")\n", "# break\n", " \n", "# # ==================== ETA ====================\n", "# remaining_epochs = NUM_EPOCHS_STAGE1 - epoch\n", "# eta_total = epoch_time * remaining_epochs / 60\n", "# print(f\" โฑ๏ธ ETA remaining: {eta_total:.0f}m | Progress: \"\n", "# f\"[{'โ–ˆ' * min(epoch//3, 30)}{'โ–‘' * (30-min(epoch//3, 30))}]\")\n", "# print()\n", "\n", "# # ==================== FINAL SUMMARY ====================\n", "# print(\"=\" * 80)\n", "# print(\"๐ŸŽ‰ STAGE 1 TRAINING COMPLETE!\")\n", "# print(f\"๐Ÿ† BEST Validation Loss: {best_val_loss:.5f}\")\n", "# print(f\"โญ BEST Model saved: models/checkpoints/stage1_best.pt\")\n", "# print(f\"๐Ÿ“Š Final Acc: EC={val_accuracies[-1]['EC_acc']:.1f}% \"\n", "# f\"EL={val_accuracies[-1]['EL_acc']:.1f}% \"\n", "# f\"EJ={val_accuracies[-1]['EJ_acc']:.1f}% \"\n", "# f\"Overall={val_accuracies[-1]['overall_acc']:.1f}%\")\n", "# print(\"=\" * 80)\n", "\n", "# # Plot\n", "# plt.figure(figsize=(12, 4))\n", "# plt.subplot(1, 2, 1)\n", "# plt.plot(train_losses, label='Train Loss', alpha=0.8)\n", "# plt.plot(val_losses, label='Val Loss', alpha=0.8)\n", "# plt.yscale('log')\n", "# plt.xlabel('Epoch'); plt.ylabel('MSE Loss'); plt.legend(); plt.grid(True, alpha=0.3)\n", "# plt.title('Stage 1 Training Progress')\n", "\n", "# plt.subplot(1, 2, 2)\n", "# epochs_range = range(1, len(val_accuracies) + 1)\n", "# plt.plot(epochs_range, [a['overall_acc'] for a in val_accuracies], linewidth=3, label='Overall')\n", "# plt.plot(epochs_range, [a['EC_acc'] for a in val_accuracies], alpha=0.7, label='EC')\n", "# plt.plot(epochs_range, [a['EL_acc'] for a in val_accuracies], alpha=0.7, label='EL')\n", "# plt.plot(epochs_range, [a['EJ_acc'] for a in val_accuracies], alpha=0.7, label='EJ')\n", "# plt.xlabel('Epoch'); plt.ylabel('Accuracy (%)'); plt.legend(); plt.grid(True, alpha=0.3)\n", "# plt.title('Validation Accuracy')\n", "\n", "# plt.tight_layout()\n", "# plt.savefig('results/figures/stage1_training.png', dpi=150, bbox_inches='tight')\n", "# plt.show()" ] }, { "cell_type": "code", "execution_count": 22, "id": "0f793d79", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================================================================================\n", "๐Ÿš€ STAGE 1 TRAINING STARTED - LIVE MONITORING MODE\n", "================================================================================\n", "๐Ÿ“Š Initial GPU memory: 1.64 GB / 8.6 GB\n", "๐Ÿ“ˆ Train batches: 863 | Val batches: 96\n", "\n", "\n", "๐Ÿ”„ Epoch 1/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.2472 19.2% 1.5m\n", " 10 0.2530 19.2% 1.3m\n", " 20 0.2106 19.2% 1.3m\n", " 30 0.3079 19.2% 1.3m\n", " 40 0.2624 19.2% 1.3m\n", " 50 0.2414 19.2% 1.3m\n", " 60 0.2128 19.2% 1.3m\n", " 70 0.2891 19.2% 1.3m\n", " 80 0.2388 19.2% 1.3m\n", " 90 0.2552 19.2% 1.2m\n", " 100 0.2163 19.2% 1.2m\n", " 110 0.2621 19.2% 1.2m\n", " 120 0.2535 19.2% 1.2m\n", " 130 0.1826 19.2% 1.2m\n", " 140 0.2213 19.2% 1.1m\n", " 150 0.2754 19.2% 1.1m\n", " 160 0.2216 19.2% 1.1m\n", " 170 0.2112 19.2% 1.1m\n", " 180 0.1986 19.2% 1.1m\n", " 190 0.2261 19.2% 1.0m\n", " 200 0.1861 19.2% 1.0m\n", " 210 0.2357 19.2% 1.0m\n", " 220 0.2550 19.2% 1.0m\n", " 230 0.2148 19.2% 1.0m\n", " 240 0.2270 19.2% 1.0m\n", " 250 0.2278 19.2% 1.0m\n", " 260 0.2238 19.2% 0.9m\n", " 270 0.2087 19.2% 0.9m\n", " 280 0.2150 19.2% 0.9m\n", " 290 0.1921 19.2% 0.9m\n", " 300 0.2098 19.2% 0.9m\n", " 310 0.1830 19.2% 0.9m\n", " 320 0.1743 19.2% 0.8m\n", " 330 0.2499 19.2% 0.8m\n", " 340 0.2387 19.2% 0.8m\n", " 350 0.2276 19.2% 0.8m\n", " 360 0.2084 19.2% 0.8m\n", " 370 0.2172 19.2% 0.8m\n", " 380 0.2323 19.2% 0.7m\n", " 390 0.2215 19.2% 0.7m\n", " 400 0.2447 19.2% 0.7m\n", " 410 0.1710 19.2% 0.7m\n", " 420 0.2082 19.2% 0.7m\n", " 430 0.1806 19.2% 0.7m\n", " 440 0.2105 19.2% 0.7m\n", " 450 0.2332 19.2% 0.6m\n", " 460 0.1841 19.2% 0.6m\n", " 470 0.1738 19.2% 0.6m\n", " 480 0.1980 19.2% 0.6m\n", " 490 0.2546 19.2% 0.6m\n", " 500 0.2016 19.2% 0.6m\n", " 510 0.2215 19.2% 0.5m\n", " 520 0.2222 19.2% 0.5m\n", " 530 0.2472 19.2% 0.5m\n", " 540 0.1979 19.2% 0.5m\n", " 550 0.2304 19.2% 0.5m\n", " 560 0.2276 19.2% 0.5m\n", " 570 0.2253 19.2% 0.5m\n", " 580 0.2406 19.2% 0.4m\n", " 590 0.2102 19.2% 0.4m\n", " 600 0.1855 19.2% 0.4m\n", " 610 0.2386 19.2% 0.4m\n", " 620 0.2382 19.2% 0.4m\n", " 630 0.2180 19.2% 0.4m\n", " 640 0.1727 19.2% 0.3m\n", " 650 0.1503 19.2% 0.3m\n", " 660 0.1909 19.2% 0.3m\n", " 670 0.2351 19.2% 0.3m\n", " 680 0.2098 19.2% 0.3m\n", " 690 0.2044 19.2% 0.3m\n", " 700 0.1990 19.2% 0.3m\n", " 710 0.2280 19.2% 0.2m\n", " 720 0.2191 19.2% 0.2m\n", " 730 0.2016 19.2% 0.2m\n", " 740 0.2028 19.2% 0.2m\n", " 750 0.1706 19.2% 0.2m\n", " 760 0.1927 19.2% 0.2m\n", " 770 0.2189 19.2% 0.1m\n", " 780 0.2066 19.2% 0.1m\n", " 790 0.1971 19.2% 0.1m\n", " 800 0.1929 19.2% 0.1m\n", " 810 0.2116 19.2% 0.1m\n", " 820 0.2683 19.2% 0.1m\n", " 830 0.1682 19.2% 0.1m\n", " 840 0.2175 19.2% 0.0m\n", " 850 0.2405 19.2% 0.0m\n", " 860 0.2575 19.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 1 COMPLETE in 79s\n", "\n", "โœ… EPOCH 1 SUMMARY\n", " โฑ๏ธ Time: 87s (1.5m)\n", " ๐Ÿ“‰ Loss: Train=0.21985 โ†’ Val=0.19346\n", " ๐Ÿ“Š Acc: EC=77.3% EL=75.3% EJ=82.3% Overall=78.3%\n", " โญ NEW BEST MODEL SAVED (val_loss=0.19346)\n", " โฑ๏ธ Remaining ETA โ‰ˆ 144m\n", "\n", "๐Ÿ”„ Epoch 2/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1957 19.2% 1.3m\n", " 10 0.1924 19.2% 1.2m\n", " 20 0.2241 19.2% 1.3m\n", " 30 0.2031 19.2% 1.2m\n", " 40 0.2044 19.2% 1.2m\n", " 50 0.2263 19.2% 1.2m\n", " 60 0.1737 19.2% 1.2m\n", " 70 0.2003 19.2% 1.2m\n", " 80 0.2021 19.2% 1.2m\n", " 90 0.1890 19.2% 1.2m\n", " 100 0.2434 19.2% 1.1m\n", " 110 0.2150 19.2% 1.1m\n", " 120 0.1761 19.2% 1.1m\n", " 130 0.2068 19.2% 1.1m\n", " 140 0.2333 19.2% 1.1m\n", " 150 0.1633 19.2% 1.1m\n", " 160 0.2011 19.2% 1.0m\n", " 170 0.2208 19.2% 1.0m\n", " 180 0.2214 19.2% 1.0m\n", " 190 0.2023 19.2% 1.0m\n", " 200 0.1579 19.2% 1.0m\n", " 210 0.2278 19.2% 1.0m\n", " 220 0.2410 19.2% 1.0m\n", " 230 0.1984 19.2% 0.9m\n", " 240 0.2335 19.2% 0.9m\n", " 250 0.2127 19.2% 0.9m\n", " 260 0.2159 19.2% 0.9m\n", " 270 0.1875 19.2% 0.9m\n", " 280 0.2565 19.2% 0.9m\n", " 290 0.2090 19.2% 0.8m\n", " 300 0.1988 19.2% 0.8m\n", " 310 0.2123 19.2% 0.8m\n", " 320 0.2031 19.2% 0.8m\n", " 330 0.1956 19.2% 0.8m\n", " 340 0.1886 19.2% 0.8m\n", " 350 0.1824 19.2% 0.8m\n", " 360 0.2352 19.2% 0.7m\n", " 370 0.2007 19.2% 0.7m\n", " 380 0.1815 19.2% 0.7m\n", " 390 0.2212 19.2% 0.7m\n", " 400 0.1897 19.2% 0.7m\n", " 410 0.2151 19.2% 0.7m\n", " 420 0.2301 19.2% 0.7m\n", " 430 0.1826 19.2% 0.6m\n", " 440 0.1970 19.2% 0.6m\n", " 450 0.1973 19.2% 0.6m\n", " 460 0.1855 19.2% 0.6m\n", " 470 0.1907 19.2% 0.6m\n", " 480 0.1751 19.2% 0.6m\n", " 490 0.2161 19.2% 0.6m\n", " 500 0.2178 19.2% 0.5m\n", " 510 0.2260 19.2% 0.5m\n", " 520 0.2518 19.2% 0.5m\n", " 530 0.1946 19.2% 0.5m\n", " 540 0.2219 19.2% 0.5m\n", " 550 0.2079 19.2% 0.5m\n", " 560 0.2136 19.2% 0.4m\n", " 570 0.2298 19.2% 0.4m\n", " 580 0.2167 19.2% 0.4m\n", " 590 0.1925 19.2% 0.4m\n", " 600 0.2294 19.2% 0.4m\n", " 610 0.1767 19.2% 0.4m\n", " 620 0.2161 19.2% 0.4m\n", " 630 0.2409 19.2% 0.3m\n", " 640 0.1891 19.2% 0.3m\n", " 650 0.2312 19.2% 0.3m\n", " 660 0.2072 19.2% 0.3m\n", " 670 0.1964 19.2% 0.3m\n", " 680 0.1837 19.2% 0.3m\n", " 690 0.2415 19.2% 0.3m\n", " 700 0.2107 19.2% 0.2m\n", " 710 0.2132 19.2% 0.2m\n", " 720 0.2043 19.2% 0.2m\n", " 730 0.1911 19.2% 0.2m\n", " 740 0.1735 19.2% 0.2m\n", " 750 0.2563 19.2% 0.2m\n", " 760 0.1817 19.2% 0.2m\n", " 770 0.2153 19.2% 0.1m\n", " 780 0.1810 19.2% 0.1m\n", " 790 0.2070 19.2% 0.1m\n", " 800 0.1926 19.2% 0.1m\n", " 810 0.2020 19.2% 0.1m\n", " 820 0.2237 19.2% 0.1m\n", " 830 0.2141 19.2% 0.0m\n", " 840 0.1639 19.2% 0.0m\n", " 850 0.2139 19.2% 0.0m\n", " 860 0.2221 19.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 2 COMPLETE in 76s\n", "\n", "โœ… EPOCH 2 SUMMARY\n", " โฑ๏ธ Time: 84s (1.4m)\n", " ๐Ÿ“‰ Loss: Train=0.20724 โ†’ Val=0.18636\n", " ๐Ÿ“Š Acc: EC=78.4% EL=75.2% EJ=83.2% Overall=79.0%\n", " โญ NEW BEST MODEL SAVED (val_loss=0.18636)\n", " โฑ๏ธ Remaining ETA โ‰ˆ 138m\n", "\n", "๐Ÿ”„ Epoch 3/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1889 19.2% 1.3m\n", " 10 0.2106 19.2% 1.3m\n", " 20 0.1685 19.2% 1.3m\n", " 30 0.1724 19.2% 1.2m\n", " 40 0.2081 19.2% 1.2m\n", " 50 0.1747 19.2% 1.2m\n", " 60 0.2175 19.2% 1.2m\n", " 70 0.1841 19.2% 1.2m\n", " 80 0.1916 19.2% 1.2m\n", " 90 0.1906 19.2% 1.2m\n", " 100 0.2225 19.2% 1.1m\n", " 110 0.2124 19.2% 1.1m\n", " 120 0.1825 19.2% 1.1m\n", " 130 0.2043 19.2% 1.1m\n", " 140 0.2111 19.2% 1.1m\n", " 150 0.2374 19.2% 1.1m\n", " 160 0.2118 19.2% 1.0m\n", " 170 0.1914 19.2% 1.0m\n", " 180 0.2101 19.2% 1.0m\n", " 190 0.1893 19.2% 1.0m\n", " 200 0.2016 19.2% 1.0m\n", " 210 0.2081 19.2% 1.0m\n", " 220 0.1912 19.2% 1.0m\n", " 230 0.2012 19.2% 0.9m\n", " 240 0.2293 19.2% 0.9m\n", " 250 0.2005 19.2% 0.9m\n", " 260 0.2061 19.2% 0.9m\n", " 270 0.1731 19.2% 0.9m\n", " 280 0.2000 19.2% 0.9m\n", " 290 0.2016 19.2% 0.8m\n", " 300 0.2155 19.2% 0.8m\n", " 310 0.1786 19.2% 0.8m\n", " 320 0.1791 19.2% 0.8m\n", " 330 0.1749 19.2% 0.8m\n", " 340 0.1867 19.2% 0.8m\n", " 350 0.2014 19.2% 0.8m\n", " 360 0.2502 19.2% 0.7m\n", " 370 0.1833 19.2% 0.7m\n", " 380 0.1713 19.2% 0.7m\n", " 390 0.2069 19.2% 0.7m\n", " 400 0.2151 19.2% 0.7m\n", " 410 0.1956 19.2% 0.7m\n", " 420 0.2243 19.2% 0.7m\n", " 430 0.2419 19.2% 0.6m\n", " 440 0.2135 19.2% 0.6m\n", " 450 0.2303 19.2% 0.6m\n", " 460 0.2491 19.2% 0.6m\n", " 470 0.1884 19.2% 0.6m\n", " 480 0.2148 19.2% 0.6m\n", " 490 0.1923 19.2% 0.6m\n", " 500 0.2285 19.2% 0.5m\n", " 510 0.1824 19.2% 0.5m\n", " 520 0.1904 19.2% 0.5m\n", " 530 0.1852 19.2% 0.5m\n", " 540 0.1858 19.2% 0.5m\n", " 550 0.2038 19.2% 0.5m\n", " 560 0.1833 19.2% 0.4m\n", " 570 0.2278 19.2% 0.4m\n", " 580 0.1955 19.2% 0.4m\n", " 590 0.2186 19.2% 0.4m\n", " 600 0.2532 19.2% 0.4m\n", " 610 0.1682 19.2% 0.4m\n", " 620 0.2185 19.2% 0.4m\n", " 630 0.2217 19.2% 0.3m\n", " 640 0.2038 19.2% 0.3m\n", " 650 0.2187 19.2% 0.3m\n", " 660 0.1561 19.2% 0.3m\n", " 670 0.1932 19.2% 0.3m\n", " 680 0.1941 19.2% 0.3m\n", " 690 0.1747 19.2% 0.3m\n", " 700 0.2077 19.2% 0.2m\n", " 710 0.1857 19.2% 0.2m\n", " 720 0.1813 19.2% 0.2m\n", " 730 0.1908 19.2% 0.2m\n", " 740 0.1704 19.2% 0.2m\n", " 750 0.2433 19.2% 0.2m\n", " 760 0.1707 19.2% 0.2m\n", " 770 0.2054 19.2% 0.1m\n", " 780 0.1889 19.2% 0.1m\n", " 790 0.2201 19.2% 0.1m\n", " 800 0.1982 19.2% 0.1m\n", " 810 0.2110 19.2% 0.1m\n", " 820 0.2376 19.2% 0.1m\n", " 830 0.2255 19.2% 0.0m\n", " 840 0.2102 19.2% 0.0m\n", " 850 0.1913 19.2% 0.0m\n", " 860 0.1935 19.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 3 COMPLETE in 76s\n", "\n", "โœ… EPOCH 3 SUMMARY\n", " โฑ๏ธ Time: 84s (1.4m)\n", " ๐Ÿ“‰ Loss: Train=0.20445 โ†’ Val=0.19048\n", " ๐Ÿ“Š Acc: EC=77.6% EL=75.1% EJ=82.9% Overall=78.6%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 136m\n", "\n", "๐Ÿ”„ Epoch 4/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1941 19.2% 1.3m\n", " 10 0.1870 19.2% 1.2m\n", " 20 0.2053 19.2% 1.2m\n", " 30 0.1908 19.2% 1.2m\n", " 40 0.2362 19.2% 1.2m\n", " 50 0.1640 19.2% 1.2m\n", " 60 0.2297 19.2% 1.2m\n", " 70 0.2164 19.2% 1.2m\n", " 80 0.2061 19.2% 1.2m\n", " 90 0.2465 19.2% 1.1m\n", " 100 0.2115 19.2% 1.1m\n", " 110 0.2441 19.2% 1.1m\n", " 120 0.2018 19.2% 1.1m\n", " 130 0.2199 19.2% 1.1m\n", " 140 0.2079 19.2% 1.1m\n", " 150 0.2007 19.2% 1.0m\n", " 160 0.2301 19.2% 1.0m\n", " 170 0.1729 19.2% 1.0m\n", " 180 0.2324 19.2% 1.0m\n", " 190 0.2004 19.2% 1.0m\n", " 200 0.1640 19.2% 1.0m\n", " 210 0.2141 19.2% 1.0m\n", " 220 0.1790 19.2% 0.9m\n", " 230 0.2066 19.2% 0.9m\n", " 240 0.2202 19.2% 0.9m\n", " 250 0.2172 19.2% 0.9m\n", " 260 0.1974 19.2% 0.9m\n", " 270 0.1866 19.2% 0.9m\n", " 280 0.2331 19.2% 0.9m\n", " 290 0.2659 19.2% 0.8m\n", " 300 0.2369 19.2% 0.8m\n", " 310 0.1786 19.2% 0.8m\n", " 320 0.2020 19.2% 0.8m\n", " 330 0.1955 19.2% 0.8m\n", " 340 0.2216 19.2% 0.8m\n", " 350 0.1974 19.2% 0.8m\n", " 360 0.2357 19.2% 0.7m\n", " 370 0.2388 19.2% 0.7m\n", " 380 0.2122 19.2% 0.7m\n", " 390 0.2393 19.2% 0.7m\n", " 400 0.2170 19.2% 0.7m\n", " 410 0.2160 19.2% 0.7m\n", " 420 0.2353 19.2% 0.7m\n", " 430 0.2140 19.2% 0.6m\n", " 440 0.2248 19.2% 0.6m\n", " 450 0.2467 19.2% 0.6m\n", " 460 0.1934 19.2% 0.6m\n", " 470 0.1917 19.2% 0.6m\n", " 480 0.1795 19.2% 0.6m\n", " 490 0.2194 19.2% 0.5m\n", " 500 0.1613 19.2% 0.5m\n", " 510 0.1675 19.2% 0.5m\n", " 520 0.1758 19.2% 0.5m\n", " 530 0.2239 19.2% 0.5m\n", " 540 0.2261 19.2% 0.5m\n", " 550 0.2230 19.2% 0.5m\n", " 560 0.2120 19.2% 0.4m\n", " 570 0.2024 19.2% 0.4m\n", " 580 0.2100 19.2% 0.4m\n", " 590 0.2093 19.2% 0.4m\n", " 600 0.2004 19.2% 0.4m\n", " 610 0.2229 19.2% 0.4m\n", " 620 0.1803 19.2% 0.4m\n", " 630 0.1972 19.2% 0.3m\n", " 640 0.2400 19.2% 0.3m\n", " 650 0.2080 19.2% 0.3m\n", " 660 0.2110 19.2% 0.3m\n", " 670 0.2119 19.2% 0.3m\n", " 680 0.1772 19.2% 0.3m\n", " 690 0.2010 19.2% 0.3m\n", " 700 0.1864 19.2% 0.2m\n", " 710 0.2035 19.2% 0.2m\n", " 720 0.1645 19.2% 0.2m\n", " 730 0.1959 19.2% 0.2m\n", " 740 0.2193 19.2% 0.2m\n", " 750 0.2278 19.2% 0.2m\n", " 760 0.1874 19.2% 0.2m\n", " 770 0.2198 19.2% 0.1m\n", " 780 0.2554 19.2% 0.1m\n", " 790 0.1878 19.2% 0.1m\n", " 800 0.2034 19.2% 0.1m\n", " 810 0.1927 19.2% 0.1m\n", " 820 0.2278 19.2% 0.1m\n", " 830 0.1840 19.2% 0.0m\n", " 840 0.2607 19.2% 0.0m\n", " 850 0.1977 19.2% 0.0m\n", " 860 0.2148 19.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 4 COMPLETE in 76s\n", "\n", "โœ… EPOCH 4 SUMMARY\n", " โฑ๏ธ Time: 84s (1.4m)\n", " ๐Ÿ“‰ Loss: Train=0.20333 โ†’ Val=0.18967\n", " ๐Ÿ“Š Acc: EC=77.1% EL=75.4% EJ=83.4% Overall=78.6%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 134m\n", "๐Ÿ”“ Unfreezing backbone for fine-tuning\n", "\n", "๐Ÿ”„ Epoch 5/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1647 20.6% 2.7m\n", " 10 0.2265 23.2% 2.6m\n", " 20 0.2122 23.2% 2.6m\n", " 30 0.1643 23.2% 2.5m\n", " 40 0.1648 23.2% 2.5m\n", " 50 0.1915 23.2% 2.5m\n", " 60 0.2168 23.2% 2.4m\n", " 70 0.1688 23.2% 2.4m\n", " 80 0.1754 23.2% 2.4m\n", " 90 0.2131 23.2% 2.4m\n", " 100 0.2259 23.2% 2.3m\n", " 110 0.1676 23.2% 2.3m\n", " 120 0.1537 23.2% 2.3m\n", " 130 0.1873 23.2% 2.3m\n", " 140 0.1738 23.2% 2.2m\n", " 150 0.2028 23.2% 2.2m\n", " 160 0.1707 23.2% 2.2m\n", " 170 0.1613 23.2% 2.1m\n", " 180 0.1924 23.2% 2.1m\n", " 190 0.1458 23.2% 2.1m\n", " 200 0.2189 23.2% 2.0m\n", " 210 0.1808 23.2% 2.0m\n", " 220 0.1322 23.2% 2.0m\n", " 230 0.1389 23.2% 1.9m\n", " 240 0.1998 23.2% 1.9m\n", " 250 0.1621 23.2% 1.9m\n", " 260 0.1275 23.2% 1.8m\n", " 270 0.1741 23.2% 1.8m\n", " 280 0.1372 23.2% 1.8m\n", " 290 0.1768 23.2% 1.8m\n", " 300 0.1751 23.2% 1.7m\n", " 310 0.1716 23.2% 1.7m\n", " 320 0.1709 23.2% 1.7m\n", " 330 0.1710 23.2% 1.6m\n", " 340 0.1700 23.2% 1.6m\n", " 350 0.1885 23.2% 1.6m\n", " 360 0.1631 23.2% 1.5m\n", " 370 0.1928 23.2% 1.5m\n", " 380 0.1583 23.2% 1.5m\n", " 390 0.1921 23.2% 1.5m\n", " 400 0.1574 23.2% 1.4m\n", " 410 0.1638 23.2% 1.4m\n", " 420 0.2143 23.2% 1.4m\n", " 430 0.1529 23.2% 1.3m\n", " 440 0.1594 23.2% 1.3m\n", " 450 0.1604 23.2% 1.3m\n", " 460 0.1800 23.2% 1.2m\n", " 470 0.1400 23.2% 1.2m\n", " 480 0.2051 23.2% 1.2m\n", " 490 0.1685 23.2% 1.1m\n", " 500 0.1498 23.2% 1.1m\n", " 510 0.1781 23.2% 1.1m\n", " 520 0.1942 23.2% 1.1m\n", " 530 0.1812 23.2% 1.0m\n", " 540 0.1415 23.2% 1.0m\n", " 550 0.1788 23.2% 1.0m\n", " 560 0.1731 23.2% 0.9m\n", " 570 0.1748 23.2% 0.9m\n", " 580 0.1937 23.2% 0.9m\n", " 590 0.2392 23.2% 0.8m\n", " 600 0.1820 23.2% 0.8m\n", " 610 0.1918 23.2% 0.8m\n", " 620 0.1318 23.2% 0.7m\n", " 630 0.1313 23.2% 0.7m\n", " 640 0.1687 23.2% 0.7m\n", " 650 0.1414 23.2% 0.7m\n", " 660 0.1874 23.2% 0.6m\n", " 670 0.1427 23.2% 0.6m\n", " 680 0.1797 23.2% 0.6m\n", " 690 0.1713 23.2% 0.5m\n", " 700 0.1618 23.2% 0.5m\n", " 710 0.2211 23.2% 0.5m\n", " 720 0.1729 23.2% 0.4m\n", " 730 0.1626 23.2% 0.4m\n", " 740 0.1670 23.2% 0.4m\n", " 750 0.2094 23.2% 0.3m\n", " 760 0.1432 23.2% 0.3m\n", " 770 0.1765 23.2% 0.3m\n", " 780 0.1197 23.2% 0.3m\n", " 790 0.1961 23.2% 0.2m\n", " 800 0.1428 23.2% 0.2m\n", " 810 0.1643 23.2% 0.2m\n", " 820 0.2214 23.2% 0.1m\n", " 830 0.1887 23.2% 0.1m\n", " 840 0.1608 23.2% 0.1m\n", " 850 0.1819 23.2% 0.0m\n", " 860 0.1558 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 5 COMPLETE in 160s\n", "\n", "โœ… EPOCH 5 SUMMARY\n", " โฑ๏ธ Time: 168s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.17745 โ†’ Val=0.16768\n", " ๐Ÿ“Š Acc: EC=81.2% EL=75.4% EJ=86.0% Overall=80.9%\n", " โญ NEW BEST MODEL SAVED (val_loss=0.16768)\n", " โฑ๏ธ Remaining ETA โ‰ˆ 266m\n", "\n", "๐Ÿ”„ Epoch 6/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1224 23.2% 2.7m\n", " 10 0.2069 23.2% 2.7m\n", " 20 0.1790 23.2% 2.6m\n", " 30 0.1669 23.2% 2.6m\n", " 40 0.1552 23.2% 2.5m\n", " 50 0.1759 23.2% 2.5m\n", " 60 0.1684 23.2% 2.5m\n", " 70 0.2020 23.2% 2.5m\n", " 80 0.1706 23.2% 2.4m\n", " 90 0.1793 23.2% 2.4m\n", " 100 0.1473 23.2% 2.4m\n", " 110 0.1710 23.2% 2.4m\n", " 120 0.1773 23.2% 2.3m\n", " 130 0.1645 23.2% 2.3m\n", " 140 0.1725 23.2% 2.3m\n", " 150 0.1565 23.2% 2.2m\n", " 160 0.1655 23.2% 2.2m\n", " 170 0.1797 23.2% 2.2m\n", " 180 0.1860 23.2% 2.1m\n", " 190 0.1225 23.2% 2.1m\n", " 200 0.1517 23.2% 2.1m\n", " 210 0.1820 23.2% 2.0m\n", " 220 0.2083 23.2% 2.0m\n", " 230 0.1628 23.2% 2.0m\n", " 240 0.1466 23.2% 2.0m\n", " 250 0.2110 23.2% 1.9m\n", " 260 0.2070 23.2% 1.9m\n", " 270 0.1650 23.2% 1.9m\n", " 280 0.1719 23.2% 1.8m\n", " 290 0.1572 23.2% 1.8m\n", " 300 0.1631 23.2% 1.8m\n", " 310 0.1570 23.2% 1.7m\n", " 320 0.1614 23.2% 1.7m\n", " 330 0.1804 23.2% 1.7m\n", " 340 0.1464 23.2% 1.6m\n", " 350 0.1767 23.2% 1.6m\n", " 360 0.1653 23.2% 1.6m\n", " 370 0.1973 23.2% 1.5m\n", " 380 0.1688 23.2% 1.5m\n", " 390 0.1952 23.2% 1.5m\n", " 400 0.1667 23.2% 1.4m\n", " 410 0.1650 23.2% 1.4m\n", " 420 0.1923 23.2% 1.4m\n", " 430 0.1600 23.2% 1.4m\n", " 440 0.1673 23.2% 1.3m\n", " 450 0.1230 23.2% 1.3m\n", " 460 0.1602 23.2% 1.3m\n", " 470 0.1792 23.2% 1.2m\n", " 480 0.1338 23.2% 1.2m\n", " 490 0.1822 23.2% 1.2m\n", " 500 0.1925 23.2% 1.1m\n", " 510 0.1782 23.2% 1.1m\n", " 520 0.1841 23.2% 1.1m\n", " 530 0.1302 23.2% 1.0m\n", " 540 0.1160 23.2% 1.0m\n", " 550 0.1820 23.2% 1.0m\n", " 560 0.2117 23.2% 0.9m\n", " 570 0.1935 23.2% 0.9m\n", " 580 0.1783 23.2% 0.9m\n", " 590 0.1795 23.2% 0.9m\n", " 600 0.1757 23.2% 0.8m\n", " 610 0.1738 23.2% 0.8m\n", " 620 0.1989 23.2% 0.8m\n", " 630 0.1435 23.2% 0.7m\n", " 640 0.1543 23.2% 0.7m\n", " 650 0.1546 23.2% 0.7m\n", " 660 0.1883 23.2% 0.6m\n", " 670 0.1533 23.2% 0.6m\n", " 680 0.1625 23.2% 0.6m\n", " 690 0.1613 23.2% 0.5m\n", " 700 0.1968 23.2% 0.5m\n", " 710 0.1474 23.2% 0.5m\n", " 720 0.1448 23.2% 0.4m\n", " 730 0.1878 23.2% 0.4m\n", " 740 0.1963 23.2% 0.4m\n", " 750 0.1499 23.2% 0.4m\n", " 760 0.1782 23.2% 0.3m\n", " 770 0.1655 23.2% 0.3m\n", " 780 0.1742 23.2% 0.3m\n", " 790 0.1271 23.2% 0.2m\n", " 800 0.1560 23.2% 0.2m\n", " 810 0.1827 23.2% 0.2m\n", " 820 0.2088 23.2% 0.1m\n", " 830 0.1657 23.2% 0.1m\n", " 840 0.1881 23.2% 0.1m\n", " 850 0.1889 23.2% 0.0m\n", " 860 0.2020 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 6 COMPLETE in 162s\n", "\n", "โœ… EPOCH 6 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.17035 โ†’ Val=0.16544\n", " ๐Ÿ“Š Acc: EC=81.3% EL=75.3% EJ=86.7% Overall=81.1%\n", " โญ NEW BEST MODEL SAVED (val_loss=0.16544)\n", " โฑ๏ธ Remaining ETA โ‰ˆ 266m\n", "\n", "๐Ÿ”„ Epoch 7/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1527 23.2% 2.7m\n", " 10 0.1547 23.2% 2.7m\n", " 20 0.1891 23.2% 2.6m\n", " 30 0.1783 23.2% 2.6m\n", " 40 0.1913 23.2% 2.6m\n", " 50 0.1636 23.2% 2.6m\n", " 60 0.1826 23.2% 2.5m\n", " 70 0.2061 23.2% 2.5m\n", " 80 0.1859 23.2% 2.5m\n", " 90 0.1370 23.2% 2.4m\n", " 100 0.1648 23.2% 2.4m\n", " 110 0.1569 23.2% 2.4m\n", " 120 0.1455 23.2% 2.3m\n", " 130 0.2262 23.2% 2.3m\n", " 140 0.1581 23.2% 2.3m\n", " 150 0.1313 23.2% 2.2m\n", " 160 0.1886 23.2% 2.2m\n", " 170 0.1827 23.2% 2.2m\n", " 180 0.1788 23.2% 2.1m\n", " 190 0.1826 23.2% 2.1m\n", " 200 0.1542 23.2% 2.1m\n", " 210 0.1719 23.2% 2.0m\n", " 220 0.1915 23.2% 2.0m\n", " 230 0.1539 23.2% 2.0m\n", " 240 0.1461 23.2% 1.9m\n", " 250 0.1604 23.2% 1.9m\n", " 260 0.1411 23.2% 1.9m\n", " 270 0.1768 23.2% 1.8m\n", " 280 0.1726 23.2% 1.8m\n", " 290 0.1375 23.2% 1.8m\n", " 300 0.1597 23.2% 1.7m\n", " 310 0.1941 23.2% 1.7m\n", " 320 0.1386 23.2% 1.7m\n", " 330 0.1929 23.2% 1.7m\n", " 340 0.1490 23.2% 1.6m\n", " 350 0.1856 23.2% 1.6m\n", " 360 0.1838 23.2% 1.6m\n", " 370 0.2080 23.2% 1.5m\n", " 380 0.1830 23.2% 1.5m\n", " 390 0.2111 23.2% 1.5m\n", " 400 0.1426 23.2% 1.4m\n", " 410 0.1630 23.2% 1.4m\n", " 420 0.1643 23.2% 1.4m\n", " 430 0.1377 23.2% 1.3m\n", " 440 0.1500 23.2% 1.3m\n", " 450 0.2040 23.2% 1.3m\n", " 460 0.1881 23.2% 1.2m\n", " 470 0.1708 23.2% 1.2m\n", " 480 0.2086 23.2% 1.2m\n", " 490 0.1470 23.2% 1.2m\n", " 500 0.1580 23.2% 1.1m\n", " 510 0.1527 23.2% 1.1m\n", " 520 0.1878 23.2% 1.1m\n", " 530 0.1865 23.2% 1.0m\n", " 540 0.1688 23.2% 1.0m\n", " 550 0.1740 23.2% 1.0m\n", " 560 0.1454 23.2% 0.9m\n", " 570 0.1439 23.2% 0.9m\n", " 580 0.1491 23.2% 0.9m\n", " 590 0.1763 23.2% 0.8m\n", " 600 0.1415 23.2% 0.8m\n", " 610 0.2339 23.2% 0.8m\n", " 620 0.1749 23.2% 0.8m\n", " 630 0.1545 23.2% 0.7m\n", " 640 0.1543 23.2% 0.7m\n", " 650 0.1541 23.2% 0.7m\n", " 660 0.1424 23.2% 0.6m\n", " 670 0.1550 23.2% 0.6m\n", " 680 0.1654 23.2% 0.6m\n", " 690 0.1821 23.2% 0.5m\n", " 700 0.2063 23.2% 0.5m\n", " 710 0.1811 23.2% 0.5m\n", " 720 0.1990 23.2% 0.4m\n", " 730 0.1541 23.2% 0.4m\n", " 740 0.1697 23.2% 0.4m\n", " 750 0.1705 23.2% 0.4m\n", " 760 0.1930 23.2% 0.3m\n", " 770 0.1469 23.2% 0.3m\n", " 780 0.1646 23.2% 0.3m\n", " 790 0.1629 23.2% 0.2m\n", " 800 0.1858 23.2% 0.2m\n", " 810 0.1742 23.2% 0.2m\n", " 820 0.1652 23.2% 0.1m\n", " 830 0.1601 23.2% 0.1m\n", " 840 0.1278 23.2% 0.1m\n", " 850 0.1747 23.2% 0.0m\n", " 860 0.1841 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 7 COMPLETE in 160s\n", "\n", "โœ… EPOCH 7 SUMMARY\n", " โฑ๏ธ Time: 169s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16914 โ†’ Val=0.16483\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.3% EJ=86.6% Overall=81.2%\n", " โญ NEW BEST MODEL SAVED (val_loss=0.16483)\n", " โฑ๏ธ Remaining ETA โ‰ˆ 261m\n", "\n", "๐Ÿ”„ Epoch 8/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1742 23.2% 2.7m\n", " 10 0.1522 23.2% 2.7m\n", " 20 0.1706 23.2% 2.7m\n", " 30 0.1478 23.2% 2.6m\n", " 40 0.1669 23.2% 2.6m\n", " 50 0.1899 23.2% 2.5m\n", " 60 0.1736 23.2% 2.5m\n", " 70 0.1573 23.2% 2.5m\n", " 80 0.1481 23.2% 2.4m\n", " 90 0.2096 23.2% 2.4m\n", " 100 0.1901 23.2% 2.4m\n", " 110 0.1602 23.2% 2.3m\n", " 120 0.1652 23.2% 2.3m\n", " 130 0.1901 23.2% 2.3m\n", " 140 0.0976 23.2% 2.3m\n", " 150 0.1770 23.2% 2.2m\n", " 160 0.1776 23.2% 2.2m\n", " 170 0.1605 23.2% 2.2m\n", " 180 0.1729 23.2% 2.1m\n", " 190 0.1597 23.2% 2.1m\n", " 200 0.1834 23.2% 2.1m\n", " 210 0.1644 23.2% 2.0m\n", " 220 0.1683 23.2% 2.0m\n", " 230 0.1616 23.2% 2.0m\n", " 240 0.1931 23.2% 1.9m\n", " 250 0.1750 23.2% 1.9m\n", " 260 0.2016 23.2% 1.9m\n", " 270 0.1975 23.2% 1.9m\n", " 280 0.1892 23.2% 1.8m\n", " 290 0.1634 23.2% 1.8m\n", " 300 0.1873 23.2% 1.8m\n", " 310 0.1670 23.2% 1.7m\n", " 320 0.1430 23.2% 1.7m\n", " 330 0.1718 23.2% 1.7m\n", " 340 0.1747 23.2% 1.6m\n", " 350 0.2115 23.2% 1.6m\n", " 360 0.1595 23.2% 1.6m\n", " 370 0.1721 23.2% 1.5m\n", " 380 0.1878 23.2% 1.5m\n", " 390 0.1661 23.2% 1.5m\n", " 400 0.1870 23.2% 1.4m\n", " 410 0.1861 23.2% 1.4m\n", " 420 0.1335 23.2% 1.4m\n", " 430 0.1474 23.2% 1.4m\n", " 440 0.1445 23.2% 1.3m\n", " 450 0.1833 23.2% 1.3m\n", " 460 0.2387 23.2% 1.3m\n", " 470 0.1802 23.2% 1.2m\n", " 480 0.1716 23.2% 1.2m\n", " 490 0.1594 23.2% 1.2m\n", " 500 0.2219 23.2% 1.1m\n", " 510 0.1742 23.2% 1.1m\n", " 520 0.1777 23.2% 1.1m\n", " 530 0.2004 23.2% 1.0m\n", " 540 0.1726 23.2% 1.0m\n", " 550 0.1769 23.2% 1.0m\n", " 560 0.1660 23.2% 0.9m\n", " 570 0.1448 23.2% 0.9m\n", " 580 0.1389 23.2% 0.9m\n", " 590 0.2252 23.2% 0.9m\n", " 600 0.1344 23.2% 0.8m\n", " 610 0.1604 23.2% 0.8m\n", " 620 0.1626 23.2% 0.8m\n", " 630 0.1546 23.2% 0.7m\n", " 640 0.1575 23.2% 0.7m\n", " 650 0.1731 23.2% 0.7m\n", " 660 0.1365 23.2% 0.6m\n", " 670 0.1499 23.2% 0.6m\n", " 680 0.1679 23.2% 0.6m\n", " 690 0.1781 23.2% 0.5m\n", " 700 0.1454 23.2% 0.5m\n", " 710 0.1717 23.2% 0.5m\n", " 720 0.1881 23.2% 0.4m\n", " 730 0.1569 23.2% 0.4m\n", " 740 0.1837 23.2% 0.4m\n", " 750 0.1454 23.2% 0.4m\n", " 760 0.1800 23.2% 0.3m\n", " 770 0.1911 23.2% 0.3m\n", " 780 0.1956 23.2% 0.3m\n", " 790 0.1554 23.2% 0.2m\n", " 800 0.2131 23.2% 0.2m\n", " 810 0.1367 23.2% 0.2m\n", " 820 0.1910 23.2% 0.1m\n", " 830 0.1583 23.2% 0.1m\n", " 840 0.1837 23.2% 0.1m\n", " 850 0.1243 23.2% 0.0m\n", " 860 0.1725 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 8 COMPLETE in 162s\n", "\n", "โœ… EPOCH 8 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16872 โ†’ Val=0.16462\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.3% EJ=86.7% Overall=81.2%\n", " โญ NEW BEST MODEL SAVED (val_loss=0.16462)\n", " โฑ๏ธ Remaining ETA โ‰ˆ 260m\n", "\n", "๐Ÿ”„ Epoch 9/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1987 23.2% 2.6m\n", " 10 0.1602 23.2% 2.7m\n", " 20 0.1346 23.2% 2.6m\n", " 30 0.1503 23.2% 2.6m\n", " 40 0.2123 23.2% 2.6m\n", " 50 0.1551 23.2% 2.5m\n", " 60 0.1605 23.2% 2.5m\n", " 70 0.1789 23.2% 2.5m\n", " 80 0.2188 23.2% 2.5m\n", " 90 0.2130 23.2% 2.4m\n", " 100 0.1747 23.2% 2.4m\n", " 110 0.1931 23.2% 2.4m\n", " 120 0.1262 23.2% 2.3m\n", " 130 0.1677 23.2% 2.3m\n", " 140 0.1761 23.2% 2.3m\n", " 150 0.1845 23.2% 2.2m\n", " 160 0.1625 23.2% 2.2m\n", " 170 0.2198 23.2% 2.2m\n", " 180 0.2066 23.2% 2.1m\n", " 190 0.1935 23.2% 2.1m\n", " 200 0.1487 23.2% 2.1m\n", " 210 0.1492 23.2% 2.1m\n", " 220 0.1675 23.2% 2.0m\n", " 230 0.1370 23.2% 2.0m\n", " 240 0.1846 23.2% 2.0m\n", " 250 0.1908 23.2% 1.9m\n", " 260 0.1714 23.2% 1.9m\n", " 270 0.2204 23.2% 1.9m\n", " 280 0.1506 23.2% 1.8m\n", " 290 0.1656 23.2% 1.8m\n", " 300 0.1809 23.2% 1.8m\n", " 310 0.1823 23.2% 1.7m\n", " 320 0.2135 23.2% 1.7m\n", " 330 0.1683 23.2% 1.7m\n", " 340 0.1414 23.2% 1.6m\n", " 350 0.1525 23.2% 1.6m\n", " 360 0.1418 23.2% 1.6m\n", " 370 0.1615 23.2% 1.5m\n", " 380 0.1519 23.2% 1.5m\n", " 390 0.1542 23.2% 1.5m\n", " 400 0.1671 23.2% 1.4m\n", " 410 0.1447 23.2% 1.4m\n", " 420 0.1919 23.2% 1.4m\n", " 430 0.1775 23.2% 1.4m\n", " 440 0.1292 23.2% 1.3m\n", " 450 0.1705 23.2% 1.3m\n", " 460 0.1612 23.2% 1.3m\n", " 470 0.1411 23.2% 1.2m\n", " 480 0.1811 23.2% 1.2m\n", " 490 0.1568 23.2% 1.2m\n", " 500 0.1620 23.2% 1.1m\n", " 510 0.1325 23.2% 1.1m\n", " 520 0.1648 23.2% 1.1m\n", " 530 0.1879 23.2% 1.0m\n", " 540 0.1389 23.2% 1.0m\n", " 550 0.2039 23.2% 1.0m\n", " 560 0.1540 23.2% 0.9m\n", " 570 0.1761 23.2% 0.9m\n", " 580 0.1954 23.2% 0.9m\n", " 590 0.1674 23.2% 0.9m\n", " 600 0.1364 23.2% 0.8m\n", " 610 0.1759 23.2% 0.8m\n", " 620 0.1405 23.2% 0.8m\n", " 630 0.1626 23.2% 0.7m\n", " 640 0.1465 23.2% 0.7m\n", " 650 0.1625 23.2% 0.7m\n", " 660 0.1322 23.2% 0.6m\n", " 670 0.1865 23.2% 0.6m\n", " 680 0.1678 23.2% 0.6m\n", " 690 0.1984 23.2% 0.5m\n", " 700 0.1849 23.2% 0.5m\n", " 710 0.1475 23.2% 0.5m\n", " 720 0.1596 23.2% 0.4m\n", " 730 0.1622 23.2% 0.4m\n", " 740 0.1518 23.2% 0.4m\n", " 750 0.1639 23.2% 0.4m\n", " 760 0.1780 23.2% 0.3m\n", " 770 0.1538 23.2% 0.3m\n", " 780 0.1435 23.2% 0.3m\n", " 790 0.2093 23.2% 0.2m\n", " 800 0.1631 23.2% 0.2m\n", " 810 0.1409 23.2% 0.2m\n", " 820 0.1543 23.2% 0.1m\n", " 830 0.2182 23.2% 0.1m\n", " 840 0.1900 23.2% 0.1m\n", " 850 0.1487 23.2% 0.0m\n", " 860 0.1688 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 9 COMPLETE in 162s\n", "\n", "โœ… EPOCH 9 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16827 โ†’ Val=0.16457\n", " ๐Ÿ“Š Acc: EC=81.5% EL=75.4% EJ=86.7% Overall=81.2%\n", " โญ NEW BEST MODEL SAVED (val_loss=0.16457)\n", " โฑ๏ธ Remaining ETA โ‰ˆ 259m\n", "\n", "๐Ÿ”„ Epoch 10/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.2100 23.2% 2.9m\n", " 10 0.1349 23.2% 2.7m\n", " 20 0.2062 23.2% 2.6m\n", " 30 0.1748 23.2% 2.6m\n", " 40 0.2162 23.2% 2.6m\n", " 50 0.1512 23.2% 2.5m\n", " 60 0.1605 23.2% 2.5m\n", " 70 0.1274 23.2% 2.5m\n", " 80 0.1513 23.2% 2.4m\n", " 90 0.1661 23.2% 2.4m\n", " 100 0.1788 23.2% 2.4m\n", " 110 0.1581 23.2% 2.3m\n", " 120 0.2010 23.2% 2.3m\n", " 130 0.2075 23.2% 2.3m\n", " 140 0.1675 23.2% 2.3m\n", " 150 0.1771 23.2% 2.2m\n", " 160 0.1508 23.2% 2.2m\n", " 170 0.1531 23.2% 2.2m\n", " 180 0.1819 23.2% 2.1m\n", " 190 0.1734 23.2% 2.1m\n", " 200 0.1534 23.2% 2.1m\n", " 210 0.1805 23.2% 2.0m\n", " 220 0.2114 23.2% 2.0m\n", " 230 0.1469 23.2% 2.0m\n", " 240 0.1779 23.2% 1.9m\n", " 250 0.1907 23.2% 1.9m\n", " 260 0.1590 23.2% 1.9m\n", " 270 0.1864 23.2% 1.8m\n", " 280 0.1716 23.2% 1.8m\n", " 290 0.1662 23.2% 1.8m\n", " 300 0.2090 23.2% 1.8m\n", " 310 0.1423 23.2% 1.7m\n", " 320 0.1638 23.2% 1.7m\n", " 330 0.1921 23.2% 1.7m\n", " 340 0.1450 23.2% 1.6m\n", " 350 0.0950 23.2% 1.6m\n", " 360 0.1588 23.2% 1.6m\n", " 370 0.1827 23.2% 1.5m\n", " 380 0.1529 23.2% 1.5m\n", " 390 0.1517 23.2% 1.5m\n", " 400 0.1330 23.2% 1.4m\n", " 410 0.1471 23.2% 1.4m\n", " 420 0.1544 23.2% 1.4m\n", " 430 0.1969 23.2% 1.3m\n", " 440 0.1895 23.2% 1.3m\n", " 450 0.1822 23.2% 1.3m\n", " 460 0.1488 23.2% 1.3m\n", " 470 0.1775 23.2% 1.2m\n", " 480 0.1643 23.2% 1.2m\n", " 490 0.1765 23.2% 1.2m\n", " 500 0.2084 23.2% 1.1m\n", " 510 0.1585 23.2% 1.1m\n", " 520 0.1718 23.2% 1.1m\n", " 530 0.1493 23.2% 1.0m\n", " 540 0.1809 23.2% 1.0m\n", " 550 0.1897 23.2% 1.0m\n", " 560 0.1753 23.2% 0.9m\n", " 570 0.1647 23.2% 0.9m\n", " 580 0.1941 23.2% 0.9m\n", " 590 0.1590 23.2% 0.9m\n", " 600 0.1255 23.2% 0.8m\n", " 610 0.1601 23.2% 0.8m\n", " 620 0.1754 23.2% 0.8m\n", " 630 0.1607 23.2% 0.7m\n", " 640 0.1518 23.2% 0.7m\n", " 650 0.1944 23.2% 0.7m\n", " 660 0.1893 23.2% 0.6m\n", " 670 0.1472 23.2% 0.6m\n", " 680 0.1599 23.2% 0.6m\n", " 690 0.1550 23.2% 0.5m\n", " 700 0.2074 23.2% 0.5m\n", " 710 0.1710 23.2% 0.5m\n", " 720 0.1407 23.2% 0.4m\n", " 730 0.1582 23.2% 0.4m\n", " 740 0.1903 23.2% 0.4m\n", " 750 0.1845 23.2% 0.4m\n", " 760 0.1785 23.2% 0.3m\n", " 770 0.1534 23.2% 0.3m\n", " 780 0.1850 23.2% 0.3m\n", " 790 0.1808 23.2% 0.2m\n", " 800 0.1526 23.2% 0.2m\n", " 810 0.1536 23.2% 0.2m\n", " 820 0.1552 23.2% 0.1m\n", " 830 0.1570 23.2% 0.1m\n", " 840 0.1532 23.2% 0.1m\n", " 850 0.1726 23.2% 0.0m\n", " 860 0.1579 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 10 COMPLETE in 162s\n", "\n", "โœ… EPOCH 10 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16809 โ†’ Val=0.16463\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.3% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 255m\n", "\n", "๐Ÿ”„ Epoch 11/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1571 23.2% 2.7m\n", " 10 0.1832 23.2% 2.7m\n", " 20 0.1595 23.2% 2.7m\n", " 30 0.1742 23.2% 2.6m\n", " 40 0.1385 23.2% 2.6m\n", " 50 0.1570 23.2% 2.6m\n", " 60 0.1732 23.2% 2.5m\n", " 70 0.1712 23.2% 2.5m\n", " 80 0.1352 23.2% 2.5m\n", " 90 0.1722 23.2% 2.4m\n", " 100 0.1781 23.2% 2.4m\n", " 110 0.1554 23.2% 2.4m\n", " 120 0.1675 23.2% 2.3m\n", " 130 0.1578 23.2% 2.3m\n", " 140 0.1515 23.2% 2.3m\n", " 150 0.1746 23.2% 2.2m\n", " 160 0.1826 23.2% 2.2m\n", " 170 0.1449 23.2% 2.2m\n", " 180 0.1769 23.2% 2.1m\n", " 190 0.1862 23.2% 2.1m\n", " 200 0.1338 23.2% 2.1m\n", " 210 0.1800 23.2% 2.1m\n", " 220 0.1755 23.2% 2.0m\n", " 230 0.1567 23.2% 2.0m\n", " 240 0.1785 23.2% 2.0m\n", " 250 0.1805 23.2% 1.9m\n", " 260 0.2086 23.2% 1.9m\n", " 270 0.1576 23.2% 1.9m\n", " 280 0.2110 23.2% 1.8m\n", " 290 0.1933 23.2% 1.8m\n", " 300 0.1869 23.2% 1.8m\n", " 310 0.2021 23.2% 1.7m\n", " 320 0.1810 23.2% 1.7m\n", " 330 0.2239 23.2% 1.7m\n", " 340 0.1622 23.2% 1.6m\n", " 350 0.1429 23.2% 1.6m\n", " 360 0.1276 23.2% 1.6m\n", " 370 0.1675 23.2% 1.5m\n", " 380 0.1540 23.2% 1.5m\n", " 390 0.1616 23.2% 1.5m\n", " 400 0.1523 23.2% 1.4m\n", " 410 0.1408 23.2% 1.4m\n", " 420 0.1746 23.2% 1.4m\n", " 430 0.1775 23.2% 1.4m\n", " 440 0.1835 23.2% 1.3m\n", " 450 0.1875 23.2% 1.3m\n", " 460 0.1665 23.2% 1.3m\n", " 470 0.1855 23.2% 1.2m\n", " 480 0.1686 23.2% 1.2m\n", " 490 0.1950 23.2% 1.2m\n", " 500 0.2058 23.2% 1.1m\n", " 510 0.1265 23.2% 1.1m\n", " 520 0.1567 23.2% 1.1m\n", " 530 0.1556 23.2% 1.0m\n", " 540 0.1939 23.2% 1.0m\n", " 550 0.1636 23.2% 1.0m\n", " 560 0.1621 23.2% 0.9m\n", " 570 0.1722 23.2% 0.9m\n", " 580 0.1622 23.2% 0.9m\n", " 590 0.2033 23.2% 0.9m\n", " 600 0.1561 23.2% 0.8m\n", " 610 0.1355 23.2% 0.8m\n", " 620 0.1950 23.2% 0.8m\n", " 630 0.1973 23.2% 0.7m\n", " 640 0.1441 23.2% 0.7m\n", " 650 0.2151 23.2% 0.7m\n", " 660 0.1776 23.2% 0.6m\n", " 670 0.1530 23.2% 0.6m\n", " 680 0.1591 23.2% 0.6m\n", " 690 0.2029 23.2% 0.5m\n", " 700 0.1803 23.2% 0.5m\n", " 710 0.1785 23.2% 0.5m\n", " 720 0.1845 23.2% 0.4m\n", " 730 0.1525 23.2% 0.4m\n", " 740 0.1814 23.2% 0.4m\n", " 750 0.1865 23.2% 0.4m\n", " 760 0.1612 23.2% 0.3m\n", " 770 0.1588 23.2% 0.3m\n", " 780 0.2003 23.2% 0.3m\n", " 790 0.1841 23.2% 0.2m\n", " 800 0.1485 23.2% 0.2m\n", " 810 0.1531 23.2% 0.2m\n", " 820 0.1871 23.2% 0.1m\n", " 830 0.1916 23.2% 0.1m\n", " 840 0.1659 23.2% 0.1m\n", " 850 0.1619 23.2% 0.0m\n", " 860 0.1805 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 11 COMPLETE in 162s\n", "\n", "โœ… EPOCH 11 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16840 โ†’ Val=0.16673\n", " ๐Ÿ“Š Acc: EC=81.2% EL=75.4% EJ=86.3% Overall=81.0%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 252m\n", "\n", "๐Ÿ”„ Epoch 12/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1480 23.2% 2.8m\n", " 10 0.1615 23.2% 2.6m\n", " 20 0.1849 23.2% 2.6m\n", " 30 0.1763 23.2% 2.6m\n", " 40 0.1607 23.2% 2.6m\n", " 50 0.1781 23.2% 2.5m\n", " 60 0.1617 23.2% 2.5m\n", " 70 0.1620 23.2% 2.5m\n", " 80 0.1608 23.2% 2.4m\n", " 90 0.1746 23.2% 2.4m\n", " 100 0.1818 23.2% 2.4m\n", " 110 0.1936 23.2% 2.3m\n", " 120 0.1345 23.2% 2.3m\n", " 130 0.1715 23.2% 2.3m\n", " 140 0.1602 23.2% 2.3m\n", " 150 0.1943 23.2% 2.2m\n", " 160 0.1608 23.2% 2.2m\n", " 170 0.1923 23.2% 2.2m\n", " 180 0.1710 23.2% 2.1m\n", " 190 0.1896 23.2% 2.1m\n", " 200 0.1756 23.2% 2.1m\n", " 210 0.1771 23.2% 2.0m\n", " 220 0.1923 23.2% 2.0m\n", " 230 0.1824 23.2% 2.0m\n", " 240 0.1767 23.2% 1.9m\n", " 250 0.1673 23.2% 1.9m\n", " 260 0.2080 23.2% 1.9m\n", " 270 0.1829 23.2% 1.8m\n", " 280 0.1313 23.2% 1.8m\n", " 290 0.1485 23.2% 1.8m\n", " 300 0.1461 23.2% 1.7m\n", " 310 0.1562 23.2% 1.7m\n", " 320 0.1653 23.2% 1.7m\n", " 330 0.1645 23.2% 1.7m\n", " 340 0.1879 23.2% 1.6m\n", " 350 0.2023 23.2% 1.6m\n", " 360 0.1667 23.2% 1.6m\n", " 370 0.1716 23.2% 1.5m\n", " 380 0.1384 23.2% 1.5m\n", " 390 0.1239 23.2% 1.5m\n", " 400 0.1819 23.2% 1.4m\n", " 410 0.1426 23.2% 1.4m\n", " 420 0.1600 23.2% 1.4m\n", " 430 0.1440 23.2% 1.3m\n", " 440 0.1577 23.2% 1.3m\n", " 450 0.1035 23.2% 1.3m\n", " 460 0.1790 23.2% 1.3m\n", " 470 0.1561 23.2% 1.2m\n", " 480 0.1459 23.2% 1.2m\n", " 490 0.2077 23.2% 1.2m\n", " 500 0.1673 23.2% 1.1m\n", " 510 0.1476 23.2% 1.1m\n", " 520 0.1705 23.2% 1.1m\n", " 530 0.1513 23.2% 1.0m\n", " 540 0.1850 23.2% 1.0m\n", " 550 0.1716 23.2% 1.0m\n", " 560 0.1902 23.2% 0.9m\n", " 570 0.1532 23.2% 0.9m\n", " 580 0.1758 23.2% 0.9m\n", " 590 0.1603 23.2% 0.8m\n", " 600 0.1805 23.2% 0.8m\n", " 610 0.1631 23.2% 0.8m\n", " 620 0.1644 23.2% 0.8m\n", " 630 0.1303 23.2% 0.7m\n", " 640 0.1847 23.2% 0.7m\n", " 650 0.2193 23.2% 0.7m\n", " 660 0.1776 23.2% 0.6m\n", " 670 0.1877 23.2% 0.6m\n", " 680 0.2168 23.2% 0.6m\n", " 690 0.1832 23.2% 0.5m\n", " 700 0.2000 23.2% 0.5m\n", " 710 0.1929 23.2% 0.5m\n", " 720 0.1811 23.2% 0.4m\n", " 730 0.1426 23.2% 0.4m\n", " 740 0.1761 23.2% 0.4m\n", " 750 0.1625 23.2% 0.4m\n", " 760 0.1988 23.2% 0.3m\n", " 770 0.1426 23.2% 0.3m\n", " 780 0.1891 23.2% 0.3m\n", " 790 0.1987 23.2% 0.2m\n", " 800 0.1717 23.2% 0.2m\n", " 810 0.1638 23.2% 0.2m\n", " 820 0.2034 23.2% 0.1m\n", " 830 0.1579 23.2% 0.1m\n", " 840 0.1778 23.2% 0.1m\n", " 850 0.1434 23.2% 0.0m\n", " 860 0.1565 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 12 COMPLETE in 161s\n", "\n", "โœ… EPOCH 12 SUMMARY\n", " โฑ๏ธ Time: 169s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16793 โ†’ Val=0.16486\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.5% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 247m\n", "\n", "๐Ÿ”„ Epoch 13/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1801 23.2% 2.6m\n", " 10 0.1931 23.2% 2.7m\n", " 20 0.1537 23.2% 2.6m\n", " 30 0.1275 23.2% 2.6m\n", " 40 0.1524 23.2% 2.6m\n", " 50 0.1467 23.2% 2.5m\n", " 60 0.1676 23.2% 2.5m\n", " 70 0.1078 23.2% 2.5m\n", " 80 0.2014 23.2% 2.4m\n", " 90 0.1609 23.2% 2.4m\n", " 100 0.1896 23.2% 2.4m\n", " 110 0.1937 23.2% 2.3m\n", " 120 0.1647 23.2% 2.3m\n", " 130 0.1719 23.2% 2.3m\n", " 140 0.1402 23.2% 2.2m\n", " 150 0.1454 23.2% 2.2m\n", " 160 0.1518 23.2% 2.2m\n", " 170 0.1606 23.2% 2.1m\n", " 180 0.1054 23.2% 2.1m\n", " 190 0.1329 23.2% 2.1m\n", " 200 0.1587 23.2% 2.1m\n", " 210 0.1524 23.2% 2.0m\n", " 220 0.1723 23.2% 2.0m\n", " 230 0.1516 23.2% 2.0m\n", " 240 0.1611 23.2% 1.9m\n", " 250 0.1404 23.2% 1.9m\n", " 260 0.1680 23.2% 1.9m\n", " 270 0.1662 23.2% 1.8m\n", " 280 0.1404 23.2% 1.8m\n", " 290 0.1705 23.2% 1.8m\n", " 300 0.1210 23.2% 1.7m\n", " 310 0.1708 23.2% 1.7m\n", " 320 0.1586 23.2% 1.7m\n", " 330 0.1994 23.2% 1.7m\n", " 340 0.1764 23.2% 1.6m\n", " 350 0.1409 23.2% 1.6m\n", " 360 0.1718 23.2% 1.6m\n", " 370 0.1499 23.2% 1.5m\n", " 380 0.1763 23.2% 1.5m\n", " 390 0.1350 23.2% 1.5m\n", " 400 0.1565 23.2% 1.4m\n", " 410 0.1778 23.2% 1.4m\n", " 420 0.1646 23.2% 1.4m\n", " 430 0.1480 23.2% 1.3m\n", " 440 0.1822 23.2% 1.3m\n", " 450 0.1989 23.2% 1.3m\n", " 460 0.1334 23.2% 1.3m\n", " 470 0.1482 23.2% 1.2m\n", " 480 0.1819 23.2% 1.2m\n", " 490 0.1690 23.2% 1.2m\n", " 500 0.1650 23.2% 1.1m\n", " 510 0.1502 23.2% 1.1m\n", " 520 0.1430 23.2% 1.1m\n", " 530 0.1773 23.2% 1.0m\n", " 540 0.1999 23.2% 1.0m\n", " 550 0.1365 23.2% 1.0m\n", " 560 0.1411 23.2% 0.9m\n", " 570 0.1607 23.2% 0.9m\n", " 580 0.1563 23.2% 0.9m\n", " 590 0.1657 23.2% 0.8m\n", " 600 0.1737 23.2% 0.8m\n", " 610 0.1621 23.2% 0.8m\n", " 620 0.1703 23.2% 0.8m\n", " 630 0.1709 23.2% 0.7m\n", " 640 0.1561 23.2% 0.7m\n", " 650 0.1884 23.2% 0.7m\n", " 660 0.1860 23.2% 0.6m\n", " 670 0.1605 23.2% 0.6m\n", " 680 0.1496 23.2% 0.6m\n", " 690 0.1248 23.2% 0.5m\n", " 700 0.1470 23.2% 0.5m\n", " 710 0.1412 23.2% 0.5m\n", " 720 0.1708 23.2% 0.4m\n", " 730 0.1634 23.2% 0.4m\n", " 740 0.1408 23.2% 0.4m\n", " 750 0.1837 23.2% 0.4m\n", " 760 0.1499 23.2% 0.3m\n", " 770 0.1384 23.2% 0.3m\n", " 780 0.1911 23.2% 0.3m\n", " 790 0.1668 23.2% 0.2m\n", " 800 0.1420 23.2% 0.2m\n", " 810 0.1805 23.2% 0.2m\n", " 820 0.1515 23.2% 0.1m\n", " 830 0.1977 23.2% 0.1m\n", " 840 0.1726 23.2% 0.1m\n", " 850 0.1769 23.2% 0.0m\n", " 860 0.1891 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 13 COMPLETE in 161s\n", "\n", "โœ… EPOCH 13 SUMMARY\n", " โฑ๏ธ Time: 169s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16810 โ†’ Val=0.16447\n", " ๐Ÿ“Š Acc: EC=81.5% EL=75.4% EJ=86.7% Overall=81.2%\n", " โญ NEW BEST MODEL SAVED (val_loss=0.16447)\n", " โฑ๏ธ Remaining ETA โ‰ˆ 245m\n", "\n", "๐Ÿ”„ Epoch 14/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1733 23.2% 2.6m\n", " 10 0.1724 23.2% 2.7m\n", " 20 0.1398 23.2% 2.6m\n", " 30 0.1736 23.2% 2.6m\n", " 40 0.1610 23.2% 2.6m\n", " 50 0.1770 23.2% 2.6m\n", " 60 0.1334 23.2% 2.5m\n", " 70 0.1495 23.2% 2.5m\n", " 80 0.1680 23.2% 2.5m\n", " 90 0.1669 23.2% 2.4m\n", " 100 0.1441 23.2% 2.4m\n", " 110 0.1703 23.2% 2.4m\n", " 120 0.1478 23.2% 2.3m\n", " 130 0.1643 23.2% 2.3m\n", " 140 0.2238 23.2% 2.3m\n", " 150 0.1503 23.2% 2.2m\n", " 160 0.1215 23.2% 2.2m\n", " 170 0.1278 23.2% 2.2m\n", " 180 0.1561 23.2% 2.1m\n", " 190 0.1627 23.2% 2.1m\n", " 200 0.1763 23.2% 2.1m\n", " 210 0.1783 23.2% 2.0m\n", " 220 0.1455 23.2% 2.0m\n", " 230 0.1454 23.2% 2.0m\n", " 240 0.1599 23.2% 1.9m\n", " 250 0.1799 23.2% 1.9m\n", " 260 0.1976 23.2% 1.9m\n", " 270 0.1276 23.2% 1.9m\n", " 280 0.1492 23.2% 1.8m\n", " 290 0.1493 23.2% 1.8m\n", " 300 0.1809 23.2% 1.8m\n", " 310 0.1699 23.2% 1.7m\n", " 320 0.2242 23.2% 1.7m\n", " 330 0.2003 23.2% 1.7m\n", " 340 0.1416 23.2% 1.6m\n", " 350 0.1788 23.2% 1.6m\n", " 360 0.1925 23.2% 1.6m\n", " 370 0.1572 23.2% 1.5m\n", " 380 0.1598 23.2% 1.5m\n", " 390 0.2008 23.2% 1.5m\n", " 400 0.1622 23.2% 1.4m\n", " 410 0.1560 23.2% 1.4m\n", " 420 0.1694 23.2% 1.4m\n", " 430 0.1380 23.2% 1.4m\n", " 440 0.1747 23.2% 1.3m\n", " 450 0.1694 23.2% 1.3m\n", " 460 0.1806 23.2% 1.3m\n", " 470 0.1583 23.2% 1.2m\n", " 480 0.1991 23.2% 1.2m\n", " 490 0.1962 23.2% 1.2m\n", " 500 0.1558 23.2% 1.1m\n", " 510 0.1570 23.2% 1.1m\n", " 520 0.1633 23.2% 1.1m\n", " 530 0.1446 23.2% 1.0m\n", " 540 0.1640 23.2% 1.0m\n", " 550 0.1889 23.2% 1.0m\n", " 560 0.2044 23.2% 0.9m\n", " 570 0.1762 23.2% 0.9m\n", " 580 0.1690 23.2% 0.9m\n", " 590 0.1867 23.2% 0.9m\n", " 600 0.1599 23.2% 0.8m\n", " 610 0.1622 23.2% 0.8m\n", " 620 0.1693 23.2% 0.8m\n", " 630 0.1770 23.2% 0.7m\n", " 640 0.1684 23.2% 0.7m\n", " 650 0.1568 23.2% 0.7m\n", " 660 0.1415 23.2% 0.6m\n", " 670 0.1354 23.2% 0.6m\n", " 680 0.1753 23.2% 0.6m\n", " 690 0.2086 23.2% 0.5m\n", " 700 0.1682 23.2% 0.5m\n", " 710 0.1756 23.2% 0.5m\n", " 720 0.1393 23.2% 0.4m\n", " 730 0.1768 23.2% 0.4m\n", " 740 0.1869 23.2% 0.4m\n", " 750 0.1791 23.2% 0.4m\n", " 760 0.1677 23.2% 0.3m\n", " 770 0.1552 23.2% 0.3m\n", " 780 0.1536 23.2% 0.3m\n", " 790 0.1443 23.2% 0.2m\n", " 800 0.1571 23.2% 0.2m\n", " 810 0.1748 23.2% 0.2m\n", " 820 0.1520 23.2% 0.1m\n", " 830 0.1710 23.2% 0.1m\n", " 840 0.1524 23.2% 0.1m\n", " 850 0.1872 23.2% 0.0m\n", " 860 0.1801 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 14 COMPLETE in 162s\n", "\n", "โœ… EPOCH 14 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16764 โ†’ Val=0.16430\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.2%\n", " โญ NEW BEST MODEL SAVED (val_loss=0.16430)\n", " โฑ๏ธ Remaining ETA โ‰ˆ 243m\n", "\n", "๐Ÿ”„ Epoch 15/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1213 23.2% 2.7m\n", " 10 0.1947 23.2% 2.7m\n", " 20 0.1757 23.2% 2.6m\n", " 30 0.1984 23.2% 2.6m\n", " 40 0.1921 23.2% 2.6m\n", " 50 0.1507 23.2% 2.5m\n", " 60 0.1914 23.2% 2.5m\n", " 70 0.1584 23.2% 2.5m\n", " 80 0.1516 23.2% 2.4m\n", " 90 0.1551 23.2% 2.4m\n", " 100 0.1837 23.2% 2.4m\n", " 110 0.1469 23.2% 2.3m\n", " 120 0.1775 23.2% 2.3m\n", " 130 0.1803 23.2% 2.3m\n", " 140 0.1901 23.2% 2.3m\n", " 150 0.2057 23.2% 2.2m\n", " 160 0.1445 23.2% 2.2m\n", " 170 0.1793 23.2% 2.2m\n", " 180 0.1749 23.2% 2.1m\n", " 190 0.1558 23.2% 2.1m\n", " 200 0.1989 23.2% 2.1m\n", " 210 0.1324 23.2% 2.0m\n", " 220 0.1846 23.2% 2.0m\n", " 230 0.1780 23.2% 2.0m\n", " 240 0.1510 23.2% 1.9m\n", " 250 0.1597 23.2% 1.9m\n", " 260 0.1376 23.2% 1.9m\n", " 270 0.1605 23.2% 1.8m\n", " 280 0.1626 23.2% 1.8m\n", " 290 0.1551 23.2% 1.8m\n", " 300 0.1498 23.2% 1.8m\n", " 310 0.1539 23.2% 1.7m\n", " 320 0.1848 23.2% 1.7m\n", " 330 0.1837 23.2% 1.7m\n", " 340 0.1522 23.2% 1.6m\n", " 350 0.1691 23.2% 1.6m\n", " 360 0.1562 23.2% 1.6m\n", " 370 0.1549 23.2% 1.5m\n", " 380 0.1071 23.2% 1.5m\n", " 390 0.1609 23.2% 1.5m\n", " 400 0.2057 23.2% 1.4m\n", " 410 0.1887 23.2% 1.4m\n", " 420 0.1548 23.2% 1.4m\n", " 430 0.1383 23.2% 1.3m\n", " 440 0.1718 23.2% 1.3m\n", " 450 0.1534 23.2% 1.3m\n", " 460 0.1666 23.2% 1.3m\n", " 470 0.1629 23.2% 1.2m\n", " 480 0.1465 23.2% 1.2m\n", " 490 0.1593 23.2% 1.2m\n", " 500 0.1839 23.2% 1.1m\n", " 510 0.1772 23.2% 1.1m\n", " 520 0.2088 23.2% 1.1m\n", " 530 0.1726 23.2% 1.0m\n", " 540 0.1767 23.2% 1.0m\n", " 550 0.1969 23.2% 1.0m\n", " 560 0.1572 23.2% 0.9m\n", " 570 0.1444 23.2% 0.9m\n", " 580 0.1546 23.2% 0.9m\n", " 590 0.1811 23.2% 0.9m\n", " 600 0.1604 23.2% 0.8m\n", " 610 0.1600 23.2% 0.8m\n", " 620 0.1491 23.2% 0.8m\n", " 630 0.1681 23.2% 0.7m\n", " 640 0.1756 23.2% 0.7m\n", " 650 0.1672 23.2% 0.7m\n", " 660 0.1422 23.2% 0.6m\n", " 670 0.2159 23.2% 0.6m\n", " 680 0.1718 23.2% 0.6m\n", " 690 0.1756 23.2% 0.5m\n", " 700 0.1609 23.2% 0.5m\n", " 710 0.1828 23.2% 0.5m\n", " 720 0.1629 23.2% 0.4m\n", " 730 0.1768 23.2% 0.4m\n", " 740 0.1449 23.2% 0.4m\n", " 750 0.1862 23.2% 0.4m\n", " 760 0.1671 23.2% 0.3m\n", " 770 0.1656 23.2% 0.3m\n", " 780 0.1584 23.2% 0.3m\n", " 790 0.1804 23.2% 0.2m\n", " 800 0.1987 23.2% 0.2m\n", " 810 0.1659 23.2% 0.2m\n", " 820 0.1254 23.2% 0.1m\n", " 830 0.1781 23.2% 0.1m\n", " 840 0.1961 23.2% 0.1m\n", " 850 0.1777 23.2% 0.0m\n", " 860 0.1418 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 15 COMPLETE in 161s\n", "\n", "โœ… EPOCH 15 SUMMARY\n", " โฑ๏ธ Time: 169s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16752 โ†’ Val=0.16423\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.6% Overall=81.2%\n", " โญ NEW BEST MODEL SAVED (val_loss=0.16423)\n", " โฑ๏ธ Remaining ETA โ‰ˆ 240m\n", "\n", "๐Ÿ”„ Epoch 16/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1776 23.2% 2.7m\n", " 10 0.1261 23.2% 2.7m\n", " 20 0.1510 23.2% 2.7m\n", " 30 0.1475 23.2% 2.6m\n", " 40 0.1164 23.2% 2.6m\n", " 50 0.1710 23.2% 2.5m\n", " 60 0.1978 23.2% 2.5m\n", " 70 0.1741 23.2% 2.5m\n", " 80 0.1906 23.2% 2.4m\n", " 90 0.1674 23.2% 2.4m\n", " 100 0.1389 23.2% 2.4m\n", " 110 0.1707 23.2% 2.4m\n", " 120 0.1531 23.2% 2.3m\n", " 130 0.1621 23.2% 2.3m\n", " 140 0.1780 23.2% 2.3m\n", " 150 0.1696 23.2% 2.2m\n", " 160 0.1955 23.2% 2.2m\n", " 170 0.1680 23.2% 2.2m\n", " 180 0.2014 23.2% 2.1m\n", " 190 0.1442 23.2% 2.1m\n", " 200 0.1769 23.2% 2.1m\n", " 210 0.1997 23.2% 2.0m\n", " 220 0.1674 23.2% 2.0m\n", " 230 0.1731 23.2% 2.0m\n", " 240 0.1755 23.2% 1.9m\n", " 250 0.1701 23.2% 1.9m\n", " 260 0.1159 23.2% 1.9m\n", " 270 0.1705 23.2% 1.8m\n", " 280 0.1592 23.2% 1.8m\n", " 290 0.1964 23.2% 1.8m\n", " 300 0.1694 23.2% 1.8m\n", " 310 0.1599 23.2% 1.7m\n", " 320 0.1328 23.2% 1.7m\n", " 330 0.1384 23.2% 1.7m\n", " 340 0.1671 23.2% 1.6m\n", " 350 0.1473 23.2% 1.6m\n", " 360 0.1627 23.2% 1.6m\n", " 370 0.1812 23.2% 1.5m\n", " 380 0.1757 23.2% 1.5m\n", " 390 0.1306 23.2% 1.5m\n", " 400 0.1712 23.2% 1.4m\n", " 410 0.2058 23.2% 1.4m\n", " 420 0.1815 23.2% 1.4m\n", " 430 0.1758 23.2% 1.3m\n", " 440 0.1511 23.2% 1.3m\n", " 450 0.1906 23.2% 1.3m\n", " 460 0.1660 23.2% 1.3m\n", " 470 0.1550 23.2% 1.2m\n", " 480 0.1689 23.2% 1.2m\n", " 490 0.1517 23.2% 1.2m\n", " 500 0.1855 23.2% 1.1m\n", " 510 0.1939 23.2% 1.1m\n", " 520 0.1879 23.2% 1.1m\n", " 530 0.2014 23.2% 1.0m\n", " 540 0.1498 23.2% 1.0m\n", " 550 0.1892 23.2% 1.0m\n", " 560 0.1700 23.2% 0.9m\n", " 570 0.1746 23.2% 0.9m\n", " 580 0.1622 23.2% 0.9m\n", " 590 0.2206 23.2% 0.9m\n", " 600 0.1588 23.2% 0.8m\n", " 610 0.1644 23.2% 0.8m\n", " 620 0.1521 23.2% 0.8m\n", " 630 0.1796 23.2% 0.7m\n", " 640 0.1733 23.2% 0.7m\n", " 650 0.1863 23.2% 0.7m\n", " 660 0.1684 23.2% 0.6m\n", " 670 0.1585 23.2% 0.6m\n", " 680 0.1540 23.2% 0.6m\n", " 690 0.1837 23.2% 0.5m\n", " 700 0.1908 23.2% 0.5m\n", " 710 0.1924 23.2% 0.5m\n", " 720 0.1828 23.2% 0.4m\n", " 730 0.1615 23.2% 0.4m\n", " 740 0.1556 23.2% 0.4m\n", " 750 0.1587 23.2% 0.4m\n", " 760 0.1349 23.2% 0.3m\n", " 770 0.1602 23.2% 0.3m\n", " 780 0.1699 23.2% 0.3m\n", " 790 0.2097 23.2% 0.2m\n", " 800 0.1676 23.2% 0.2m\n", " 810 0.1674 23.2% 0.2m\n", " 820 0.1839 23.2% 0.1m\n", " 830 0.1960 23.2% 0.1m\n", " 840 0.2016 23.2% 0.1m\n", " 850 0.1541 23.2% 0.0m\n", " 860 0.1492 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 16 COMPLETE in 161s\n", "\n", "โœ… EPOCH 16 SUMMARY\n", " โฑ๏ธ Time: 169s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16754 โ†’ Val=0.16426\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.6% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 237m\n", "\n", "๐Ÿ”„ Epoch 17/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.2191 23.2% 2.6m\n", " 10 0.1685 23.2% 2.6m\n", " 20 0.1986 23.2% 2.6m\n", " 30 0.1550 23.2% 2.6m\n", " 40 0.1601 23.2% 2.5m\n", " 50 0.1753 23.2% 2.5m\n", " 60 0.2003 23.2% 2.5m\n", " 70 0.1954 23.2% 2.5m\n", " 80 0.1245 23.2% 2.4m\n", " 90 0.1686 23.2% 2.4m\n", " 100 0.1634 23.2% 2.4m\n", " 110 0.1892 23.2% 2.3m\n", " 120 0.1576 23.2% 2.3m\n", " 130 0.1707 23.2% 2.3m\n", " 140 0.1788 23.2% 2.2m\n", " 150 0.1457 23.2% 2.2m\n", " 160 0.1750 23.2% 2.2m\n", " 170 0.1746 23.2% 2.2m\n", " 180 0.1807 23.2% 2.1m\n", " 190 0.1521 23.2% 2.1m\n", " 200 0.1712 23.2% 2.1m\n", " 210 0.1661 23.2% 2.0m\n", " 220 0.1531 23.2% 2.0m\n", " 230 0.1657 23.2% 2.0m\n", " 240 0.2026 23.2% 1.9m\n", " 250 0.1883 23.2% 1.9m\n", " 260 0.1462 23.2% 1.9m\n", " 270 0.1477 23.2% 1.8m\n", " 280 0.1276 23.2% 1.8m\n", " 290 0.1808 23.2% 1.8m\n", " 300 0.1874 23.2% 1.7m\n", " 310 0.1709 23.2% 1.7m\n", " 320 0.1667 23.2% 1.7m\n", " 330 0.1972 23.2% 1.7m\n", " 340 0.1405 23.2% 1.6m\n", " 350 0.1795 23.2% 1.6m\n", " 360 0.1563 23.2% 1.6m\n", " 370 0.1779 23.2% 1.5m\n", " 380 0.2144 23.2% 1.5m\n", " 390 0.1508 23.2% 1.5m\n", " 400 0.1802 23.2% 1.4m\n", " 410 0.1820 23.2% 1.4m\n", " 420 0.1570 23.2% 1.4m\n", " 430 0.1452 23.2% 1.3m\n", " 440 0.1471 23.2% 1.3m\n", " 450 0.2202 23.2% 1.3m\n", " 460 0.1642 23.2% 1.3m\n", " 470 0.1478 23.2% 1.2m\n", " 480 0.1773 23.2% 1.2m\n", " 490 0.1531 23.2% 1.2m\n", " 500 0.1295 23.2% 1.1m\n", " 510 0.1641 23.2% 1.1m\n", " 520 0.1757 23.2% 1.1m\n", " 530 0.1528 23.2% 1.0m\n", " 540 0.1971 23.2% 1.0m\n", " 550 0.1881 23.2% 1.0m\n", " 560 0.1404 23.2% 0.9m\n", " 570 0.1977 23.2% 0.9m\n", " 580 0.1866 23.2% 0.9m\n", " 590 0.1585 23.2% 0.8m\n", " 600 0.1486 23.2% 0.8m\n", " 610 0.1709 23.2% 0.8m\n", " 620 0.1657 23.2% 0.8m\n", " 630 0.1673 23.2% 0.7m\n", " 640 0.1504 23.2% 0.7m\n", " 650 0.1854 23.2% 0.7m\n", " 660 0.1770 23.2% 0.6m\n", " 670 0.1978 23.2% 0.6m\n", " 680 0.1546 23.2% 0.6m\n", " 690 0.1756 23.2% 0.5m\n", " 700 0.1741 23.2% 0.5m\n", " 710 0.1592 23.2% 0.5m\n", " 720 0.1858 23.2% 0.4m\n", " 730 0.2055 23.2% 0.4m\n", " 740 0.1683 23.2% 0.4m\n", " 750 0.1456 23.2% 0.4m\n", " 760 0.1747 23.2% 0.3m\n", " 770 0.1249 23.2% 0.3m\n", " 780 0.1585 23.2% 0.3m\n", " 790 0.1785 23.2% 0.2m\n", " 800 0.1570 23.2% 0.2m\n", " 810 0.1632 23.2% 0.2m\n", " 820 0.1182 23.2% 0.1m\n", " 830 0.1586 23.2% 0.1m\n", " 840 0.1862 23.2% 0.1m\n", " 850 0.1738 23.2% 0.0m\n", " 860 0.1635 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 17 COMPLETE in 161s\n", "\n", "โœ… EPOCH 17 SUMMARY\n", " โฑ๏ธ Time: 169s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16768 โ†’ Val=0.16528\n", " ๐Ÿ“Š Acc: EC=81.3% EL=75.4% EJ=86.7% Overall=81.1%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 233m\n", "\n", "๐Ÿ”„ Epoch 18/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1556 23.2% 2.6m\n", " 10 0.2086 23.2% 2.7m\n", " 20 0.2199 23.2% 2.7m\n", " 30 0.1616 23.2% 2.6m\n", " 40 0.1735 23.2% 2.6m\n", " 50 0.2002 23.2% 2.6m\n", " 60 0.1746 23.2% 2.5m\n", " 70 0.1813 23.2% 2.5m\n", " 80 0.1479 23.2% 2.5m\n", " 90 0.1908 23.2% 2.4m\n", " 100 0.1361 23.2% 2.4m\n", " 110 0.1783 23.2% 2.4m\n", " 120 0.1663 23.2% 2.3m\n", " 130 0.1577 23.2% 2.3m\n", " 140 0.2038 23.2% 2.3m\n", " 150 0.1555 23.2% 2.2m\n", " 160 0.1623 23.2% 2.2m\n", " 170 0.1809 23.2% 2.2m\n", " 180 0.2173 23.2% 2.1m\n", " 190 0.1947 23.2% 2.1m\n", " 200 0.1635 23.2% 2.1m\n", " 210 0.1502 23.2% 2.0m\n", " 220 0.1931 23.2% 2.0m\n", " 230 0.1468 23.2% 2.0m\n", " 240 0.1722 23.2% 1.9m\n", " 250 0.1668 23.2% 1.9m\n", " 260 0.1948 23.2% 1.9m\n", " 270 0.1674 23.2% 1.9m\n", " 280 0.1480 23.2% 1.8m\n", " 290 0.1748 23.2% 1.8m\n", " 300 0.1523 23.2% 1.8m\n", " 310 0.1721 23.2% 1.7m\n", " 320 0.1629 23.2% 1.7m\n", " 330 0.1657 23.2% 1.7m\n", " 340 0.1727 23.2% 1.6m\n", " 350 0.1495 23.2% 1.6m\n", " 360 0.1601 23.2% 1.6m\n", " 370 0.1844 23.2% 1.5m\n", " 380 0.1742 23.2% 1.5m\n", " 390 0.1525 23.2% 1.5m\n", " 400 0.1360 23.2% 1.4m\n", " 410 0.1428 23.2% 1.4m\n", " 420 0.1691 23.2% 1.4m\n", " 430 0.1559 23.2% 1.4m\n", " 440 0.1247 23.2% 1.3m\n", " 450 0.1430 23.2% 1.3m\n", " 460 0.1419 23.2% 1.3m\n", " 470 0.1344 23.2% 1.2m\n", " 480 0.1531 23.2% 1.2m\n", " 490 0.1783 23.2% 1.2m\n", " 500 0.1528 23.2% 1.1m\n", " 510 0.1754 23.2% 1.1m\n", " 520 0.1800 23.2% 1.1m\n", " 530 0.1804 23.2% 1.0m\n", " 540 0.1234 23.2% 1.0m\n", " 550 0.1850 23.2% 1.0m\n", " 560 0.1667 23.2% 0.9m\n", " 570 0.1511 23.2% 0.9m\n", " 580 0.1898 23.2% 0.9m\n", " 590 0.1857 23.2% 0.9m\n", " 600 0.2043 23.2% 0.8m\n", " 610 0.1481 23.2% 0.8m\n", " 620 0.1471 23.2% 0.8m\n", " 630 0.1580 23.2% 0.7m\n", " 640 0.1520 23.2% 0.7m\n", " 650 0.2278 23.2% 0.7m\n", " 660 0.2052 23.2% 0.6m\n", " 670 0.1643 23.2% 0.6m\n", " 680 0.1951 23.2% 0.6m\n", " 690 0.1290 23.2% 0.5m\n", " 700 0.1762 23.2% 0.5m\n", " 710 0.1820 23.2% 0.5m\n", " 720 0.1501 23.2% 0.4m\n", " 730 0.1376 23.2% 0.4m\n", " 740 0.1801 23.2% 0.4m\n", " 750 0.1736 23.2% 0.4m\n", " 760 0.1582 23.2% 0.3m\n", " 770 0.1787 23.2% 0.3m\n", " 780 0.1829 23.2% 0.3m\n", " 790 0.1686 23.2% 0.2m\n", " 800 0.1653 23.2% 0.2m\n", " 810 0.1351 23.2% 0.2m\n", " 820 0.2046 23.2% 0.1m\n", " 830 0.1689 23.2% 0.1m\n", " 840 0.1588 23.2% 0.1m\n", " 850 0.1516 23.2% 0.0m\n", " 860 0.1914 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 18 COMPLETE in 161s\n", "\n", "โœ… EPOCH 18 SUMMARY\n", " โฑ๏ธ Time: 169s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16755 โ†’ Val=0.16436\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 231m\n", "\n", "๐Ÿ”„ Epoch 19/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1525 23.2% 2.7m\n", " 10 0.1669 23.2% 2.7m\n", " 20 0.1487 23.2% 2.6m\n", " 30 0.2084 23.2% 2.6m\n", " 40 0.1779 23.2% 2.6m\n", " 50 0.1448 23.2% 2.5m\n", " 60 0.1551 23.2% 2.5m\n", " 70 0.1155 23.2% 2.5m\n", " 80 0.1786 23.2% 2.5m\n", " 90 0.1701 23.2% 2.4m\n", " 100 0.1781 23.2% 2.4m\n", " 110 0.1905 23.2% 2.4m\n", " 120 0.1899 23.2% 2.3m\n", " 130 0.1452 23.2% 2.3m\n", " 140 0.1638 23.2% 2.3m\n", " 150 0.1753 23.2% 2.2m\n", " 160 0.1793 23.2% 2.2m\n", " 170 0.1391 23.2% 2.2m\n", " 180 0.1709 23.2% 2.1m\n", " 190 0.1890 23.2% 2.1m\n", " 200 0.1770 23.2% 2.1m\n", " 210 0.1568 23.2% 2.0m\n", " 220 0.1622 23.2% 2.0m\n", " 230 0.1839 23.2% 2.0m\n", " 240 0.2063 23.2% 1.9m\n", " 250 0.1529 23.2% 1.9m\n", " 260 0.1691 23.2% 1.9m\n", " 270 0.1925 23.2% 1.9m\n", " 280 0.1539 23.2% 1.8m\n", " 290 0.2082 23.2% 1.8m\n", " 300 0.1580 23.2% 1.8m\n", " 310 0.1623 23.2% 1.7m\n", " 320 0.1522 23.2% 1.7m\n", " 330 0.1741 23.2% 1.7m\n", " 340 0.1649 23.2% 1.6m\n", " 350 0.2204 23.2% 1.6m\n", " 360 0.1360 23.2% 1.6m\n", " 370 0.2045 23.2% 1.5m\n", " 380 0.1788 23.2% 1.5m\n", " 390 0.1343 23.2% 1.5m\n", " 400 0.1725 23.2% 1.4m\n", " 410 0.1473 23.2% 1.4m\n", " 420 0.1294 23.2% 1.4m\n", " 430 0.1572 23.2% 1.3m\n", " 440 0.1814 23.2% 1.3m\n", " 450 0.1708 23.2% 1.3m\n", " 460 0.1359 23.2% 1.3m\n", " 470 0.1412 23.2% 1.2m\n", " 480 0.1690 23.2% 1.2m\n", " 490 0.1549 23.2% 1.2m\n", " 500 0.1536 23.2% 1.1m\n", " 510 0.1588 23.2% 1.1m\n", " 520 0.1629 23.2% 1.1m\n", " 530 0.1695 23.2% 1.0m\n", " 540 0.1752 23.2% 1.0m\n", " 550 0.1521 23.2% 1.0m\n", " 560 0.1540 23.2% 0.9m\n", " 570 0.1468 23.2% 0.9m\n", " 580 0.1832 23.2% 0.9m\n", " 590 0.1674 23.2% 0.9m\n", " 600 0.1630 23.2% 0.8m\n", " 610 0.1653 23.2% 0.8m\n", " 620 0.1804 23.2% 0.8m\n", " 630 0.1163 23.2% 0.7m\n", " 640 0.1500 23.2% 0.7m\n", " 650 0.1680 23.2% 0.7m\n", " 660 0.1738 23.2% 0.6m\n", " 670 0.1870 23.2% 0.6m\n", " 680 0.1686 23.2% 0.6m\n", " 690 0.1811 23.2% 0.5m\n", " 700 0.1608 23.2% 0.5m\n", " 710 0.1496 23.2% 0.5m\n", " 720 0.1472 23.2% 0.4m\n", " 730 0.1814 23.2% 0.4m\n", " 740 0.1814 23.2% 0.4m\n", " 750 0.1709 23.2% 0.4m\n", " 760 0.2073 23.2% 0.3m\n", " 770 0.1674 23.2% 0.3m\n", " 780 0.2093 23.2% 0.3m\n", " 790 0.1651 23.2% 0.2m\n", " 800 0.1930 23.2% 0.2m\n", " 810 0.1831 23.2% 0.2m\n", " 820 0.1599 23.2% 0.1m\n", " 830 0.2002 23.2% 0.1m\n", " 840 0.1963 23.2% 0.1m\n", " 850 0.1563 23.2% 0.0m\n", " 860 0.1838 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 19 COMPLETE in 161s\n", "\n", "โœ… EPOCH 19 SUMMARY\n", " โฑ๏ธ Time: 169s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16737 โ†’ Val=0.16442\n", " ๐Ÿ“Š Acc: EC=81.5% EL=75.4% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 229m\n", "\n", "๐Ÿ”„ Epoch 20/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1511 23.2% 2.6m\n", " 10 0.1285 23.2% 2.7m\n", " 20 0.1480 23.2% 2.6m\n", " 30 0.1647 23.2% 2.6m\n", " 40 0.1657 23.2% 2.6m\n", " 50 0.1629 23.2% 2.5m\n", " 60 0.1700 23.2% 2.5m\n", " 70 0.1698 23.2% 2.5m\n", " 80 0.1449 23.2% 2.4m\n", " 90 0.2170 23.2% 2.4m\n", " 100 0.1437 23.2% 2.4m\n", " 110 0.1927 23.2% 2.3m\n", " 120 0.1913 23.2% 2.3m\n", " 130 0.1396 23.2% 2.3m\n", " 140 0.1464 23.2% 2.3m\n", " 150 0.1760 23.2% 2.2m\n", " 160 0.1698 23.2% 2.2m\n", " 170 0.1546 23.2% 2.2m\n", " 180 0.1615 23.2% 2.1m\n", " 190 0.1306 23.2% 2.1m\n", " 200 0.1766 23.2% 2.1m\n", " 210 0.1558 23.2% 2.0m\n", " 220 0.1632 23.2% 2.0m\n", " 230 0.1477 23.2% 2.0m\n", " 240 0.1687 23.2% 1.9m\n", " 250 0.2011 23.2% 1.9m\n", " 260 0.1468 23.2% 1.9m\n", " 270 0.1639 23.2% 1.8m\n", " 280 0.1813 23.2% 1.8m\n", " 290 0.1972 23.2% 1.8m\n", " 300 0.1576 23.2% 1.8m\n", " 310 0.1574 23.2% 1.7m\n", " 320 0.1283 23.2% 1.7m\n", " 330 0.1732 23.2% 1.7m\n", " 340 0.1672 23.2% 1.6m\n", " 350 0.1515 23.2% 1.6m\n", " 360 0.1776 23.2% 1.6m\n", " 370 0.1756 23.2% 1.5m\n", " 380 0.1773 23.2% 1.5m\n", " 390 0.1707 23.2% 1.5m\n", " 400 0.1837 23.2% 1.4m\n", " 410 0.1277 23.2% 1.4m\n", " 420 0.1381 23.2% 1.4m\n", " 430 0.1470 23.2% 1.4m\n", " 440 0.1599 23.2% 1.3m\n", " 450 0.1852 23.2% 1.3m\n", " 460 0.1164 23.2% 1.3m\n", " 470 0.1671 23.2% 1.2m\n", " 480 0.1809 23.2% 1.2m\n", " 490 0.1958 23.2% 1.2m\n", " 500 0.1926 23.2% 1.1m\n", " 510 0.1399 23.2% 1.1m\n", " 520 0.1690 23.2% 1.1m\n", " 530 0.2006 23.2% 1.0m\n", " 540 0.1621 23.2% 1.0m\n", " 550 0.1616 23.2% 1.0m\n", " 560 0.1327 23.2% 0.9m\n", " 570 0.1516 23.2% 0.9m\n", " 580 0.1563 23.2% 0.9m\n", " 590 0.1446 23.2% 0.9m\n", " 600 0.1919 23.2% 0.8m\n", " 610 0.1924 23.2% 0.8m\n", " 620 0.1408 23.2% 0.8m\n", " 630 0.1471 23.2% 0.7m\n", " 640 0.1742 23.2% 0.7m\n", " 650 0.1828 23.2% 0.7m\n", " 660 0.1558 23.2% 0.6m\n", " 670 0.1543 23.2% 0.6m\n", " 680 0.1945 23.2% 0.6m\n", " 690 0.1383 23.2% 0.5m\n", " 700 0.1803 23.2% 0.5m\n", " 710 0.1310 23.2% 0.5m\n", " 720 0.1815 23.2% 0.4m\n", " 730 0.1534 23.2% 0.4m\n", " 740 0.1624 23.2% 0.4m\n", " 750 0.1751 23.2% 0.4m\n", " 760 0.1539 23.2% 0.3m\n", " 770 0.1669 23.2% 0.3m\n", " 780 0.1287 23.2% 0.3m\n", " 790 0.1659 23.2% 0.2m\n", " 800 0.1937 23.2% 0.2m\n", " 810 0.1194 23.2% 0.2m\n", " 820 0.2056 23.2% 0.1m\n", " 830 0.1616 23.2% 0.1m\n", " 840 0.1595 23.2% 0.1m\n", " 850 0.1752 23.2% 0.0m\n", " 860 0.1710 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 20 COMPLETE in 162s\n", "\n", "โœ… EPOCH 20 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16757 โ†’ Val=0.16412\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.2%\n", " โญ NEW BEST MODEL SAVED (val_loss=0.16412)\n", " โฑ๏ธ Remaining ETA โ‰ˆ 226m\n", "\n", "๐Ÿ”„ Epoch 21/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1599 23.2% 2.7m\n", " 10 0.1490 23.2% 2.7m\n", " 20 0.1689 23.2% 2.7m\n", " 30 0.1763 23.2% 2.6m\n", " 40 0.1533 23.2% 2.6m\n", " 50 0.1515 23.2% 2.6m\n", " 60 0.1696 23.2% 2.5m\n", " 70 0.1582 23.2% 2.5m\n", " 80 0.1904 23.2% 2.5m\n", " 90 0.1441 23.2% 2.5m\n", " 100 0.1853 23.2% 2.4m\n", " 110 0.1779 23.2% 2.4m\n", " 120 0.1424 23.2% 2.3m\n", " 130 0.1539 23.2% 2.3m\n", " 140 0.1637 23.2% 2.3m\n", " 150 0.1649 23.2% 2.2m\n", " 160 0.1907 23.2% 2.2m\n", " 170 0.1772 23.2% 2.2m\n", " 180 0.1556 23.2% 2.1m\n", " 190 0.1973 23.2% 2.1m\n", " 200 0.1733 23.2% 2.1m\n", " 210 0.1659 23.2% 2.0m\n", " 220 0.1682 23.2% 2.0m\n", " 230 0.1894 23.2% 2.0m\n", " 240 0.1576 23.2% 2.0m\n", " 250 0.1793 23.2% 1.9m\n", " 260 0.1697 23.2% 1.9m\n", " 270 0.1382 23.2% 1.9m\n", " 280 0.1671 23.2% 1.8m\n", " 290 0.1400 23.2% 1.8m\n", " 300 0.1771 23.2% 1.8m\n", " 310 0.1634 23.2% 1.7m\n", " 320 0.1617 23.2% 1.7m\n", " 330 0.1712 23.2% 1.7m\n", " 340 0.1377 23.2% 1.6m\n", " 350 0.1479 23.2% 1.6m\n", " 360 0.1531 23.2% 1.6m\n", " 370 0.1889 23.2% 1.5m\n", " 380 0.1648 23.2% 1.5m\n", " 390 0.1649 23.2% 1.5m\n", " 400 0.1446 23.2% 1.4m\n", " 410 0.1573 23.2% 1.4m\n", " 420 0.2065 23.2% 1.4m\n", " 430 0.1821 23.2% 1.3m\n", " 440 0.1365 23.2% 1.3m\n", " 450 0.1698 23.2% 1.3m\n", " 460 0.1368 23.2% 1.3m\n", " 470 0.1323 23.2% 1.2m\n", " 480 0.1731 23.2% 1.2m\n", " 490 0.1769 23.2% 1.2m\n", " 500 0.1335 23.2% 1.1m\n", " 510 0.2031 23.2% 1.1m\n", " 520 0.1301 23.2% 1.1m\n", " 530 0.1346 23.2% 1.0m\n", " 540 0.1957 23.2% 1.0m\n", " 550 0.1912 23.2% 1.0m\n", " 560 0.1694 23.2% 0.9m\n", " 570 0.1686 23.2% 0.9m\n", " 580 0.1881 23.2% 0.9m\n", " 590 0.1584 23.2% 0.9m\n", " 600 0.1614 23.2% 0.8m\n", " 610 0.1756 23.2% 0.8m\n", " 620 0.1431 23.2% 0.8m\n", " 630 0.1586 23.2% 0.7m\n", " 640 0.1610 23.2% 0.7m\n", " 650 0.1507 23.2% 0.7m\n", " 660 0.1828 23.2% 0.6m\n", " 670 0.1821 23.2% 0.6m\n", " 680 0.1741 23.2% 0.6m\n", " 690 0.1743 23.2% 0.5m\n", " 700 0.1384 23.2% 0.5m\n", " 710 0.1473 23.2% 0.5m\n", " 720 0.2020 23.2% 0.4m\n", " 730 0.1780 23.2% 0.4m\n", " 740 0.1367 23.2% 0.4m\n", " 750 0.1831 23.2% 0.4m\n", " 760 0.1700 23.2% 0.3m\n", " 770 0.1737 23.2% 0.3m\n", " 780 0.1240 23.2% 0.3m\n", " 790 0.1505 23.2% 0.2m\n", " 800 0.1622 23.2% 0.2m\n", " 810 0.1403 23.2% 0.2m\n", " 820 0.2161 23.2% 0.1m\n", " 830 0.1647 23.2% 0.1m\n", " 840 0.1490 23.2% 0.1m\n", " 850 0.1440 23.2% 0.0m\n", " 860 0.1608 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 21 COMPLETE in 161s\n", "\n", "โœ… EPOCH 21 SUMMARY\n", " โฑ๏ธ Time: 169s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16725 โ†’ Val=0.16459\n", " ๐Ÿ“Š Acc: EC=81.5% EL=75.4% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 223m\n", "\n", "๐Ÿ”„ Epoch 22/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1404 23.2% 2.6m\n", " 10 0.1781 23.2% 2.7m\n", " 20 0.2118 23.2% 2.6m\n", " 30 0.1514 23.2% 2.6m\n", " 40 0.1529 23.2% 2.6m\n", " 50 0.1722 23.2% 2.5m\n", " 60 0.1623 23.2% 2.5m\n", " 70 0.1480 23.2% 2.5m\n", " 80 0.1442 23.2% 2.4m\n", " 90 0.1529 23.2% 2.4m\n", " 100 0.1960 23.2% 2.4m\n", " 110 0.1617 23.2% 2.3m\n", " 120 0.1426 23.2% 2.3m\n", " 130 0.1972 23.2% 2.3m\n", " 140 0.1964 23.2% 2.3m\n", " 150 0.1763 23.2% 2.2m\n", " 160 0.1763 23.2% 2.2m\n", " 170 0.1882 23.2% 2.2m\n", " 180 0.1398 23.2% 2.1m\n", " 190 0.1250 23.2% 2.1m\n", " 200 0.1613 23.2% 2.1m\n", " 210 0.1822 23.2% 2.0m\n", " 220 0.1677 23.2% 2.0m\n", " 230 0.1772 23.2% 2.0m\n", " 240 0.1442 23.2% 1.9m\n", " 250 0.1971 23.2% 1.9m\n", " 260 0.2006 23.2% 1.9m\n", " 270 0.1858 23.2% 1.8m\n", " 280 0.1506 23.2% 1.8m\n", " 290 0.1243 23.2% 1.8m\n", " 300 0.1930 23.2% 1.8m\n", " 310 0.1664 23.2% 1.7m\n", " 320 0.1552 23.2% 1.7m\n", " 330 0.1936 23.2% 1.7m\n", " 340 0.1769 23.2% 1.6m\n", " 350 0.1850 23.2% 1.6m\n", " 360 0.1334 23.2% 1.6m\n", " 370 0.1472 23.2% 1.5m\n", " 380 0.1551 23.2% 1.5m\n", " 390 0.1689 23.2% 1.5m\n", " 400 0.1573 23.2% 1.4m\n", " 410 0.1619 23.2% 1.4m\n", " 420 0.1851 23.2% 1.4m\n", " 430 0.2175 23.2% 1.3m\n", " 440 0.1338 23.2% 1.3m\n", " 450 0.1884 23.2% 1.3m\n", " 460 0.1494 23.2% 1.3m\n", " 470 0.1695 23.2% 1.2m\n", " 480 0.1854 23.2% 1.2m\n", " 490 0.1884 23.2% 1.2m\n", " 500 0.1874 23.2% 1.1m\n", " 510 0.1683 23.2% 1.1m\n", " 520 0.1304 23.2% 1.1m\n", " 530 0.1492 23.2% 1.0m\n", " 540 0.1802 23.2% 1.0m\n", " 550 0.1606 23.2% 1.0m\n", " 560 0.1061 23.2% 0.9m\n", " 570 0.1774 23.2% 0.9m\n", " 580 0.1736 23.2% 0.9m\n", " 590 0.1446 23.2% 0.8m\n", " 600 0.1711 23.2% 0.8m\n", " 610 0.1632 23.2% 0.8m\n", " 620 0.1271 23.2% 0.8m\n", " 630 0.1771 23.2% 0.7m\n", " 640 0.1744 23.2% 0.7m\n", " 650 0.1795 23.2% 0.7m\n", " 660 0.1629 23.2% 0.6m\n", " 670 0.1959 23.2% 0.6m\n", " 680 0.2048 23.2% 0.6m\n", " 690 0.1668 23.2% 0.5m\n", " 700 0.1809 23.2% 0.5m\n", " 710 0.1806 23.2% 0.5m\n", " 720 0.1675 23.2% 0.4m\n", " 730 0.1853 23.2% 0.4m\n", " 740 0.1625 23.2% 0.4m\n", " 750 0.1947 23.2% 0.4m\n", " 760 0.1421 23.2% 0.3m\n", " 770 0.1453 23.2% 0.3m\n", " 780 0.1387 23.2% 0.3m\n", " 790 0.1653 23.2% 0.2m\n", " 800 0.1745 23.2% 0.2m\n", " 810 0.1586 23.2% 0.2m\n", " 820 0.1576 23.2% 0.1m\n", " 830 0.1995 23.2% 0.1m\n", " 840 0.1435 23.2% 0.1m\n", " 850 0.1958 23.2% 0.0m\n", " 860 0.1854 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 22 COMPLETE in 161s\n", "\n", "โœ… EPOCH 22 SUMMARY\n", " โฑ๏ธ Time: 169s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16735 โ†’ Val=0.16429\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 220m\n", "\n", "๐Ÿ”„ Epoch 23/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1919 23.2% 2.7m\n", " 10 0.1179 23.2% 2.7m\n", " 20 0.1817 23.2% 2.6m\n", " 30 0.1693 23.2% 2.6m\n", " 40 0.1533 23.2% 2.5m\n", " 50 0.1777 23.2% 2.5m\n", " 60 0.1474 23.2% 2.5m\n", " 70 0.1589 23.2% 2.5m\n", " 80 0.1490 23.2% 2.4m\n", " 90 0.1533 23.2% 2.4m\n", " 100 0.1908 23.2% 2.4m\n", " 110 0.1761 23.2% 2.3m\n", " 120 0.1608 23.2% 2.3m\n", " 130 0.1553 23.2% 2.3m\n", " 140 0.1680 23.2% 2.2m\n", " 150 0.1771 23.2% 2.2m\n", " 160 0.1735 23.2% 2.2m\n", " 170 0.1711 23.2% 2.2m\n", " 180 0.1675 23.2% 2.1m\n", " 190 0.1659 23.2% 2.1m\n", " 200 0.1733 23.2% 2.1m\n", " 210 0.1656 23.2% 2.0m\n", " 220 0.1982 23.2% 2.0m\n", " 230 0.1701 23.2% 2.0m\n", " 240 0.1986 23.2% 1.9m\n", " 250 0.1797 23.2% 1.9m\n", " 260 0.1253 23.2% 1.9m\n", " 270 0.1747 23.2% 1.8m\n", " 280 0.2022 23.2% 1.8m\n", " 290 0.1604 23.2% 1.8m\n", " 300 0.1756 23.2% 1.7m\n", " 310 0.1601 23.2% 1.7m\n", " 320 0.1528 23.2% 1.7m\n", " 330 0.1368 23.2% 1.7m\n", " 340 0.2012 23.2% 1.6m\n", " 350 0.1617 23.2% 1.6m\n", " 360 0.1657 23.2% 1.6m\n", " 370 0.1746 23.2% 1.5m\n", " 380 0.2142 23.2% 1.5m\n", " 390 0.1470 23.2% 1.5m\n", " 400 0.1776 23.2% 1.4m\n", " 410 0.1726 23.2% 1.4m\n", " 420 0.1374 23.2% 1.4m\n", " 430 0.1993 23.2% 1.3m\n", " 440 0.1328 23.2% 1.3m\n", " 450 0.1481 23.2% 1.3m\n", " 460 0.1750 23.2% 1.3m\n", " 470 0.1359 23.2% 1.2m\n", " 480 0.1561 23.2% 1.2m\n", " 490 0.1805 23.2% 1.2m\n", " 500 0.1693 23.2% 1.1m\n", " 510 0.1988 23.2% 1.1m\n", " 520 0.1536 23.2% 1.1m\n", " 530 0.1867 23.2% 1.0m\n", " 540 0.1658 23.2% 1.0m\n", " 550 0.1307 23.2% 1.0m\n", " 560 0.1824 23.2% 0.9m\n", " 570 0.1381 23.2% 0.9m\n", " 580 0.1456 23.2% 0.9m\n", " 590 0.1558 23.2% 0.8m\n", " 600 0.1515 23.2% 0.8m\n", " 610 0.1711 23.2% 0.8m\n", " 620 0.2202 23.2% 0.8m\n", " 630 0.1809 23.2% 0.7m\n", " 640 0.1881 23.2% 0.7m\n", " 650 0.1407 23.2% 0.7m\n", " 660 0.1525 23.2% 0.6m\n", " 670 0.1688 23.2% 0.6m\n", " 680 0.2197 23.2% 0.6m\n", " 690 0.1580 23.2% 0.5m\n", " 700 0.1557 23.2% 0.5m\n", " 710 0.1988 23.2% 0.5m\n", " 720 0.1740 23.2% 0.4m\n", " 730 0.1822 23.2% 0.4m\n", " 740 0.1330 23.2% 0.4m\n", " 750 0.1965 23.2% 0.4m\n", " 760 0.1593 23.2% 0.3m\n", " 770 0.1466 23.2% 0.3m\n", " 780 0.1653 23.2% 0.3m\n", " 790 0.1526 23.2% 0.2m\n", " 800 0.1695 23.2% 0.2m\n", " 810 0.2039 23.2% 0.2m\n", " 820 0.1467 23.2% 0.1m\n", " 830 0.1795 23.2% 0.1m\n", " 840 0.1449 23.2% 0.1m\n", " 850 0.1863 23.2% 0.0m\n", " 860 0.1886 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 23 COMPLETE in 161s\n", "\n", "โœ… EPOCH 23 SUMMARY\n", " โฑ๏ธ Time: 169s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16726 โ†’ Val=0.16436\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.6% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 217m\n", "\n", "๐Ÿ”„ Epoch 24/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1784 23.2% 2.8m\n", " 10 0.1791 23.2% 2.7m\n", " 20 0.1540 23.2% 2.6m\n", " 30 0.1354 23.2% 2.6m\n", " 40 0.2029 23.2% 2.6m\n", " 50 0.1575 23.2% 2.6m\n", " 60 0.1827 23.2% 2.5m\n", " 70 0.1260 23.2% 2.5m\n", " 80 0.1560 23.2% 2.5m\n", " 90 0.1508 23.2% 2.4m\n", " 100 0.1676 23.2% 2.4m\n", " 110 0.1573 23.2% 2.4m\n", " 120 0.1581 23.2% 2.3m\n", " 130 0.1877 23.2% 2.3m\n", " 140 0.1582 23.2% 2.3m\n", " 150 0.2198 23.2% 2.2m\n", " 160 0.1804 23.2% 2.2m\n", " 170 0.1584 23.2% 2.2m\n", " 180 0.1523 23.2% 2.1m\n", " 190 0.1831 23.2% 2.1m\n", " 200 0.1597 23.2% 2.1m\n", " 210 0.1725 23.2% 2.0m\n", " 220 0.1698 23.2% 2.0m\n", " 230 0.1556 23.2% 2.0m\n", " 240 0.1577 23.2% 2.0m\n", " 250 0.1459 23.2% 1.9m\n", " 260 0.1952 23.2% 1.9m\n", " 270 0.1682 23.2% 1.9m\n", " 280 0.2038 23.2% 1.8m\n", " 290 0.2085 23.2% 1.8m\n", " 300 0.2178 23.2% 1.8m\n", " 310 0.1812 23.2% 1.7m\n", " 320 0.1360 23.2% 1.7m\n", " 330 0.1263 23.2% 1.7m\n", " 340 0.1922 23.2% 1.6m\n", " 350 0.1507 23.2% 1.6m\n", " 360 0.1929 23.2% 1.6m\n", " 370 0.1603 23.2% 1.5m\n", " 380 0.1592 23.2% 1.5m\n", " 390 0.1591 23.2% 1.5m\n", " 400 0.2136 23.2% 1.4m\n", " 410 0.1463 23.2% 1.4m\n", " 420 0.1978 23.2% 1.4m\n", " 430 0.2040 23.2% 1.4m\n", " 440 0.1976 23.2% 1.3m\n", " 450 0.1721 23.2% 1.3m\n", " 460 0.2101 23.2% 1.3m\n", " 470 0.1455 23.2% 1.2m\n", " 480 0.1758 23.2% 1.2m\n", " 490 0.1753 23.2% 1.2m\n", " 500 0.1660 23.2% 1.1m\n", " 510 0.1668 23.2% 1.1m\n", " 520 0.1468 23.2% 1.1m\n", " 530 0.1693 23.2% 1.0m\n", " 540 0.1547 23.2% 1.0m\n", " 550 0.1782 23.2% 1.0m\n", " 560 0.1541 23.2% 0.9m\n", " 570 0.1823 23.2% 0.9m\n", " 580 0.1762 23.2% 0.9m\n", " 590 0.1177 23.2% 0.9m\n", " 600 0.1644 23.2% 0.8m\n", " 610 0.1428 23.2% 0.8m\n", " 620 0.2136 23.2% 0.8m\n", " 630 0.1695 23.2% 0.7m\n", " 640 0.1729 23.2% 0.7m\n", " 650 0.1907 23.2% 0.7m\n", " 660 0.1800 23.2% 0.6m\n", " 670 0.1408 23.2% 0.6m\n", " 680 0.2325 23.2% 0.6m\n", " 690 0.1932 23.2% 0.5m\n", " 700 0.1751 23.2% 0.5m\n", " 710 0.1414 23.2% 0.5m\n", " 720 0.1776 23.2% 0.4m\n", " 730 0.1801 23.2% 0.4m\n", " 740 0.1665 23.2% 0.4m\n", " 750 0.1415 23.2% 0.4m\n", " 760 0.1941 23.2% 0.3m\n", " 770 0.1887 23.2% 0.3m\n", " 780 0.1610 23.2% 0.3m\n", " 790 0.1458 23.2% 0.2m\n", " 800 0.1692 23.2% 0.2m\n", " 810 0.2065 23.2% 0.2m\n", " 820 0.1396 23.2% 0.1m\n", " 830 0.1655 23.2% 0.1m\n", " 840 0.1873 23.2% 0.1m\n", " 850 0.2018 23.2% 0.0m\n", " 860 0.1700 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 24 COMPLETE in 162s\n", "\n", "โœ… EPOCH 24 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16733 โ†’ Val=0.16471\n", " ๐Ÿ“Š Acc: EC=81.5% EL=75.4% EJ=86.6% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 215m\n", "\n", "๐Ÿ”„ Epoch 25/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1493 23.2% 2.7m\n", " 10 0.1632 23.2% 2.7m\n", " 20 0.2123 23.2% 2.6m\n", " 30 0.1685 23.2% 2.6m\n", " 40 0.1588 23.2% 2.6m\n", " 50 0.1819 23.2% 2.6m\n", " 60 0.1917 23.2% 2.5m\n", " 70 0.1683 23.2% 2.5m\n", " 80 0.2200 23.2% 2.5m\n", " 90 0.1886 23.2% 2.4m\n", " 100 0.1673 23.2% 2.4m\n", " 110 0.1621 23.2% 2.4m\n", " 120 0.1671 23.2% 2.3m\n", " 130 0.1218 23.2% 2.3m\n", " 140 0.1296 23.2% 2.3m\n", " 150 0.1768 23.2% 2.2m\n", " 160 0.1765 23.2% 2.2m\n", " 170 0.1577 23.2% 2.2m\n", " 180 0.1433 23.2% 2.1m\n", " 190 0.1724 23.2% 2.1m\n", " 200 0.1688 23.2% 2.1m\n", " 210 0.1811 23.2% 2.0m\n", " 220 0.1330 23.2% 2.0m\n", " 230 0.1983 23.2% 2.0m\n", " 240 0.1839 23.2% 2.0m\n", " 250 0.1864 23.2% 1.9m\n", " 260 0.2065 23.2% 1.9m\n", " 270 0.1453 23.2% 1.9m\n", " 280 0.1545 23.2% 1.8m\n", " 290 0.1392 23.2% 1.8m\n", " 300 0.1507 23.2% 1.8m\n", " 310 0.1584 23.2% 1.7m\n", " 320 0.1843 23.2% 1.7m\n", " 330 0.1946 23.2% 1.7m\n", " 340 0.2254 23.2% 1.6m\n", " 350 0.2149 23.2% 1.6m\n", " 360 0.1752 23.2% 1.6m\n", " 370 0.1341 23.2% 1.5m\n", " 380 0.1549 23.2% 1.5m\n", " 390 0.1600 23.2% 1.5m\n", " 400 0.1565 23.2% 1.4m\n", " 410 0.1704 23.2% 1.4m\n", " 420 0.1813 23.2% 1.4m\n", " 430 0.1927 23.2% 1.4m\n", " 440 0.2012 23.2% 1.3m\n", " 450 0.1245 23.2% 1.3m\n", " 460 0.2493 23.2% 1.3m\n", " 470 0.1953 23.2% 1.2m\n", " 480 0.1263 23.2% 1.2m\n", " 490 0.1695 23.2% 1.2m\n", " 500 0.2262 23.2% 1.1m\n", " 510 0.1419 23.2% 1.1m\n", " 520 0.1853 23.2% 1.1m\n", " 530 0.1892 23.2% 1.0m\n", " 540 0.1545 23.2% 1.0m\n", " 550 0.1436 23.2% 1.0m\n", " 560 0.1844 23.2% 0.9m\n", " 570 0.1393 23.2% 0.9m\n", " 580 0.1808 23.2% 0.9m\n", " 590 0.1477 23.2% 0.9m\n", " 600 0.1763 23.2% 0.8m\n", " 610 0.1104 23.2% 0.8m\n", " 620 0.1409 23.2% 0.8m\n", " 630 0.1764 23.2% 0.7m\n", " 640 0.1895 23.2% 0.7m\n", " 650 0.1745 23.2% 0.7m\n", " 660 0.1787 23.2% 0.6m\n", " 670 0.1354 23.2% 0.6m\n", " 680 0.1745 23.2% 0.6m\n", " 690 0.1698 23.2% 0.5m\n", " 700 0.1790 23.2% 0.5m\n", " 710 0.1607 23.2% 0.5m\n", " 720 0.1823 23.2% 0.4m\n", " 730 0.1541 23.2% 0.4m\n", " 740 0.2009 23.2% 0.4m\n", " 750 0.1599 23.2% 0.4m\n", " 760 0.1857 23.2% 0.3m\n", " 770 0.1813 23.2% 0.3m\n", " 780 0.1669 23.2% 0.3m\n", " 790 0.1907 23.2% 0.2m\n", " 800 0.2187 23.2% 0.2m\n", " 810 0.1404 23.2% 0.2m\n", " 820 0.1527 23.2% 0.1m\n", " 830 0.1498 23.2% 0.1m\n", " 840 0.1815 23.2% 0.1m\n", " 850 0.1850 23.2% 0.0m\n", " 860 0.1376 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 25 COMPLETE in 162s\n", "\n", "โœ… EPOCH 25 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16711 โ†’ Val=0.16487\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.5% Overall=81.1%\n", " โš ๏ธ No improvement for 5/30\n", " โฑ๏ธ Remaining ETA โ‰ˆ 212m\n", "\n", "๐Ÿ”„ Epoch 26/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.2221 23.2% 2.7m\n", " 10 0.1944 23.2% 2.6m\n", " 20 0.1644 23.2% 2.6m\n", " 30 0.1646 23.2% 2.6m\n", " 40 0.1971 23.2% 2.5m\n", " 50 0.1524 23.2% 2.5m\n", " 60 0.2110 23.2% 2.5m\n", " 70 0.1937 23.2% 2.5m\n", " 80 0.1856 23.2% 2.4m\n", " 90 0.1790 23.2% 2.4m\n", " 100 0.1949 23.2% 2.4m\n", " 110 0.1566 23.2% 2.3m\n", " 120 0.1779 23.2% 2.3m\n", " 130 0.1563 23.2% 2.3m\n", " 140 0.1808 23.2% 2.2m\n", " 150 0.1845 23.2% 2.2m\n", " 160 0.1809 23.2% 2.2m\n", " 170 0.2058 23.2% 2.2m\n", " 180 0.1545 23.2% 2.1m\n", " 190 0.2055 23.2% 2.1m\n", " 200 0.1601 23.2% 2.1m\n", " 210 0.1521 23.2% 2.0m\n", " 220 0.1692 23.2% 2.0m\n", " 230 0.2261 23.2% 2.0m\n", " 240 0.1344 23.2% 1.9m\n", " 250 0.1779 23.2% 1.9m\n", " 260 0.1467 23.2% 1.9m\n", " 270 0.1694 23.2% 1.9m\n", " 280 0.1634 23.2% 1.8m\n", " 290 0.1960 23.2% 1.8m\n", " 300 0.1492 23.2% 1.8m\n", " 310 0.1605 23.2% 1.7m\n", " 320 0.1496 23.2% 1.7m\n", " 330 0.1431 23.2% 1.7m\n", " 340 0.1434 23.2% 1.6m\n", " 350 0.1805 23.2% 1.6m\n", " 360 0.1617 23.2% 1.6m\n", " 370 0.1843 23.2% 1.5m\n", " 380 0.1719 23.2% 1.5m\n", " 390 0.1841 23.2% 1.5m\n", " 400 0.1793 23.2% 1.4m\n", " 410 0.2103 23.2% 1.4m\n", " 420 0.1476 23.2% 1.4m\n", " 430 0.1575 23.2% 1.4m\n", " 440 0.2092 23.2% 1.3m\n", " 450 0.1685 23.2% 1.3m\n", " 460 0.1829 23.2% 1.3m\n", " 470 0.1495 23.2% 1.2m\n", " 480 0.1809 23.2% 1.2m\n", " 490 0.1916 23.2% 1.2m\n", " 500 0.1371 23.2% 1.1m\n", " 510 0.1368 23.2% 1.1m\n", " 520 0.2084 23.2% 1.1m\n", " 530 0.2072 23.2% 1.0m\n", " 540 0.1386 23.2% 1.0m\n", " 550 0.1430 23.2% 1.0m\n", " 560 0.1523 23.2% 0.9m\n", " 570 0.1388 23.2% 0.9m\n", " 580 0.1677 23.2% 0.9m\n", " 590 0.1638 23.2% 0.9m\n", " 600 0.1618 23.2% 0.8m\n", " 610 0.1710 23.2% 0.8m\n", " 620 0.1793 23.2% 0.8m\n", " 630 0.1558 23.2% 0.7m\n", " 640 0.1704 23.2% 0.7m\n", " 650 0.1823 23.2% 0.7m\n", " 660 0.1511 23.2% 0.6m\n", " 670 0.1854 23.2% 0.6m\n", " 680 0.1827 23.2% 0.6m\n", " 690 0.1761 23.2% 0.5m\n", " 700 0.1420 23.2% 0.5m\n", " 710 0.1515 23.2% 0.5m\n", " 720 0.1795 23.2% 0.4m\n", " 730 0.1801 23.2% 0.4m\n", " 740 0.1424 23.2% 0.4m\n", " 750 0.1724 23.2% 0.4m\n", " 760 0.1362 23.2% 0.3m\n", " 770 0.1694 23.2% 0.3m\n", " 780 0.1684 23.2% 0.3m\n", " 790 0.1941 23.2% 0.2m\n", " 800 0.2180 23.2% 0.2m\n", " 810 0.1885 23.2% 0.2m\n", " 820 0.1626 23.2% 0.1m\n", " 830 0.1588 23.2% 0.1m\n", " 840 0.1796 23.2% 0.1m\n", " 850 0.1351 23.2% 0.0m\n", " 860 0.1502 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 26 COMPLETE in 162s\n", "\n", "โœ… EPOCH 26 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16711 โ†’ Val=0.16428\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.6% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 209m\n", "\n", "๐Ÿ”„ Epoch 27/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1562 23.2% 2.6m\n", " 10 0.1752 23.2% 2.7m\n", " 20 0.1764 23.2% 2.6m\n", " 30 0.1632 23.2% 2.6m\n", " 40 0.1670 23.2% 2.6m\n", " 50 0.1627 23.2% 2.5m\n", " 60 0.1945 23.2% 2.5m\n", " 70 0.1816 23.2% 2.5m\n", " 80 0.1603 23.2% 2.5m\n", " 90 0.1528 23.2% 2.4m\n", " 100 0.1803 23.2% 2.4m\n", " 110 0.1496 23.2% 2.4m\n", " 120 0.1663 23.2% 2.3m\n", " 130 0.1765 23.2% 2.3m\n", " 140 0.1604 23.2% 2.3m\n", " 150 0.1529 23.2% 2.2m\n", " 160 0.1489 23.2% 2.2m\n", " 170 0.1896 23.2% 2.2m\n", " 180 0.1708 23.2% 2.1m\n", " 190 0.1665 23.2% 2.1m\n", " 200 0.2061 23.2% 2.1m\n", " 210 0.1841 23.2% 2.0m\n", " 220 0.1537 23.2% 2.0m\n", " 230 0.1813 23.2% 2.0m\n", " 240 0.1516 23.2% 1.9m\n", " 250 0.1609 23.2% 1.9m\n", " 260 0.1333 23.2% 1.9m\n", " 270 0.1329 23.2% 1.9m\n", " 280 0.1408 23.2% 1.8m\n", " 290 0.1756 23.2% 1.8m\n", " 300 0.1836 23.2% 1.8m\n", " 310 0.1653 23.2% 1.7m\n", " 320 0.1851 23.2% 1.7m\n", " 330 0.1700 23.2% 1.7m\n", " 340 0.1840 23.2% 1.6m\n", " 350 0.1914 23.2% 1.6m\n", " 360 0.1736 23.2% 1.6m\n", " 370 0.1528 23.2% 1.5m\n", " 380 0.1839 23.2% 1.5m\n", " 390 0.1444 23.2% 1.5m\n", " 400 0.1347 23.2% 1.4m\n", " 410 0.1796 23.2% 1.4m\n", " 420 0.1514 23.2% 1.4m\n", " 430 0.1986 23.2% 1.3m\n", " 440 0.1933 23.2% 1.3m\n", " 450 0.1287 23.2% 1.3m\n", " 460 0.2167 23.2% 1.3m\n", " 470 0.1442 23.2% 1.2m\n", " 480 0.1719 23.2% 1.2m\n", " 490 0.2124 23.2% 1.2m\n", " 500 0.1808 23.2% 1.1m\n", " 510 0.1421 23.2% 1.1m\n", " 520 0.1756 23.2% 1.1m\n", " 530 0.1629 23.2% 1.0m\n", " 540 0.1592 23.2% 1.0m\n", " 550 0.1680 23.2% 1.0m\n", " 560 0.1536 23.2% 0.9m\n", " 570 0.1629 23.2% 0.9m\n", " 580 0.1917 23.2% 0.9m\n", " 590 0.1468 23.2% 0.8m\n", " 600 0.1858 23.2% 0.8m\n", " 610 0.1688 23.2% 0.8m\n", " 620 0.1302 23.2% 0.8m\n", " 630 0.1724 23.2% 0.7m\n", " 640 0.1609 23.2% 0.7m\n", " 650 0.1866 23.2% 0.7m\n", " 660 0.1578 23.2% 0.6m\n", " 670 0.1527 23.2% 0.6m\n", " 680 0.1867 23.2% 0.6m\n", " 690 0.1554 23.2% 0.5m\n", " 700 0.1917 23.2% 0.5m\n", " 710 0.1143 23.2% 0.5m\n", " 720 0.1607 23.2% 0.4m\n", " 730 0.1524 23.2% 0.4m\n", " 740 0.1674 23.2% 0.4m\n", " 750 0.1705 23.2% 0.4m\n", " 760 0.1419 23.2% 0.3m\n", " 770 0.1510 23.2% 0.3m\n", " 780 0.1632 23.2% 0.3m\n", " 790 0.2119 23.2% 0.2m\n", " 800 0.1885 23.2% 0.2m\n", " 810 0.1609 23.2% 0.2m\n", " 820 0.1223 23.2% 0.1m\n", " 830 0.1736 23.2% 0.1m\n", " 840 0.1556 23.2% 0.1m\n", " 850 0.1217 23.2% 0.0m\n", " 860 0.1458 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 27 COMPLETE in 161s\n", "\n", "โœ… EPOCH 27 SUMMARY\n", " โฑ๏ธ Time: 169s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16710 โ†’ Val=0.16395\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.3%\n", " โญ NEW BEST MODEL SAVED (val_loss=0.16395)\n", " โฑ๏ธ Remaining ETA โ‰ˆ 206m\n", "\n", "๐Ÿ”„ Epoch 28/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1857 23.2% 2.6m\n", " 10 0.1679 23.2% 2.7m\n", " 20 0.1248 23.2% 2.6m\n", " 30 0.1515 23.2% 2.6m\n", " 40 0.2149 23.2% 2.6m\n", " 50 0.1859 23.2% 2.5m\n", " 60 0.1350 23.2% 2.5m\n", " 70 0.1836 23.2% 2.5m\n", " 80 0.1387 23.2% 2.4m\n", " 90 0.1941 23.2% 2.4m\n", " 100 0.1496 23.2% 2.4m\n", " 110 0.2256 23.2% 2.3m\n", " 120 0.1839 23.2% 2.3m\n", " 130 0.1614 23.2% 2.3m\n", " 140 0.2069 23.2% 2.2m\n", " 150 0.1176 23.2% 2.2m\n", " 160 0.1723 23.2% 2.2m\n", " 170 0.1792 23.2% 2.1m\n", " 180 0.1522 23.2% 2.1m\n", " 190 0.1519 23.2% 2.1m\n", " 200 0.1875 23.2% 2.1m\n", " 210 0.2035 23.2% 2.0m\n", " 220 0.2039 23.2% 2.0m\n", " 230 0.2107 23.2% 2.0m\n", " 240 0.1832 23.2% 1.9m\n", " 250 0.1845 23.2% 1.9m\n", " 260 0.1560 23.2% 1.9m\n", " 270 0.1551 23.2% 1.8m\n", " 280 0.1797 23.2% 1.8m\n", " 290 0.1811 23.2% 1.8m\n", " 300 0.1978 23.2% 1.7m\n", " 310 0.1787 23.2% 1.7m\n", " 320 0.1669 23.2% 1.7m\n", " 330 0.1513 23.2% 1.7m\n", " 340 0.1768 23.2% 1.6m\n", " 350 0.1580 23.2% 1.6m\n", " 360 0.1937 23.2% 1.6m\n", " 370 0.1385 23.2% 1.5m\n", " 380 0.1725 23.2% 1.5m\n", " 390 0.2004 23.2% 1.5m\n", " 400 0.1629 23.2% 1.4m\n", " 410 0.1646 23.2% 1.4m\n", " 420 0.1776 23.2% 1.4m\n", " 430 0.1368 23.2% 1.3m\n", " 440 0.1872 23.2% 1.3m\n", " 450 0.1613 23.2% 1.3m\n", " 460 0.1301 23.2% 1.3m\n", " 470 0.1635 23.2% 1.2m\n", " 480 0.2024 23.2% 1.2m\n", " 490 0.1869 23.2% 1.2m\n", " 500 0.1572 23.2% 1.1m\n", " 510 0.1703 23.2% 1.1m\n", " 520 0.1335 23.2% 1.1m\n", " 530 0.1901 23.2% 1.0m\n", " 540 0.2203 23.2% 1.0m\n", " 550 0.1102 23.2% 1.0m\n", " 560 0.1521 23.2% 0.9m\n", " 570 0.1993 23.2% 0.9m\n", " 580 0.1885 23.2% 0.9m\n", " 590 0.1735 23.2% 0.9m\n", " 600 0.1855 23.2% 0.8m\n", " 610 0.1786 23.2% 0.8m\n", " 620 0.1606 23.2% 0.8m\n", " 630 0.1519 23.2% 0.7m\n", " 640 0.2004 23.2% 0.7m\n", " 650 0.1755 23.2% 0.7m\n", " 660 0.1812 23.2% 0.6m\n", " 670 0.1818 23.2% 0.6m\n", " 680 0.1474 23.2% 0.6m\n", " 690 0.1664 23.2% 0.5m\n", " 700 0.1866 23.2% 0.5m\n", " 710 0.1388 23.2% 0.5m\n", " 720 0.1722 23.2% 0.4m\n", " 730 0.1476 23.2% 0.4m\n", " 740 0.1590 23.2% 0.4m\n", " 750 0.1418 23.2% 0.4m\n", " 760 0.1236 23.2% 0.3m\n", " 770 0.1501 23.2% 0.3m\n", " 780 0.1878 23.2% 0.3m\n", " 790 0.1894 23.2% 0.2m\n", " 800 0.1394 23.2% 0.2m\n", " 810 0.1719 23.2% 0.2m\n", " 820 0.1505 23.2% 0.1m\n", " 830 0.1283 23.2% 0.1m\n", " 840 0.2084 23.2% 0.1m\n", " 850 0.1848 23.2% 0.0m\n", " 860 0.1855 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 28 COMPLETE in 161s\n", "\n", "โœ… EPOCH 28 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16705 โ†’ Val=0.16416\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 203m\n", "\n", "๐Ÿ”„ Epoch 29/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1680 23.2% 2.9m\n", " 10 0.1709 23.2% 2.7m\n", " 20 0.1564 23.2% 2.6m\n", " 30 0.1497 23.2% 2.6m\n", " 40 0.1938 23.2% 2.5m\n", " 50 0.1580 23.2% 2.5m\n", " 60 0.1674 23.2% 2.5m\n", " 70 0.1614 23.2% 2.5m\n", " 80 0.1530 23.2% 2.4m\n", " 90 0.1326 23.2% 2.4m\n", " 100 0.1951 23.2% 2.4m\n", " 110 0.2200 23.2% 2.3m\n", " 120 0.1892 23.2% 2.3m\n", " 130 0.1582 23.2% 2.3m\n", " 140 0.1681 23.2% 2.3m\n", " 150 0.1839 23.2% 2.2m\n", " 160 0.1377 23.2% 2.2m\n", " 170 0.1787 23.2% 2.2m\n", " 180 0.1833 23.2% 2.1m\n", " 190 0.1502 23.2% 2.1m\n", " 200 0.1825 23.2% 2.1m\n", " 210 0.1391 23.2% 2.0m\n", " 220 0.1692 23.2% 2.0m\n", " 230 0.1773 23.2% 2.0m\n", " 240 0.1646 23.2% 1.9m\n", " 250 0.1942 23.2% 1.9m\n", " 260 0.1613 23.2% 1.9m\n", " 270 0.1640 23.2% 1.8m\n", " 280 0.2096 23.2% 1.8m\n", " 290 0.1600 23.2% 1.8m\n", " 300 0.1183 23.2% 1.8m\n", " 310 0.1761 23.2% 1.7m\n", " 320 0.1710 23.2% 1.7m\n", " 330 0.1541 23.2% 1.7m\n", " 340 0.1621 23.2% 1.6m\n", " 350 0.1883 23.2% 1.6m\n", " 360 0.1604 23.2% 1.6m\n", " 370 0.1772 23.2% 1.5m\n", " 380 0.1794 23.2% 1.5m\n", " 390 0.1816 23.2% 1.5m\n", " 400 0.1862 23.2% 1.4m\n", " 410 0.1576 23.2% 1.4m\n", " 420 0.1851 23.2% 1.4m\n", " 430 0.1475 23.2% 1.3m\n", " 440 0.2136 23.2% 1.3m\n", " 450 0.1631 23.2% 1.3m\n", " 460 0.1731 23.2% 1.3m\n", " 470 0.1611 23.2% 1.2m\n", " 480 0.1848 23.2% 1.2m\n", " 490 0.1757 23.2% 1.2m\n", " 500 0.1355 23.2% 1.1m\n", " 510 0.1741 23.2% 1.1m\n", " 520 0.1479 23.2% 1.1m\n", " 530 0.1688 23.2% 1.0m\n", " 540 0.1356 23.2% 1.0m\n", " 550 0.2057 23.2% 1.0m\n", " 560 0.1439 23.2% 0.9m\n", " 570 0.1593 23.2% 0.9m\n", " 580 0.1456 23.2% 0.9m\n", " 590 0.1930 23.2% 0.9m\n", " 600 0.1668 23.2% 0.8m\n", " 610 0.1626 23.2% 0.8m\n", " 620 0.1573 23.2% 0.8m\n", " 630 0.2317 23.2% 0.7m\n", " 640 0.1591 23.2% 0.7m\n", " 650 0.2059 23.2% 0.7m\n", " 660 0.1640 23.2% 0.6m\n", " 670 0.1557 23.2% 0.6m\n", " 680 0.1797 23.2% 0.6m\n", " 690 0.1343 23.2% 0.5m\n", " 700 0.1346 23.2% 0.5m\n", " 710 0.1700 23.2% 0.5m\n", " 720 0.1636 23.2% 0.4m\n", " 730 0.1864 23.2% 0.4m\n", " 740 0.1686 23.2% 0.4m\n", " 750 0.1587 23.2% 0.4m\n", " 760 0.1888 23.2% 0.3m\n", " 770 0.1460 23.2% 0.3m\n", " 780 0.1795 23.2% 0.3m\n", " 790 0.1542 23.2% 0.2m\n", " 800 0.1820 23.2% 0.2m\n", " 810 0.1411 23.2% 0.2m\n", " 820 0.1734 23.2% 0.1m\n", " 830 0.1621 23.2% 0.1m\n", " 840 0.1894 23.2% 0.1m\n", " 850 0.1535 23.2% 0.0m\n", " 860 0.1768 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 29 COMPLETE in 161s\n", "\n", "โœ… EPOCH 29 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16713 โ†’ Val=0.16424\n", " ๐Ÿ“Š Acc: EC=81.5% EL=75.4% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 201m\n", "\n", "๐Ÿ”„ Epoch 30/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.2034 23.2% 2.7m\n", " 10 0.1697 23.2% 2.7m\n", " 20 0.1645 23.2% 2.6m\n", " 30 0.2069 23.2% 2.6m\n", " 40 0.1663 23.2% 2.6m\n", " 50 0.1825 23.2% 2.5m\n", " 60 0.1772 23.2% 2.5m\n", " 70 0.1803 23.2% 2.5m\n", " 80 0.2421 23.2% 2.4m\n", " 90 0.1491 23.2% 2.4m\n", " 100 0.1535 23.2% 2.4m\n", " 110 0.1895 23.2% 2.4m\n", " 120 0.1752 23.2% 2.3m\n", " 130 0.1615 23.2% 2.3m\n", " 140 0.1659 23.2% 2.3m\n", " 150 0.1527 23.2% 2.2m\n", " 160 0.1522 23.2% 2.2m\n", " 170 0.1976 23.2% 2.2m\n", " 180 0.1842 23.2% 2.1m\n", " 190 0.1474 23.2% 2.1m\n", " 200 0.1770 23.2% 2.1m\n", " 210 0.1674 23.2% 2.0m\n", " 220 0.1451 23.2% 2.0m\n", " 230 0.1728 23.2% 2.0m\n", " 240 0.1761 23.2% 1.9m\n", " 250 0.1618 23.2% 1.9m\n", " 260 0.1558 23.2% 1.9m\n", " 270 0.1207 23.2% 1.9m\n", " 280 0.1661 23.2% 1.8m\n", " 290 0.1754 23.2% 1.8m\n", " 300 0.1771 23.2% 1.8m\n", " 310 0.1973 23.2% 1.7m\n", " 320 0.1717 23.2% 1.7m\n", " 330 0.1649 23.2% 1.7m\n", " 340 0.1475 23.2% 1.6m\n", " 350 0.1374 23.2% 1.6m\n", " 360 0.1717 23.2% 1.6m\n", " 370 0.1881 23.2% 1.5m\n", " 380 0.1873 23.2% 1.5m\n", " 390 0.1793 23.2% 1.5m\n", " 400 0.1877 23.2% 1.4m\n", " 410 0.1769 23.2% 1.4m\n", " 420 0.1676 23.2% 1.4m\n", " 430 0.1493 23.2% 1.4m\n", " 440 0.1963 23.2% 1.3m\n", " 450 0.1691 23.2% 1.3m\n", " 460 0.1945 23.2% 1.3m\n", " 470 0.1620 23.2% 1.2m\n", " 480 0.1806 23.2% 1.2m\n", " 490 0.2004 23.2% 1.2m\n", " 500 0.1609 23.2% 1.1m\n", " 510 0.1489 23.2% 1.1m\n", " 520 0.1218 23.2% 1.1m\n", " 530 0.1687 23.2% 1.0m\n", " 540 0.1894 23.2% 1.0m\n", " 550 0.1882 23.2% 1.0m\n", " 560 0.2213 23.2% 0.9m\n", " 570 0.1717 23.2% 0.9m\n", " 580 0.1835 23.2% 0.9m\n", " 590 0.2069 23.2% 0.9m\n", " 600 0.1578 23.2% 0.8m\n", " 610 0.1737 23.2% 0.8m\n", " 620 0.1546 23.2% 0.8m\n", " 630 0.1649 23.2% 0.7m\n", " 640 0.1538 23.2% 0.7m\n", " 650 0.2005 23.2% 0.7m\n", " 660 0.1823 23.2% 0.6m\n", " 670 0.1586 23.2% 0.6m\n", " 680 0.1906 23.2% 0.6m\n", " 690 0.1469 23.2% 0.5m\n", " 700 0.1712 23.2% 0.5m\n", " 710 0.1292 23.2% 0.5m\n", " 720 0.1326 23.2% 0.4m\n", " 730 0.1650 23.2% 0.4m\n", " 740 0.1775 23.2% 0.4m\n", " 750 0.1432 23.2% 0.4m\n", " 760 0.1915 23.2% 0.3m\n", " 770 0.1620 23.2% 0.3m\n", " 780 0.1637 23.2% 0.3m\n", " 790 0.1638 23.2% 0.2m\n", " 800 0.1038 23.2% 0.2m\n", " 810 0.1771 23.2% 0.2m\n", " 820 0.2012 23.2% 0.1m\n", " 830 0.1727 23.2% 0.1m\n", " 840 0.1496 23.2% 0.1m\n", " 850 0.1923 23.2% 0.0m\n", " 860 0.1490 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 30 COMPLETE in 162s\n", "\n", "โœ… EPOCH 30 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16705 โ†’ Val=0.16420\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 198m\n", "\n", "๐Ÿ”„ Epoch 31/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1863 23.2% 2.7m\n", " 10 0.1529 23.2% 2.6m\n", " 20 0.1882 23.2% 2.7m\n", " 30 0.1630 23.2% 2.6m\n", " 40 0.1862 23.2% 2.6m\n", " 50 0.1410 23.2% 2.6m\n", " 60 0.1245 23.2% 2.5m\n", " 70 0.1581 23.2% 2.5m\n", " 80 0.1894 23.2% 2.5m\n", " 90 0.1650 23.2% 2.4m\n", " 100 0.1623 23.2% 2.4m\n", " 110 0.1558 23.2% 2.4m\n", " 120 0.1938 23.2% 2.3m\n", " 130 0.2118 23.2% 2.3m\n", " 140 0.1666 23.2% 2.3m\n", " 150 0.1781 23.2% 2.2m\n", " 160 0.1441 23.2% 2.2m\n", " 170 0.1432 23.2% 2.2m\n", " 180 0.1580 23.2% 2.1m\n", " 190 0.1747 23.2% 2.1m\n", " 200 0.2255 23.2% 2.1m\n", " 210 0.1776 23.2% 2.0m\n", " 220 0.1447 23.2% 2.0m\n", " 230 0.1653 23.2% 2.0m\n", " 240 0.1579 23.2% 2.0m\n", " 250 0.1420 23.2% 1.9m\n", " 260 0.1442 23.2% 1.9m\n", " 270 0.1565 23.2% 1.9m\n", " 280 0.1850 23.2% 1.8m\n", " 290 0.1594 23.2% 1.8m\n", " 300 0.1471 23.2% 1.8m\n", " 310 0.1602 23.2% 1.7m\n", " 320 0.1841 23.2% 1.7m\n", " 330 0.1437 23.2% 1.7m\n", " 340 0.1933 23.2% 1.6m\n", " 350 0.1712 23.2% 1.6m\n", " 360 0.1745 23.2% 1.6m\n", " 370 0.1512 23.2% 1.5m\n", " 380 0.2005 23.2% 1.5m\n", " 390 0.1537 23.2% 1.5m\n", " 400 0.1755 23.2% 1.5m\n", " 410 0.1608 23.2% 1.4m\n", " 420 0.2001 23.2% 1.4m\n", " 430 0.1779 23.2% 1.4m\n", " 440 0.1595 23.2% 1.3m\n", " 450 0.1370 23.2% 1.3m\n", " 460 0.1567 23.2% 1.3m\n", " 470 0.1976 23.2% 1.2m\n", " 480 0.1667 23.2% 1.2m\n", " 490 0.1321 23.2% 1.2m\n", " 500 0.1460 23.2% 1.1m\n", " 510 0.1716 23.2% 1.1m\n", " 520 0.1794 23.2% 1.1m\n", " 530 0.1521 23.2% 1.0m\n", " 540 0.2002 23.2% 1.0m\n", " 550 0.1924 23.2% 1.0m\n", " 560 0.1721 23.2% 0.9m\n", " 570 0.1303 23.2% 0.9m\n", " 580 0.1926 23.2% 0.9m\n", " 590 0.1247 23.2% 0.9m\n", " 600 0.1295 23.2% 0.8m\n", " 610 0.1723 23.2% 0.8m\n", " 620 0.1801 23.2% 0.8m\n", " 630 0.1380 23.2% 0.7m\n", " 640 0.1388 23.2% 0.7m\n", " 650 0.1617 23.2% 0.7m\n", " 660 0.1853 23.2% 0.6m\n", " 670 0.1473 23.2% 0.6m\n", " 680 0.1662 23.2% 0.6m\n", " 690 0.1854 23.2% 0.5m\n", " 700 0.1758 23.2% 0.5m\n", " 710 0.1572 23.2% 0.5m\n", " 720 0.1678 23.2% 0.4m\n", " 730 0.2120 23.2% 0.4m\n", " 740 0.1704 23.2% 0.4m\n", " 750 0.1927 23.2% 0.4m\n", " 760 0.1193 23.2% 0.3m\n", " 770 0.1589 23.2% 0.3m\n", " 780 0.1886 23.2% 0.3m\n", " 790 0.1760 23.2% 0.2m\n", " 800 0.1531 23.2% 0.2m\n", " 810 0.1324 23.2% 0.2m\n", " 820 0.1753 23.2% 0.1m\n", " 830 0.1279 23.2% 0.1m\n", " 840 0.2046 23.2% 0.1m\n", " 850 0.1533 23.2% 0.0m\n", " 860 0.1762 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 31 COMPLETE in 162s\n", "\n", "โœ… EPOCH 31 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16706 โ†’ Val=0.16419\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 196m\n", "\n", "๐Ÿ”„ Epoch 32/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1265 23.2% 2.7m\n", " 10 0.1536 23.2% 2.7m\n", " 20 0.1997 23.2% 2.6m\n", " 30 0.1824 23.2% 2.6m\n", " 40 0.1682 23.2% 2.6m\n", " 50 0.1518 23.2% 2.5m\n", " 60 0.1798 23.2% 2.5m\n", " 70 0.1855 23.2% 2.5m\n", " 80 0.1872 23.2% 2.4m\n", " 90 0.2019 23.2% 2.4m\n", " 100 0.1860 23.2% 2.4m\n", " 110 0.2063 23.2% 2.4m\n", " 120 0.1519 23.2% 2.3m\n", " 130 0.1684 23.2% 2.3m\n", " 140 0.1791 23.2% 2.3m\n", " 150 0.1749 23.2% 2.2m\n", " 160 0.2285 23.2% 2.2m\n", " 170 0.1674 23.2% 2.2m\n", " 180 0.1583 23.2% 2.1m\n", " 190 0.1589 23.2% 2.1m\n", " 200 0.1706 23.2% 2.1m\n", " 210 0.2116 23.2% 2.0m\n", " 220 0.1893 23.2% 2.0m\n", " 230 0.2097 23.2% 2.0m\n", " 240 0.1768 23.2% 1.9m\n", " 250 0.1670 23.2% 1.9m\n", " 260 0.1877 23.2% 1.9m\n", " 270 0.1561 23.2% 1.8m\n", " 280 0.1670 23.2% 1.8m\n", " 290 0.2080 23.2% 1.8m\n", " 300 0.1598 23.2% 1.8m\n", " 310 0.1579 23.2% 1.7m\n", " 320 0.1596 23.2% 1.7m\n", " 330 0.1993 23.2% 1.7m\n", " 340 0.1870 23.2% 1.6m\n", " 350 0.2102 23.2% 1.6m\n", " 360 0.1709 23.2% 1.6m\n", " 370 0.1754 23.2% 1.5m\n", " 380 0.1749 23.2% 1.5m\n", " 390 0.1611 23.2% 1.5m\n", " 400 0.1552 23.2% 1.4m\n", " 410 0.1693 23.2% 1.4m\n", " 420 0.1471 23.2% 1.4m\n", " 430 0.1483 23.2% 1.3m\n", " 440 0.1668 23.2% 1.3m\n", " 450 0.1157 23.2% 1.3m\n", " 460 0.1614 23.2% 1.3m\n", " 470 0.1659 23.2% 1.2m\n", " 480 0.1799 23.2% 1.2m\n", " 490 0.2190 23.2% 1.2m\n", " 500 0.2133 23.2% 1.1m\n", " 510 0.1692 23.2% 1.1m\n", " 520 0.1575 23.2% 1.1m\n", " 530 0.1841 23.2% 1.0m\n", " 540 0.1910 23.2% 1.0m\n", " 550 0.1608 23.2% 1.0m\n", " 560 0.1653 23.2% 0.9m\n", " 570 0.1987 23.2% 0.9m\n", " 580 0.1565 23.2% 0.9m\n", " 590 0.1529 23.2% 0.9m\n", " 600 0.1742 23.2% 0.8m\n", " 610 0.1556 23.2% 0.8m\n", " 620 0.1364 23.2% 0.8m\n", " 630 0.1264 23.2% 0.7m\n", " 640 0.1711 23.2% 0.7m\n", " 650 0.1773 23.2% 0.7m\n", " 660 0.1309 23.2% 0.6m\n", " 670 0.1618 23.2% 0.6m\n", " 680 0.1459 23.2% 0.6m\n", " 690 0.1396 23.2% 0.5m\n", " 700 0.1794 23.2% 0.5m\n", " 710 0.1561 23.2% 0.5m\n", " 720 0.1431 23.2% 0.4m\n", " 730 0.1158 23.2% 0.4m\n", " 740 0.1594 23.2% 0.4m\n", " 750 0.1422 23.2% 0.4m\n", " 760 0.1671 23.2% 0.3m\n", " 770 0.1985 23.2% 0.3m\n", " 780 0.1540 23.2% 0.3m\n", " 790 0.1865 23.2% 0.2m\n", " 800 0.1933 23.2% 0.2m\n", " 810 0.1578 23.2% 0.2m\n", " 820 0.1806 23.2% 0.1m\n", " 830 0.1490 23.2% 0.1m\n", " 840 0.1610 23.2% 0.1m\n", " 850 0.1182 23.2% 0.0m\n", " 860 0.1655 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 32 COMPLETE in 162s\n", "\n", "โœ… EPOCH 32 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16698 โ†’ Val=0.16457\n", " ๐Ÿ“Š Acc: EC=81.5% EL=75.4% EJ=86.6% Overall=81.2%\n", " โš ๏ธ No improvement for 5/30\n", " โฑ๏ธ Remaining ETA โ‰ˆ 192m\n", "\n", "๐Ÿ”„ Epoch 33/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1600 23.2% 2.7m\n", " 10 0.1318 23.2% 2.6m\n", " 20 0.1765 23.2% 2.6m\n", " 30 0.1813 23.2% 2.6m\n", " 40 0.1414 23.2% 2.5m\n", " 50 0.1817 23.2% 2.5m\n", " 60 0.1615 23.2% 2.5m\n", " 70 0.1521 23.2% 2.5m\n", " 80 0.1576 23.2% 2.4m\n", " 90 0.1554 23.2% 2.4m\n", " 100 0.1591 23.2% 2.4m\n", " 110 0.1940 23.2% 2.3m\n", " 120 0.2145 23.2% 2.3m\n", " 130 0.1729 23.2% 2.3m\n", " 140 0.1999 23.2% 2.3m\n", " 150 0.1958 23.2% 2.2m\n", " 160 0.1316 23.2% 2.2m\n", " 170 0.1748 23.2% 2.2m\n", " 180 0.1625 23.2% 2.1m\n", " 190 0.1913 23.2% 2.1m\n", " 200 0.1879 23.2% 2.1m\n", " 210 0.1560 23.2% 2.0m\n", " 220 0.1644 23.2% 2.0m\n", " 230 0.1572 23.2% 2.0m\n", " 240 0.1598 23.2% 1.9m\n", " 250 0.1593 23.2% 1.9m\n", " 260 0.1373 23.2% 1.9m\n", " 270 0.1640 23.2% 1.9m\n", " 280 0.1748 23.2% 1.8m\n", " 290 0.1556 23.2% 1.8m\n", " 300 0.2012 23.2% 1.8m\n", " 310 0.1498 23.2% 1.7m\n", " 320 0.1369 23.2% 1.7m\n", " 330 0.1826 23.2% 1.7m\n", " 340 0.1510 23.2% 1.6m\n", " 350 0.2045 23.2% 1.6m\n", " 360 0.1960 23.2% 1.6m\n", " 370 0.1472 23.2% 1.5m\n", " 380 0.2035 23.2% 1.5m\n", " 390 0.1869 23.2% 1.5m\n", " 400 0.1913 23.2% 1.4m\n", " 410 0.1520 23.2% 1.4m\n", " 420 0.1386 23.2% 1.4m\n", " 430 0.2162 23.2% 1.4m\n", " 440 0.1556 23.2% 1.3m\n", " 450 0.1746 23.2% 1.3m\n", " 460 0.1887 23.2% 1.3m\n", " 470 0.1561 23.2% 1.2m\n", " 480 0.1377 23.2% 1.2m\n", " 490 0.1697 23.2% 1.2m\n", " 500 0.1517 23.2% 1.1m\n", " 510 0.1788 23.2% 1.1m\n", " 520 0.1831 23.2% 1.1m\n", " 530 0.1480 23.2% 1.0m\n", " 540 0.1769 23.2% 1.0m\n", " 550 0.1611 23.2% 1.0m\n", " 560 0.1922 23.2% 0.9m\n", " 570 0.1743 23.2% 0.9m\n", " 580 0.1694 23.2% 0.9m\n", " 590 0.1896 23.2% 0.9m\n", " 600 0.1763 23.2% 0.8m\n", " 610 0.1779 23.2% 0.8m\n", " 620 0.1530 23.2% 0.8m\n", " 630 0.1991 23.2% 0.7m\n", " 640 0.1663 23.2% 0.7m\n", " 650 0.2019 23.2% 0.7m\n", " 660 0.1849 23.2% 0.6m\n", " 670 0.1726 23.2% 0.6m\n", " 680 0.1713 23.2% 0.6m\n", " 690 0.1680 23.2% 0.5m\n", " 700 0.1534 23.2% 0.5m\n", " 710 0.2081 23.2% 0.5m\n", " 720 0.1809 23.2% 0.4m\n", " 730 0.2037 23.2% 0.4m\n", " 740 0.1732 23.2% 0.4m\n", " 750 0.1678 23.2% 0.4m\n", " 760 0.1856 23.2% 0.3m\n", " 770 0.1643 23.2% 0.3m\n", " 780 0.1558 23.2% 0.3m\n", " 790 0.1214 23.2% 0.2m\n", " 800 0.1543 23.2% 0.2m\n", " 810 0.1774 23.2% 0.2m\n", " 820 0.1800 23.2% 0.1m\n", " 830 0.1744 23.2% 0.1m\n", " 840 0.1910 23.2% 0.1m\n", " 850 0.1508 23.2% 0.0m\n", " 860 0.1386 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 33 COMPLETE in 162s\n", "\n", "โœ… EPOCH 33 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16691 โ†’ Val=0.16443\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.6% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 190m\n", "\n", "๐Ÿ”„ Epoch 34/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1677 23.2% 2.7m\n", " 10 0.1956 23.2% 2.7m\n", " 20 0.1511 23.2% 2.6m\n", " 30 0.1735 23.2% 2.6m\n", " 40 0.1202 23.2% 2.6m\n", " 50 0.1290 23.2% 2.6m\n", " 60 0.1483 23.2% 2.5m\n", " 70 0.1380 23.2% 2.5m\n", " 80 0.1899 23.2% 2.5m\n", " 90 0.1736 23.2% 2.4m\n", " 100 0.1472 23.2% 2.4m\n", " 110 0.1510 23.2% 2.4m\n", " 120 0.1481 23.2% 2.3m\n", " 130 0.1813 23.2% 2.3m\n", " 140 0.1760 23.2% 2.3m\n", " 150 0.2084 23.2% 2.2m\n", " 160 0.1524 23.2% 2.2m\n", " 170 0.1802 23.2% 2.2m\n", " 180 0.1473 23.2% 2.1m\n", " 190 0.1584 23.2% 2.1m\n", " 200 0.1649 23.2% 2.1m\n", " 210 0.1284 23.2% 2.0m\n", " 220 0.1270 23.2% 2.0m\n", " 230 0.1915 23.2% 2.0m\n", " 240 0.1637 23.2% 2.0m\n", " 250 0.1635 23.2% 1.9m\n", " 260 0.1505 23.2% 1.9m\n", " 270 0.1487 23.2% 1.9m\n", " 280 0.1714 23.2% 1.8m\n", " 290 0.1800 23.2% 1.8m\n", " 300 0.1610 23.2% 1.8m\n", " 310 0.1557 23.2% 1.7m\n", " 320 0.1673 23.2% 1.7m\n", " 330 0.1652 23.2% 1.7m\n", " 340 0.1539 23.2% 1.6m\n", " 350 0.1420 23.2% 1.6m\n", " 360 0.1244 23.2% 1.6m\n", " 370 0.1538 23.2% 1.5m\n", " 380 0.1333 23.2% 1.5m\n", " 390 0.1268 23.2% 1.5m\n", " 400 0.1880 23.2% 1.5m\n", " 410 0.1844 23.2% 1.4m\n", " 420 0.1627 23.2% 1.4m\n", " 430 0.1932 23.2% 1.4m\n", " 440 0.1498 23.2% 1.3m\n", " 450 0.2086 23.2% 1.3m\n", " 460 0.1478 23.2% 1.3m\n", " 470 0.1782 23.2% 1.2m\n", " 480 0.1280 23.2% 1.2m\n", " 490 0.1548 23.2% 1.2m\n", " 500 0.1677 23.2% 1.1m\n", " 510 0.1690 23.2% 1.1m\n", " 520 0.1383 23.2% 1.1m\n", " 530 0.1668 23.2% 1.0m\n", " 540 0.1964 23.2% 1.0m\n", " 550 0.1818 23.2% 1.0m\n", " 560 0.1844 23.2% 1.0m\n", " 570 0.1972 23.2% 0.9m\n", " 580 0.1450 23.2% 0.9m\n", " 590 0.1772 23.2% 0.9m\n", " 600 0.2183 23.2% 0.8m\n", " 610 0.1573 23.2% 0.8m\n", " 620 0.1851 23.2% 0.8m\n", " 630 0.1582 23.2% 0.7m\n", " 640 0.1668 23.2% 0.7m\n", " 650 0.1153 23.2% 0.7m\n", " 660 0.1431 23.2% 0.6m\n", " 670 0.1574 23.2% 0.6m\n", " 680 0.1633 23.2% 0.6m\n", " 690 0.1619 23.2% 0.5m\n", " 700 0.1700 23.2% 0.5m\n", " 710 0.1734 23.2% 0.5m\n", " 720 0.1543 23.2% 0.4m\n", " 730 0.1577 23.2% 0.4m\n", " 740 0.1693 23.2% 0.4m\n", " 750 0.2101 23.2% 0.4m\n", " 760 0.1352 23.2% 0.3m\n", " 770 0.1853 23.2% 0.3m\n", " 780 0.1437 23.2% 0.3m\n", " 790 0.1454 23.2% 0.2m\n", " 800 0.1618 23.2% 0.2m\n", " 810 0.1795 23.2% 0.2m\n", " 820 0.2156 23.2% 0.1m\n", " 830 0.1548 23.2% 0.1m\n", " 840 0.1455 23.2% 0.1m\n", " 850 0.1930 23.2% 0.0m\n", " 860 0.1759 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 34 COMPLETE in 162s\n", "\n", "โœ… EPOCH 34 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16700 โ†’ Val=0.16430\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.6% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 188m\n", "\n", "๐Ÿ”„ Epoch 35/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1306 23.2% 2.6m\n", " 10 0.1704 23.2% 2.6m\n", " 20 0.1881 23.2% 2.6m\n", " 30 0.1984 23.2% 2.6m\n", " 40 0.1357 23.2% 2.6m\n", " 50 0.2121 23.2% 2.5m\n", " 60 0.1809 23.2% 2.5m\n", " 70 0.1674 23.2% 2.5m\n", " 80 0.1829 23.2% 2.5m\n", " 90 0.1758 23.2% 2.4m\n", " 100 0.1772 23.2% 2.4m\n", " 110 0.1613 23.2% 2.4m\n", " 120 0.2180 23.2% 2.3m\n", " 130 0.1601 23.2% 2.3m\n", " 140 0.1662 23.2% 2.3m\n", " 150 0.1839 23.2% 2.2m\n", " 160 0.1839 23.2% 2.2m\n", " 170 0.1275 23.2% 2.2m\n", " 180 0.1994 23.2% 2.1m\n", " 190 0.1965 23.2% 2.1m\n", " 200 0.1381 23.2% 2.1m\n", " 210 0.1511 23.2% 2.0m\n", " 220 0.1788 23.2% 2.0m\n", " 230 0.1442 23.2% 2.0m\n", " 240 0.1875 23.2% 2.0m\n", " 250 0.1450 23.2% 1.9m\n", " 260 0.1626 23.2% 1.9m\n", " 270 0.1909 23.2% 1.9m\n", " 280 0.1741 23.2% 1.8m\n", " 290 0.1472 23.2% 1.8m\n", " 300 0.1746 23.2% 1.8m\n", " 310 0.1929 23.2% 1.7m\n", " 320 0.1347 23.2% 1.7m\n", " 330 0.1992 23.2% 1.7m\n", " 340 0.1713 23.2% 1.6m\n", " 350 0.1907 23.2% 1.6m\n", " 360 0.1924 23.2% 1.6m\n", " 370 0.1692 23.2% 1.5m\n", " 380 0.2092 23.2% 1.5m\n", " 390 0.1545 23.2% 1.5m\n", " 400 0.1724 23.2% 1.5m\n", " 410 0.1947 23.2% 1.4m\n", " 420 0.1221 23.2% 1.4m\n", " 430 0.1385 23.2% 1.4m\n", " 440 0.1727 23.2% 1.3m\n", " 450 0.1969 23.2% 1.3m\n", " 460 0.1697 23.2% 1.3m\n", " 470 0.1585 23.2% 1.2m\n", " 480 0.1974 23.2% 1.2m\n", " 490 0.1821 23.2% 1.2m\n", " 500 0.1376 23.2% 1.1m\n", " 510 0.2054 23.2% 1.1m\n", " 520 0.1494 23.2% 1.1m\n", " 530 0.1467 23.2% 1.0m\n", " 540 0.1923 23.2% 1.0m\n", " 550 0.1574 23.2% 1.0m\n", " 560 0.1722 23.2% 0.9m\n", " 570 0.1572 23.2% 0.9m\n", " 580 0.2052 23.2% 0.9m\n", " 590 0.1497 23.2% 0.9m\n", " 600 0.1619 23.2% 0.8m\n", " 610 0.1459 23.2% 0.8m\n", " 620 0.2018 23.2% 0.8m\n", " 630 0.1502 23.2% 0.7m\n", " 640 0.1208 23.2% 0.7m\n", " 650 0.1291 23.2% 0.7m\n", " 660 0.1696 23.2% 0.6m\n", " 670 0.1812 23.2% 0.6m\n", " 680 0.1816 23.2% 0.6m\n", " 690 0.1505 23.2% 0.5m\n", " 700 0.1750 23.2% 0.5m\n", " 710 0.1633 23.2% 0.5m\n", " 720 0.1397 23.2% 0.4m\n", " 730 0.1486 23.2% 0.4m\n", " 740 0.1784 23.2% 0.4m\n", " 750 0.1523 23.2% 0.4m\n", " 760 0.1414 23.2% 0.3m\n", " 770 0.1936 23.2% 0.3m\n", " 780 0.1401 23.2% 0.3m\n", " 790 0.2011 23.2% 0.2m\n", " 800 0.1819 23.2% 0.2m\n", " 810 0.1427 23.2% 0.2m\n", " 820 0.1907 23.2% 0.1m\n", " 830 0.1735 23.2% 0.1m\n", " 840 0.1859 23.2% 0.1m\n", " 850 0.1649 23.2% 0.0m\n", " 860 0.1618 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 35 COMPLETE in 162s\n", "\n", "โœ… EPOCH 35 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16700 โ†’ Val=0.16439\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 184m\n", "\n", "๐Ÿ”„ Epoch 36/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1503 23.2% 2.7m\n", " 10 0.1758 23.2% 2.6m\n", " 20 0.1389 23.2% 2.6m\n", " 30 0.2108 23.2% 2.6m\n", " 40 0.1770 23.2% 2.5m\n", " 50 0.1876 23.2% 2.5m\n", " 60 0.1781 23.2% 2.5m\n", " 70 0.1865 23.2% 2.5m\n", " 80 0.1601 23.2% 2.4m\n", " 90 0.1646 23.2% 2.4m\n", " 100 0.1675 23.2% 2.4m\n", " 110 0.1749 23.2% 2.3m\n", " 120 0.1913 23.2% 2.3m\n", " 130 0.1889 23.2% 2.3m\n", " 140 0.1783 23.2% 2.3m\n", " 150 0.1379 23.2% 2.2m\n", " 160 0.1472 23.2% 2.2m\n", " 170 0.1486 23.2% 2.2m\n", " 180 0.1969 23.2% 2.1m\n", " 190 0.1665 23.2% 2.1m\n", " 200 0.1775 23.2% 2.1m\n", " 210 0.2318 23.2% 2.0m\n", " 220 0.1575 23.2% 2.0m\n", " 230 0.1428 23.2% 2.0m\n", " 240 0.1995 23.2% 2.0m\n", " 250 0.1332 23.2% 1.9m\n", " 260 0.1623 23.2% 1.9m\n", " 270 0.1559 23.2% 1.9m\n", " 280 0.1688 23.2% 1.8m\n", " 290 0.1171 23.2% 1.8m\n", " 300 0.1886 23.2% 1.8m\n", " 310 0.1705 23.2% 1.7m\n", " 320 0.1761 23.2% 1.7m\n", " 330 0.1562 23.2% 1.7m\n", " 340 0.1681 23.2% 1.6m\n", " 350 0.1727 23.2% 1.6m\n", " 360 0.1507 23.2% 1.6m\n", " 370 0.1649 23.2% 1.5m\n", " 380 0.1648 23.2% 1.5m\n", " 390 0.1664 23.2% 1.5m\n", " 400 0.1583 23.2% 1.4m\n", " 410 0.1784 23.2% 1.4m\n", " 420 0.1735 23.2% 1.4m\n", " 430 0.1471 23.2% 1.4m\n", " 440 0.1695 23.2% 1.3m\n", " 450 0.1707 23.2% 1.3m\n", " 460 0.1540 23.2% 1.3m\n", " 470 0.1441 23.2% 1.2m\n", " 480 0.1601 23.2% 1.2m\n", " 490 0.1837 23.2% 1.2m\n", " 500 0.1637 23.2% 1.1m\n", " 510 0.1357 23.2% 1.1m\n", " 520 0.1835 23.2% 1.1m\n", " 530 0.1472 23.2% 1.0m\n", " 540 0.1429 23.2% 1.0m\n", " 550 0.1336 23.2% 1.0m\n", " 560 0.1770 23.2% 0.9m\n", " 570 0.1220 23.2% 0.9m\n", " 580 0.1749 23.2% 0.9m\n", " 590 0.1978 23.2% 0.9m\n", " 600 0.1613 23.2% 0.8m\n", " 610 0.1473 23.2% 0.8m\n", " 620 0.2704 23.2% 0.8m\n", " 630 0.1798 23.2% 0.7m\n", " 640 0.1548 23.2% 0.7m\n", " 650 0.1341 23.2% 0.7m\n", " 660 0.1921 23.2% 0.6m\n", " 670 0.1383 23.2% 0.6m\n", " 680 0.2126 23.2% 0.6m\n", " 690 0.1554 23.2% 0.5m\n", " 700 0.1461 23.2% 0.5m\n", " 710 0.1979 23.2% 0.5m\n", " 720 0.1930 23.2% 0.4m\n", " 730 0.1674 23.2% 0.4m\n", " 740 0.1532 23.2% 0.4m\n", " 750 0.1669 23.2% 0.4m\n", " 760 0.1363 23.2% 0.3m\n", " 770 0.1627 23.2% 0.3m\n", " 780 0.1594 23.2% 0.3m\n", " 790 0.1426 23.2% 0.2m\n", " 800 0.1865 23.2% 0.2m\n", " 810 0.2133 23.2% 0.2m\n", " 820 0.1452 23.2% 0.1m\n", " 830 0.1660 23.2% 0.1m\n", " 840 0.1772 23.2% 0.1m\n", " 850 0.1710 23.2% 0.0m\n", " 860 0.1762 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 36 COMPLETE in 162s\n", "\n", "โœ… EPOCH 36 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16702 โ†’ Val=0.16421\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 181m\n", "\n", "๐Ÿ”„ Epoch 37/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1706 23.2% 2.7m\n", " 10 0.1645 23.2% 2.6m\n", " 20 0.1520 23.2% 2.6m\n", " 30 0.1665 23.2% 2.6m\n", " 40 0.1825 23.2% 2.6m\n", " 50 0.1990 23.2% 2.5m\n", " 60 0.1491 23.2% 2.5m\n", " 70 0.1962 23.2% 2.5m\n", " 80 0.1559 23.2% 2.4m\n", " 90 0.1875 23.2% 2.4m\n", " 100 0.1821 23.2% 2.4m\n", " 110 0.1841 23.2% 2.3m\n", " 120 0.1521 23.2% 2.3m\n", " 130 0.1565 23.2% 2.3m\n", " 140 0.1701 23.2% 2.2m\n", " 150 0.1283 23.2% 2.2m\n", " 160 0.1390 23.2% 2.2m\n", " 170 0.1865 23.2% 2.2m\n", " 180 0.1596 23.2% 2.1m\n", " 190 0.1405 23.2% 2.1m\n", " 200 0.1445 23.2% 2.1m\n", " 210 0.1641 23.2% 2.0m\n", " 220 0.2201 23.2% 2.0m\n", " 230 0.1568 23.2% 2.0m\n", " 240 0.1572 23.2% 1.9m\n", " 250 0.1796 23.2% 1.9m\n", " 260 0.1842 23.2% 1.9m\n", " 270 0.1649 23.2% 1.8m\n", " 280 0.2124 23.2% 1.8m\n", " 290 0.1835 23.2% 1.8m\n", " 300 0.1804 23.2% 1.8m\n", " 310 0.2051 23.2% 1.7m\n", " 320 0.2081 23.2% 1.7m\n", " 330 0.1695 23.2% 1.7m\n", " 340 0.1616 23.2% 1.6m\n", " 350 0.1713 23.2% 1.6m\n", " 360 0.2001 23.2% 1.6m\n", " 370 0.1607 23.2% 1.5m\n", " 380 0.1801 23.2% 1.5m\n", " 390 0.1573 23.2% 1.5m\n", " 400 0.1241 23.2% 1.4m\n", " 410 0.1523 23.2% 1.4m\n", " 420 0.1474 23.2% 1.4m\n", " 430 0.1557 23.2% 1.3m\n", " 440 0.1524 23.2% 1.3m\n", " 450 0.1584 23.2% 1.3m\n", " 460 0.1814 23.2% 1.3m\n", " 470 0.1960 23.2% 1.2m\n", " 480 0.1608 23.2% 1.2m\n", " 490 0.1603 23.2% 1.2m\n", " 500 0.1914 23.2% 1.1m\n", " 510 0.1668 23.2% 1.1m\n", " 520 0.1557 23.2% 1.1m\n", " 530 0.1495 23.2% 1.0m\n", " 540 0.1681 23.2% 1.0m\n", " 550 0.1525 23.2% 1.0m\n", " 560 0.1179 23.2% 0.9m\n", " 570 0.1781 23.2% 0.9m\n", " 580 0.1900 23.2% 0.9m\n", " 590 0.1336 23.2% 0.8m\n", " 600 0.1740 23.2% 0.8m\n", " 610 0.1306 23.2% 0.8m\n", " 620 0.1636 23.2% 0.8m\n", " 630 0.1578 23.2% 0.7m\n", " 640 0.1809 23.2% 0.7m\n", " 650 0.1802 23.2% 0.7m\n", " 660 0.1700 23.2% 0.6m\n", " 670 0.1651 23.2% 0.6m\n", " 680 0.1644 23.2% 0.6m\n", " 690 0.1129 23.2% 0.5m\n", " 700 0.1515 23.2% 0.5m\n", " 710 0.1913 23.2% 0.5m\n", " 720 0.1681 23.2% 0.4m\n", " 730 0.1638 23.2% 0.4m\n", " 740 0.1408 23.2% 0.4m\n", " 750 0.1558 23.2% 0.4m\n", " 760 0.1494 23.2% 0.3m\n", " 770 0.1619 23.2% 0.3m\n", " 780 0.1487 23.2% 0.3m\n", " 790 0.1684 23.2% 0.2m\n", " 800 0.1584 23.2% 0.2m\n", " 810 0.1923 23.2% 0.2m\n", " 820 0.1372 23.2% 0.1m\n", " 830 0.1515 23.2% 0.1m\n", " 840 0.1317 23.2% 0.1m\n", " 850 0.2047 23.2% 0.0m\n", " 860 0.1861 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 37 COMPLETE in 161s\n", "\n", "โœ… EPOCH 37 SUMMARY\n", " โฑ๏ธ Time: 169s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16692 โ†’ Val=0.16431\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.2%\n", " โš ๏ธ No improvement for 10/30\n", " โฑ๏ธ Remaining ETA โ‰ˆ 177m\n", "\n", "๐Ÿ”„ Epoch 38/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1610 23.2% 2.6m\n", " 10 0.1731 23.2% 2.6m\n", " 20 0.1689 23.2% 2.6m\n", " 30 0.1612 23.2% 2.6m\n", " 40 0.1084 23.2% 2.6m\n", " 50 0.1877 23.2% 2.5m\n", " 60 0.1289 23.2% 2.5m\n", " 70 0.1813 23.2% 2.5m\n", " 80 0.1665 23.2% 2.4m\n", " 90 0.1562 23.2% 2.4m\n", " 100 0.1460 23.2% 2.4m\n", " 110 0.1509 23.2% 2.4m\n", " 120 0.1649 23.2% 2.3m\n", " 130 0.1951 23.2% 2.3m\n", " 140 0.1546 23.2% 2.3m\n", " 150 0.1636 23.2% 2.2m\n", " 160 0.1670 23.2% 2.2m\n", " 170 0.1738 23.2% 2.2m\n", " 180 0.1931 23.2% 2.1m\n", " 190 0.1546 23.2% 2.1m\n", " 200 0.1626 23.2% 2.1m\n", " 210 0.1630 23.2% 2.0m\n", " 220 0.1675 23.2% 2.0m\n", " 230 0.1555 23.2% 2.0m\n", " 240 0.1249 23.2% 2.0m\n", " 250 0.1239 23.2% 1.9m\n", " 260 0.1627 23.2% 1.9m\n", " 270 0.1737 23.2% 1.9m\n", " 280 0.1546 23.2% 1.8m\n", " 290 0.1708 23.2% 1.8m\n", " 300 0.1316 23.2% 1.8m\n", " 310 0.1936 23.2% 1.7m\n", " 320 0.1823 23.2% 1.7m\n", " 330 0.1694 23.2% 1.7m\n", " 340 0.1374 23.2% 1.6m\n", " 350 0.1551 23.2% 1.6m\n", " 360 0.1770 23.2% 1.6m\n", " 370 0.1803 23.2% 1.5m\n", " 380 0.1637 23.2% 1.5m\n", " 390 0.1543 23.2% 1.5m\n", " 400 0.1821 23.2% 1.4m\n", " 410 0.1233 23.2% 1.4m\n", " 420 0.2193 23.2% 1.4m\n", " 430 0.1827 23.2% 1.4m\n", " 440 0.1886 23.2% 1.3m\n", " 450 0.1644 23.2% 1.3m\n", " 460 0.1462 23.2% 1.3m\n", " 470 0.1625 23.2% 1.2m\n", " 480 0.1679 23.2% 1.2m\n", " 490 0.1396 23.2% 1.2m\n", " 500 0.1619 23.2% 1.1m\n", " 510 0.1391 23.2% 1.1m\n", " 520 0.1885 23.2% 1.1m\n", " 530 0.1656 23.2% 1.0m\n", " 540 0.1684 23.2% 1.0m\n", " 550 0.1763 23.2% 1.0m\n", " 560 0.1450 23.2% 0.9m\n", " 570 0.1745 23.2% 0.9m\n", " 580 0.1856 23.2% 0.9m\n", " 590 0.1711 23.2% 0.9m\n", " 600 0.1538 23.2% 0.8m\n", " 610 0.1923 23.2% 0.8m\n", " 620 0.1830 23.2% 0.8m\n", " 630 0.1398 23.2% 0.7m\n", " 640 0.1681 23.2% 0.7m\n", " 650 0.1357 23.2% 0.7m\n", " 660 0.1942 23.2% 0.6m\n", " 670 0.1669 23.2% 0.6m\n", " 680 0.1381 23.2% 0.6m\n", " 690 0.1593 23.2% 0.5m\n", " 700 0.1911 23.2% 0.5m\n", " 710 0.1694 23.2% 0.5m\n", " 720 0.1697 23.2% 0.4m\n", " 730 0.1632 23.2% 0.4m\n", " 740 0.1721 23.2% 0.4m\n", " 750 0.1641 23.2% 0.4m\n", " 760 0.1688 23.2% 0.3m\n", " 770 0.1876 23.2% 0.3m\n", " 780 0.1679 23.2% 0.3m\n", " 790 0.1840 23.2% 0.2m\n", " 800 0.1961 23.2% 0.2m\n", " 810 0.1589 23.2% 0.2m\n", " 820 0.1970 23.2% 0.1m\n", " 830 0.1510 23.2% 0.1m\n", " 840 0.1870 23.2% 0.1m\n", " 850 0.1719 23.2% 0.0m\n", " 860 0.1533 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 38 COMPLETE in 162s\n", "\n", "โœ… EPOCH 38 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16682 โ†’ Val=0.16402\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.8% Overall=81.3%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 175m\n", "\n", "๐Ÿ”„ Epoch 39/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1468 23.2% 3.2m\n", " 10 0.1856 23.2% 2.8m\n", " 20 0.1361 23.2% 2.7m\n", " 30 0.1883 23.2% 2.6m\n", " 40 0.1568 23.2% 2.6m\n", " 50 0.1638 23.2% 2.5m\n", " 60 0.1224 23.2% 2.5m\n", " 70 0.1709 23.2% 2.5m\n", " 80 0.2056 23.2% 2.4m\n", " 90 0.1162 23.2% 2.4m\n", " 100 0.1849 23.2% 2.4m\n", " 110 0.1438 23.2% 2.3m\n", " 120 0.1624 23.2% 2.3m\n", " 130 0.2199 23.2% 2.3m\n", " 140 0.1825 23.2% 2.3m\n", " 150 0.1438 23.2% 2.2m\n", " 160 0.1859 23.2% 2.2m\n", " 170 0.1695 23.2% 2.2m\n", " 180 0.1807 23.2% 2.1m\n", " 190 0.1581 23.2% 2.1m\n", " 200 0.1397 23.2% 2.1m\n", " 210 0.1466 23.2% 2.0m\n", " 220 0.1769 23.2% 2.0m\n", " 230 0.1521 23.2% 2.0m\n", " 240 0.1831 23.2% 1.9m\n", " 250 0.1131 23.2% 1.9m\n", " 260 0.1622 23.2% 1.9m\n", " 270 0.1378 23.2% 1.9m\n", " 280 0.1651 23.2% 1.8m\n", " 290 0.1702 23.2% 1.8m\n", " 300 0.2007 23.2% 1.8m\n", " 310 0.1663 23.2% 1.7m\n", " 320 0.1831 23.2% 1.7m\n", " 330 0.1994 23.2% 1.7m\n", " 340 0.1221 23.2% 1.6m\n", " 350 0.1536 23.2% 1.6m\n", " 360 0.1460 23.2% 1.6m\n", " 370 0.1540 23.2% 1.5m\n", " 380 0.2180 23.2% 1.5m\n", " 390 0.1860 23.2% 1.5m\n", " 400 0.1590 23.2% 1.4m\n", " 410 0.1601 23.2% 1.4m\n", " 420 0.1886 23.2% 1.4m\n", " 430 0.1572 23.2% 1.4m\n", " 440 0.1710 23.2% 1.3m\n", " 450 0.1729 23.2% 1.3m\n", " 460 0.1673 23.2% 1.3m\n", " 470 0.1369 23.2% 1.2m\n", " 480 0.1674 23.2% 1.2m\n", " 490 0.1749 23.2% 1.2m\n", " 500 0.1687 23.2% 1.1m\n", " 510 0.1468 23.2% 1.1m\n", " 520 0.1674 23.2% 1.1m\n", " 530 0.1952 23.2% 1.0m\n", " 540 0.1784 23.2% 1.0m\n", " 550 0.2042 23.2% 1.0m\n", " 560 0.1700 23.2% 0.9m\n", " 570 0.1989 23.2% 0.9m\n", " 580 0.2158 23.2% 0.9m\n", " 590 0.2061 23.2% 0.9m\n", " 600 0.1719 23.2% 0.8m\n", " 610 0.1523 23.2% 0.8m\n", " 620 0.1857 23.2% 0.8m\n", " 630 0.1742 23.2% 0.7m\n", " 640 0.1452 23.2% 0.7m\n", " 650 0.1815 23.2% 0.7m\n", " 660 0.1966 23.2% 0.6m\n", " 670 0.1876 23.2% 0.6m\n", " 680 0.1428 23.2% 0.6m\n", " 690 0.1370 23.2% 0.5m\n", " 700 0.1350 23.2% 0.5m\n", " 710 0.1782 23.2% 0.5m\n", " 720 0.1707 23.2% 0.4m\n", " 730 0.1765 23.2% 0.4m\n", " 740 0.1533 23.2% 0.4m\n", " 750 0.1931 23.2% 0.4m\n", " 760 0.1832 23.2% 0.3m\n", " 770 0.1797 23.2% 0.3m\n", " 780 0.1925 23.2% 0.3m\n", " 790 0.1681 23.2% 0.2m\n", " 800 0.1729 23.2% 0.2m\n", " 810 0.1612 23.2% 0.2m\n", " 820 0.1684 23.2% 0.1m\n", " 830 0.1488 23.2% 0.1m\n", " 840 0.1174 23.2% 0.1m\n", " 850 0.1946 23.2% 0.0m\n", " 860 0.1723 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 39 COMPLETE in 162s\n", "\n", "โœ… EPOCH 39 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16677 โ†’ Val=0.16444\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.6% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 173m\n", "\n", "๐Ÿ”„ Epoch 40/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1648 23.2% 2.7m\n", " 10 0.1650 23.2% 2.7m\n", " 20 0.1469 23.2% 2.7m\n", " 30 0.1505 23.2% 2.7m\n", " 40 0.1371 23.2% 2.6m\n", " 50 0.1731 23.2% 2.6m\n", " 60 0.1252 23.2% 2.6m\n", " 70 0.1745 23.2% 2.5m\n", " 80 0.1404 23.2% 2.5m\n", " 90 0.1827 23.2% 2.4m\n", " 100 0.1932 23.2% 2.4m\n", " 110 0.1426 23.2% 2.4m\n", " 120 0.1643 23.2% 2.3m\n", " 130 0.2053 23.2% 2.3m\n", " 140 0.1729 23.2% 2.3m\n", " 150 0.1834 23.2% 2.2m\n", " 160 0.1736 23.2% 2.2m\n", " 170 0.1357 23.2% 2.2m\n", " 180 0.1564 23.2% 2.1m\n", " 190 0.1615 23.2% 2.1m\n", " 200 0.1961 23.2% 2.1m\n", " 210 0.2009 23.2% 2.1m\n", " 220 0.1319 23.2% 2.0m\n", " 230 0.1593 23.2% 2.0m\n", " 240 0.1426 23.2% 2.0m\n", " 250 0.1546 23.2% 1.9m\n", " 260 0.1718 23.2% 1.9m\n", " 270 0.1422 23.2% 1.9m\n", " 280 0.1678 23.2% 1.8m\n", " 290 0.1966 23.2% 1.8m\n", " 300 0.1761 23.2% 1.8m\n", " 310 0.1464 23.2% 1.7m\n", " 320 0.1388 23.2% 1.7m\n", " 330 0.1516 23.2% 1.7m\n", " 340 0.1792 23.2% 1.6m\n", " 350 0.1747 23.2% 1.6m\n", " 360 0.1901 23.2% 1.6m\n", " 370 0.1968 23.2% 1.5m\n", " 380 0.1757 23.2% 1.5m\n", " 390 0.1735 23.2% 1.5m\n", " 400 0.1602 23.2% 1.5m\n", " 410 0.1836 23.2% 1.4m\n", " 420 0.1591 23.2% 1.4m\n", " 430 0.1954 23.2% 1.4m\n", " 440 0.1476 23.2% 1.3m\n", " 450 0.1558 23.2% 1.3m\n", " 460 0.1661 23.2% 1.3m\n", " 470 0.1587 23.2% 1.2m\n", " 480 0.1865 23.2% 1.2m\n", " 490 0.1165 23.2% 1.2m\n", " 500 0.2042 23.2% 1.1m\n", " 510 0.1683 23.2% 1.1m\n", " 520 0.1638 23.2% 1.1m\n", " 530 0.1926 23.2% 1.0m\n", " 540 0.1992 23.2% 1.0m\n", " 550 0.1782 23.2% 1.0m\n", " 560 0.1856 23.2% 1.0m\n", " 570 0.1972 23.2% 0.9m\n", " 580 0.1756 23.2% 0.9m\n", " 590 0.1665 23.2% 0.9m\n", " 600 0.1505 23.2% 0.8m\n", " 610 0.1343 23.2% 0.8m\n", " 620 0.1516 23.2% 0.8m\n", " 630 0.1914 23.2% 0.7m\n", " 640 0.1346 23.2% 0.7m\n", " 650 0.1594 23.2% 0.7m\n", " 660 0.1463 23.2% 0.6m\n", " 670 0.1614 23.2% 0.6m\n", " 680 0.1772 23.2% 0.6m\n", " 690 0.1977 23.2% 0.5m\n", " 700 0.1681 23.2% 0.5m\n", " 710 0.1790 23.2% 0.5m\n", " 720 0.1281 23.2% 0.4m\n", " 730 0.1834 23.2% 0.4m\n", " 740 0.1872 23.2% 0.4m\n", " 750 0.1744 23.2% 0.4m\n", " 760 0.1663 23.2% 0.3m\n", " 770 0.1267 23.2% 0.3m\n", " 780 0.1806 23.2% 0.3m\n", " 790 0.1799 23.2% 0.2m\n", " 800 0.1750 23.2% 0.2m\n", " 810 0.1865 23.2% 0.2m\n", " 820 0.1704 23.2% 0.1m\n", " 830 0.2173 23.2% 0.1m\n", " 840 0.1551 23.2% 0.1m\n", " 850 0.1358 23.2% 0.0m\n", " 860 0.1361 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 40 COMPLETE in 163s\n", "\n", "โœ… EPOCH 40 SUMMARY\n", " โฑ๏ธ Time: 171s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16691 โ†’ Val=0.16423\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 171m\n", "\n", "๐Ÿ”„ Epoch 41/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1245 23.2% 2.8m\n", " 10 0.1597 23.2% 2.7m\n", " 20 0.1572 23.2% 2.7m\n", " 30 0.1726 23.2% 2.6m\n", " 40 0.1440 23.2% 2.6m\n", " 50 0.1656 23.2% 2.6m\n", " 60 0.1773 23.2% 2.5m\n", " 70 0.1552 23.2% 2.5m\n", " 80 0.1472 23.2% 2.5m\n", " 90 0.1701 23.2% 2.4m\n", " 100 0.1590 23.2% 2.4m\n", " 110 0.2005 23.2% 2.4m\n", " 120 0.1670 23.2% 2.3m\n", " 130 0.2091 23.2% 2.3m\n", " 140 0.1552 23.2% 2.3m\n", " 150 0.1826 23.2% 2.3m\n", " 160 0.1691 23.2% 2.2m\n", " 170 0.1942 23.2% 2.2m\n", " 180 0.1430 23.2% 2.2m\n", " 190 0.1677 23.2% 2.1m\n", " 200 0.1446 23.2% 2.1m\n", " 210 0.1682 23.2% 2.1m\n", " 220 0.1911 23.2% 2.0m\n", " 230 0.1861 23.2% 2.0m\n", " 240 0.1569 23.2% 2.0m\n", " 250 0.1540 23.2% 1.9m\n", " 260 0.1258 23.2% 1.9m\n", " 270 0.1690 23.2% 1.9m\n", " 280 0.1837 23.2% 1.8m\n", " 290 0.1479 23.2% 1.8m\n", " 300 0.2147 23.2% 1.8m\n", " 310 0.1657 23.2% 1.7m\n", " 320 0.2066 23.2% 1.7m\n", " 330 0.1436 23.2% 1.7m\n", " 340 0.1789 23.2% 1.6m\n", " 350 0.1691 23.2% 1.6m\n", " 360 0.1602 23.2% 1.6m\n", " 370 0.1887 23.2% 1.6m\n", " 380 0.1561 23.2% 1.5m\n", " 390 0.1846 23.2% 1.5m\n", " 400 0.1880 23.2% 1.5m\n", " 410 0.1791 23.2% 1.4m\n", " 420 0.1569 23.2% 1.4m\n", " 430 0.1901 23.2% 1.4m\n", " 440 0.1992 23.2% 1.3m\n", " 450 0.1828 23.2% 1.3m\n", " 460 0.1745 23.2% 1.3m\n", " 470 0.1832 23.2% 1.2m\n", " 480 0.1930 23.2% 1.2m\n", " 490 0.1610 23.2% 1.2m\n", " 500 0.2084 23.2% 1.1m\n", " 510 0.1788 23.2% 1.1m\n", " 520 0.1541 23.2% 1.1m\n", " 530 0.1925 23.2% 1.0m\n", " 540 0.1361 23.2% 1.0m\n", " 550 0.1828 23.2% 1.0m\n", " 560 0.1495 23.2% 1.0m\n", " 570 0.1278 23.2% 0.9m\n", " 580 0.1650 23.2% 0.9m\n", " 590 0.1580 23.2% 0.9m\n", " 600 0.1770 23.2% 0.8m\n", " 610 0.1635 23.2% 0.8m\n", " 620 0.1806 23.2% 0.8m\n", " 630 0.1533 23.2% 0.7m\n", " 640 0.1363 23.2% 0.7m\n", " 650 0.1517 23.2% 0.7m\n", " 660 0.1982 23.2% 0.6m\n", " 670 0.1789 23.2% 0.6m\n", " 680 0.1786 23.2% 0.6m\n", " 690 0.1898 23.2% 0.5m\n", " 700 0.2053 23.2% 0.5m\n", " 710 0.1730 23.2% 0.5m\n", " 720 0.1654 23.2% 0.5m\n", " 730 0.1686 23.2% 0.4m\n", " 740 0.1626 23.2% 0.4m\n", " 750 0.1723 23.2% 0.4m\n", " 760 0.1467 23.2% 0.3m\n", " 770 0.1835 23.2% 0.3m\n", " 780 0.1135 23.2% 0.3m\n", " 790 0.1701 23.2% 0.2m\n", " 800 0.1864 23.2% 0.2m\n", " 810 0.1312 23.2% 0.2m\n", " 820 0.1640 23.2% 0.1m\n", " 830 0.1618 23.2% 0.1m\n", " 840 0.2036 23.2% 0.1m\n", " 850 0.1900 23.2% 0.0m\n", " 860 0.1385 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 41 COMPLETE in 163s\n", "\n", "โœ… EPOCH 41 SUMMARY\n", " โฑ๏ธ Time: 171s (2.9m)\n", " ๐Ÿ“‰ Loss: Train=0.16696 โ†’ Val=0.16421\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 168m\n", "\n", "๐Ÿ”„ Epoch 42/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1692 23.2% 2.8m\n", " 10 0.1681 23.2% 2.7m\n", " 20 0.1644 23.2% 2.7m\n", " 30 0.1210 23.2% 2.6m\n", " 40 0.1972 23.2% 2.6m\n", " 50 0.1769 23.2% 2.6m\n", " 60 0.1355 23.2% 2.5m\n", " 70 0.1693 23.2% 2.5m\n", " 80 0.1827 23.2% 2.5m\n", " 90 0.1844 23.2% 2.4m\n", " 100 0.1781 23.2% 2.4m\n", " 110 0.1794 23.2% 2.4m\n", " 120 0.1803 23.2% 2.3m\n", " 130 0.1733 23.2% 2.3m\n", " 140 0.1765 23.2% 2.3m\n", " 150 0.1523 23.2% 2.2m\n", " 160 0.1449 23.2% 2.2m\n", " 170 0.1799 23.2% 2.2m\n", " 180 0.1441 23.2% 2.1m\n", " 190 0.1941 23.2% 2.1m\n", " 200 0.1648 23.2% 2.1m\n", " 210 0.1615 23.2% 2.0m\n", " 220 0.1859 23.2% 2.0m\n", " 230 0.1332 23.2% 2.0m\n", " 240 0.1487 23.2% 1.9m\n", " 250 0.1362 23.2% 1.9m\n", " 260 0.1708 23.2% 1.9m\n", " 270 0.1744 23.2% 1.8m\n", " 280 0.1820 23.2% 1.8m\n", " 290 0.1350 23.2% 1.8m\n", " 300 0.1725 23.2% 1.8m\n", " 310 0.1882 23.2% 1.7m\n", " 320 0.1723 23.2% 1.7m\n", " 330 0.1493 23.2% 1.7m\n", " 340 0.1738 23.2% 1.6m\n", " 350 0.1313 23.2% 1.6m\n", " 360 0.1833 23.2% 1.6m\n", " 370 0.1440 23.2% 1.5m\n", " 380 0.1536 23.2% 1.5m\n", " 390 0.1826 23.2% 1.5m\n", " 400 0.1402 23.2% 1.4m\n", " 410 0.1874 23.2% 1.4m\n", " 420 0.2039 23.2% 1.4m\n", " 430 0.1655 23.2% 1.4m\n", " 440 0.1430 23.2% 1.3m\n", " 450 0.1744 23.2% 1.3m\n", " 460 0.1773 23.2% 1.3m\n", " 470 0.1586 23.2% 1.2m\n", " 480 0.1774 23.2% 1.2m\n", " 490 0.1609 23.2% 1.2m\n", " 500 0.2073 23.2% 1.1m\n", " 510 0.1273 23.2% 1.1m\n", " 520 0.1694 23.2% 1.1m\n", " 530 0.1879 23.2% 1.0m\n", " 540 0.1314 23.2% 1.0m\n", " 550 0.1549 23.2% 1.0m\n", " 560 0.1433 23.2% 0.9m\n", " 570 0.1906 23.2% 0.9m\n", " 580 0.1846 23.2% 0.9m\n", " 590 0.1823 23.2% 0.9m\n", " 600 0.1594 23.2% 0.8m\n", " 610 0.1732 23.2% 0.8m\n", " 620 0.1766 23.2% 0.8m\n", " 630 0.1711 23.2% 0.7m\n", " 640 0.1438 23.2% 0.7m\n", " 650 0.1642 23.2% 0.7m\n", " 660 0.1697 23.2% 0.6m\n", " 670 0.1620 23.2% 0.6m\n", " 680 0.1564 23.2% 0.6m\n", " 690 0.1709 23.2% 0.5m\n", " 700 0.1805 23.2% 0.5m\n", " 710 0.1813 23.2% 0.5m\n", " 720 0.1855 23.2% 0.4m\n", " 730 0.2039 23.2% 0.4m\n", " 740 0.1759 23.2% 0.4m\n", " 750 0.1312 23.2% 0.4m\n", " 760 0.1674 23.2% 0.3m\n", " 770 0.1422 23.2% 0.3m\n", " 780 0.1878 23.2% 0.3m\n", " 790 0.1726 23.2% 0.2m\n", " 800 0.1938 23.2% 0.2m\n", " 810 0.1532 23.2% 0.2m\n", " 820 0.1755 23.2% 0.1m\n", " 830 0.1692 23.2% 0.1m\n", " 840 0.1706 23.2% 0.1m\n", " 850 0.1595 23.2% 0.0m\n", " 860 0.1691 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 42 COMPLETE in 162s\n", "\n", "โœ… EPOCH 42 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16688 โ†’ Val=0.16426\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.6% Overall=81.2%\n", " โš ๏ธ No improvement for 15/30\n", " โฑ๏ธ Remaining ETA โ‰ˆ 164m\n", "\n", "๐Ÿ”„ Epoch 43/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1739 23.2% 2.8m\n", " 10 0.1641 23.2% 2.6m\n", " 20 0.1649 23.2% 2.6m\n", " 30 0.1805 23.2% 2.6m\n", " 40 0.1427 23.2% 2.6m\n", " 50 0.1830 23.2% 2.5m\n", " 60 0.1719 23.2% 2.5m\n", " 70 0.1411 23.2% 2.5m\n", " 80 0.1611 23.2% 2.5m\n", " 90 0.1701 23.2% 2.4m\n", " 100 0.1508 23.2% 2.4m\n", " 110 0.1775 23.2% 2.4m\n", " 120 0.1615 23.2% 2.3m\n", " 130 0.1482 23.2% 2.3m\n", " 140 0.1524 23.2% 2.3m\n", " 150 0.1654 23.2% 2.2m\n", " 160 0.1544 23.2% 2.2m\n", " 170 0.2314 23.2% 2.2m\n", " 180 0.1476 23.2% 2.2m\n", " 190 0.1584 23.2% 2.1m\n", " 200 0.1698 23.2% 2.1m\n", " 210 0.1514 23.2% 2.1m\n", " 220 0.1775 23.2% 2.0m\n", " 230 0.1620 23.2% 2.0m\n", " 240 0.1631 23.2% 2.0m\n", " 250 0.1603 23.2% 1.9m\n", " 260 0.2000 23.2% 1.9m\n", " 270 0.1390 23.2% 1.9m\n", " 280 0.1540 23.2% 1.8m\n", " 290 0.2018 23.2% 1.8m\n", " 300 0.1582 23.2% 1.8m\n", " 310 0.1632 23.2% 1.7m\n", " 320 0.1706 23.2% 1.7m\n", " 330 0.1777 23.2% 1.7m\n", " 340 0.1490 23.2% 1.6m\n", " 350 0.1536 23.2% 1.6m\n", " 360 0.1801 23.2% 1.6m\n", " 370 0.1614 23.2% 1.5m\n", " 380 0.1082 23.2% 1.5m\n", " 390 0.1832 23.2% 1.5m\n", " 400 0.1497 23.2% 1.5m\n", " 410 0.1563 23.2% 1.4m\n", " 420 0.2119 23.2% 1.4m\n", " 430 0.1633 23.2% 1.4m\n", " 440 0.1938 23.2% 1.3m\n", " 450 0.1635 23.2% 1.3m\n", " 460 0.1394 23.2% 1.3m\n", " 470 0.1419 23.2% 1.2m\n", " 480 0.1523 23.2% 1.2m\n", " 490 0.1801 23.2% 1.2m\n", " 500 0.1797 23.2% 1.1m\n", " 510 0.1601 23.2% 1.1m\n", " 520 0.1475 23.2% 1.1m\n", " 530 0.1556 23.2% 1.0m\n", " 540 0.1316 23.2% 1.0m\n", " 550 0.1541 23.2% 1.0m\n", " 560 0.1748 23.2% 1.0m\n", " 570 0.1180 23.2% 0.9m\n", " 580 0.2223 23.2% 0.9m\n", " 590 0.2015 23.2% 0.9m\n", " 600 0.1409 23.2% 0.8m\n", " 610 0.1549 23.2% 0.8m\n", " 620 0.1572 23.2% 0.8m\n", " 630 0.1955 23.2% 0.7m\n", " 640 0.1611 23.2% 0.7m\n", " 650 0.1494 23.2% 0.7m\n", " 660 0.1729 23.2% 0.6m\n", " 670 0.1823 23.2% 0.6m\n", " 680 0.1424 23.2% 0.6m\n", " 690 0.1949 23.2% 0.5m\n", " 700 0.2105 23.2% 0.5m\n", " 710 0.1817 23.2% 0.5m\n", " 720 0.1381 23.2% 0.4m\n", " 730 0.1678 23.2% 0.4m\n", " 740 0.1284 23.2% 0.4m\n", " 750 0.1766 23.2% 0.4m\n", " 760 0.2068 23.2% 0.3m\n", " 770 0.1806 23.2% 0.3m\n", " 780 0.1773 23.2% 0.3m\n", " 790 0.1678 23.2% 0.2m\n", " 800 0.1927 23.2% 0.2m\n", " 810 0.1807 23.2% 0.2m\n", " 820 0.1320 23.2% 0.1m\n", " 830 0.1499 23.2% 0.1m\n", " 840 0.1883 23.2% 0.1m\n", " 850 0.1587 23.2% 0.0m\n", " 860 0.1556 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 43 COMPLETE in 163s\n", "\n", "โœ… EPOCH 43 SUMMARY\n", " โฑ๏ธ Time: 171s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16678 โ†’ Val=0.16424\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.6% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 162m\n", "\n", "๐Ÿ”„ Epoch 44/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1480 23.2% 2.6m\n", " 10 0.1603 23.2% 2.6m\n", " 20 0.1662 23.2% 2.6m\n", " 30 0.1517 23.2% 2.6m\n", " 40 0.1760 23.2% 2.6m\n", " 50 0.1919 23.2% 2.5m\n", " 60 0.1615 23.2% 2.5m\n", " 70 0.2222 23.2% 2.5m\n", " 80 0.1815 23.2% 2.5m\n", " 90 0.2182 23.2% 2.4m\n", " 100 0.1501 23.2% 2.4m\n", " 110 0.2107 23.2% 2.4m\n", " 120 0.1732 23.2% 2.3m\n", " 130 0.1297 23.2% 2.3m\n", " 140 0.1664 23.2% 2.3m\n", " 150 0.1818 23.2% 2.2m\n", " 160 0.1887 23.2% 2.2m\n", " 170 0.1489 23.2% 2.2m\n", " 180 0.1832 23.2% 2.1m\n", " 190 0.1635 23.2% 2.1m\n", " 200 0.1343 23.2% 2.1m\n", " 210 0.1769 23.2% 2.0m\n", " 220 0.1578 23.2% 2.0m\n", " 230 0.1395 23.2% 2.0m\n", " 240 0.1665 23.2% 2.0m\n", " 250 0.1752 23.2% 1.9m\n", " 260 0.1547 23.2% 1.9m\n", " 270 0.1784 23.2% 1.9m\n", " 280 0.1522 23.2% 1.8m\n", " 290 0.1666 23.2% 1.8m\n", " 300 0.1419 23.2% 1.8m\n", " 310 0.1839 23.2% 1.7m\n", " 320 0.1547 23.2% 1.7m\n", " 330 0.1442 23.2% 1.7m\n", " 340 0.1604 23.2% 1.6m\n", " 350 0.1765 23.2% 1.6m\n", " 360 0.1743 23.2% 1.6m\n", " 370 0.1711 23.2% 1.5m\n", " 380 0.1624 23.2% 1.5m\n", " 390 0.1647 23.2% 1.5m\n", " 400 0.1478 23.2% 1.5m\n", " 410 0.1914 23.2% 1.4m\n", " 420 0.1552 23.2% 1.4m\n", " 430 0.1808 23.2% 1.4m\n", " 440 0.1322 23.2% 1.3m\n", " 450 0.1225 23.2% 1.3m\n", " 460 0.1867 23.2% 1.3m\n", " 470 0.2174 23.2% 1.2m\n", " 480 0.1659 23.2% 1.2m\n", " 490 0.1899 23.2% 1.2m\n", " 500 0.1620 23.2% 1.1m\n", " 510 0.1690 23.2% 1.1m\n", " 520 0.1443 23.2% 1.1m\n", " 530 0.1806 23.2% 1.0m\n", " 540 0.1719 23.2% 1.0m\n", " 550 0.1829 23.2% 1.0m\n", " 560 0.1928 23.2% 0.9m\n", " 570 0.1734 23.2% 0.9m\n", " 580 0.1783 23.2% 0.9m\n", " 590 0.1527 23.2% 0.9m\n", " 600 0.1626 23.2% 0.8m\n", " 610 0.1498 23.2% 0.8m\n", " 620 0.1896 23.2% 0.8m\n", " 630 0.1724 23.2% 0.7m\n", " 640 0.1491 23.2% 0.7m\n", " 650 0.1760 23.2% 0.7m\n", " 660 0.1725 23.2% 0.6m\n", " 670 0.1529 23.2% 0.6m\n", " 680 0.1579 23.2% 0.6m\n", " 690 0.1445 23.2% 0.5m\n", " 700 0.1955 23.2% 0.5m\n", " 710 0.1823 23.2% 0.5m\n", " 720 0.1710 23.2% 0.4m\n", " 730 0.1396 23.2% 0.4m\n", " 740 0.1271 23.2% 0.4m\n", " 750 0.1645 23.2% 0.4m\n", " 760 0.1373 23.2% 0.3m\n", " 770 0.1920 23.2% 0.3m\n", " 780 0.1680 23.2% 0.3m\n", " 790 0.1814 23.2% 0.2m\n", " 800 0.1820 23.2% 0.2m\n", " 810 0.1398 23.2% 0.2m\n", " 820 0.1760 23.2% 0.1m\n", " 830 0.1657 23.2% 0.1m\n", " 840 0.1824 23.2% 0.1m\n", " 850 0.1617 23.2% 0.0m\n", " 860 0.1258 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 44 COMPLETE in 162s\n", "\n", "โœ… EPOCH 44 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16683 โ†’ Val=0.16410\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 159m\n", "\n", "๐Ÿ”„ Epoch 45/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1606 23.2% 2.7m\n", " 10 0.1780 23.2% 2.7m\n", " 20 0.1808 23.2% 2.7m\n", " 30 0.1660 23.2% 2.6m\n", " 40 0.1104 23.2% 2.6m\n", " 50 0.1540 23.2% 2.5m\n", " 60 0.2006 23.2% 2.5m\n", " 70 0.1639 23.2% 2.5m\n", " 80 0.1508 23.2% 2.5m\n", " 90 0.1630 23.2% 2.4m\n", " 100 0.1539 23.2% 2.4m\n", " 110 0.1285 23.2% 2.4m\n", " 120 0.1797 23.2% 2.3m\n", " 130 0.1853 23.2% 2.3m\n", " 140 0.1571 23.2% 2.3m\n", " 150 0.1749 23.2% 2.2m\n", " 160 0.1364 23.2% 2.2m\n", " 170 0.1311 23.2% 2.2m\n", " 180 0.1808 23.2% 2.1m\n", " 190 0.1454 23.2% 2.1m\n", " 200 0.1618 23.2% 2.1m\n", " 210 0.1787 23.2% 2.0m\n", " 220 0.1770 23.2% 2.0m\n", " 230 0.1779 23.2% 2.0m\n", " 240 0.1493 23.2% 2.0m\n", " 250 0.1909 23.2% 1.9m\n", " 260 0.1844 23.2% 1.9m\n", " 270 0.1844 23.2% 1.9m\n", " 280 0.1781 23.2% 1.8m\n", " 290 0.1630 23.2% 1.8m\n", " 300 0.1722 23.2% 1.8m\n", " 310 0.1906 23.2% 1.7m\n", " 320 0.1644 23.2% 1.7m\n", " 330 0.1693 23.2% 1.7m\n", " 340 0.1911 23.2% 1.6m\n", " 350 0.1821 23.2% 1.6m\n", " 360 0.1821 23.2% 1.6m\n", " 370 0.1558 23.2% 1.5m\n", " 380 0.1721 23.2% 1.5m\n", " 390 0.1461 23.2% 1.5m\n", " 400 0.1815 23.2% 1.5m\n", " 410 0.1803 23.2% 1.4m\n", " 420 0.2168 23.2% 1.4m\n", " 430 0.1660 23.2% 1.4m\n", " 440 0.1816 23.2% 1.3m\n", " 450 0.1616 23.2% 1.3m\n", " 460 0.1717 23.2% 1.3m\n", " 470 0.1890 23.2% 1.2m\n", " 480 0.1605 23.2% 1.2m\n", " 490 0.1389 23.2% 1.2m\n", " 500 0.1694 23.2% 1.1m\n", " 510 0.2012 23.2% 1.1m\n", " 520 0.1612 23.2% 1.1m\n", " 530 0.1577 23.2% 1.0m\n", " 540 0.1660 23.2% 1.0m\n", " 550 0.1381 23.2% 1.0m\n", " 560 0.1715 23.2% 1.0m\n", " 570 0.1617 23.2% 0.9m\n", " 580 0.1573 23.2% 0.9m\n", " 590 0.1454 23.2% 0.9m\n", " 600 0.1302 23.2% 0.8m\n", " 610 0.1572 23.2% 0.8m\n", " 620 0.1586 23.2% 0.8m\n", " 630 0.1524 23.2% 0.7m\n", " 640 0.1635 23.2% 0.7m\n", " 650 0.1927 23.2% 0.7m\n", " 660 0.1207 23.2% 0.6m\n", " 670 0.1526 23.2% 0.6m\n", " 680 0.1818 23.2% 0.6m\n", " 690 0.2094 23.2% 0.5m\n", " 700 0.1597 23.2% 0.5m\n", " 710 0.1703 23.2% 0.5m\n", " 720 0.1584 23.2% 0.4m\n", " 730 0.1688 23.2% 0.4m\n", " 740 0.1546 23.2% 0.4m\n", " 750 0.1916 23.2% 0.4m\n", " 760 0.2073 23.2% 0.3m\n", " 770 0.1735 23.2% 0.3m\n", " 780 0.1537 23.2% 0.3m\n", " 790 0.1896 23.2% 0.2m\n", " 800 0.1815 23.2% 0.2m\n", " 810 0.1464 23.2% 0.2m\n", " 820 0.1750 23.2% 0.1m\n", " 830 0.1918 23.2% 0.1m\n", " 840 0.1668 23.2% 0.1m\n", " 850 0.1618 23.2% 0.0m\n", " 860 0.1417 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 45 COMPLETE in 163s\n", "\n", "โœ… EPOCH 45 SUMMARY\n", " โฑ๏ธ Time: 171s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16683 โ†’ Val=0.16424\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 157m\n", "\n", "๐Ÿ”„ Epoch 46/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1733 23.2% 2.7m\n", " 10 0.1726 23.2% 2.7m\n", " 20 0.1437 23.2% 2.7m\n", " 30 0.1695 23.2% 2.6m\n", " 40 0.1579 23.2% 2.6m\n", " 50 0.1666 23.2% 2.6m\n", " 60 0.1327 23.2% 2.5m\n", " 70 0.1747 23.2% 2.5m\n", " 80 0.1443 23.2% 2.5m\n", " 90 0.1769 23.2% 2.4m\n", " 100 0.1754 23.2% 2.4m\n", " 110 0.1536 23.2% 2.4m\n", " 120 0.1510 23.2% 2.3m\n", " 130 0.1585 23.2% 2.3m\n", " 140 0.1418 23.2% 2.3m\n", " 150 0.1559 23.2% 2.2m\n", " 160 0.1963 23.2% 2.2m\n", " 170 0.1509 23.2% 2.2m\n", " 180 0.1986 23.2% 2.1m\n", " 190 0.1716 23.2% 2.1m\n", " 200 0.2109 23.2% 2.1m\n", " 210 0.1733 23.2% 2.0m\n", " 220 0.1894 23.2% 2.0m\n", " 230 0.1970 23.2% 2.0m\n", " 240 0.1647 23.2% 2.0m\n", " 250 0.1462 23.2% 1.9m\n", " 260 0.1548 23.2% 1.9m\n", " 270 0.1524 23.2% 1.9m\n", " 280 0.1573 23.2% 1.8m\n", " 290 0.1844 23.2% 1.8m\n", " 300 0.1867 23.2% 1.8m\n", " 310 0.1804 23.2% 1.7m\n", " 320 0.1729 23.2% 1.7m\n", " 330 0.1484 23.2% 1.7m\n", " 340 0.1574 23.2% 1.6m\n", " 350 0.1604 23.2% 1.6m\n", " 360 0.1807 23.2% 1.6m\n", " 370 0.1723 23.2% 1.5m\n", " 380 0.2117 23.2% 1.5m\n", " 390 0.1589 23.2% 1.5m\n", " 400 0.1772 23.2% 1.4m\n", " 410 0.1444 23.2% 1.4m\n", " 420 0.1621 23.2% 1.4m\n", " 430 0.1964 23.2% 1.4m\n", " 440 0.1552 23.2% 1.3m\n", " 450 0.1623 23.2% 1.3m\n", " 460 0.2137 23.2% 1.3m\n", " 470 0.1306 23.2% 1.2m\n", " 480 0.1172 23.2% 1.2m\n", " 490 0.2077 23.2% 1.2m\n", " 500 0.1816 23.2% 1.1m\n", " 510 0.1851 23.2% 1.1m\n", " 520 0.1756 23.2% 1.1m\n", " 530 0.1763 23.2% 1.0m\n", " 540 0.1785 23.2% 1.0m\n", " 550 0.1697 23.2% 1.0m\n", " 560 0.1958 23.2% 0.9m\n", " 570 0.1616 23.2% 0.9m\n", " 580 0.1731 23.2% 0.9m\n", " 590 0.1604 23.2% 0.9m\n", " 600 0.1645 23.2% 0.8m\n", " 610 0.1257 23.2% 0.8m\n", " 620 0.1745 23.2% 0.8m\n", " 630 0.1452 23.2% 0.7m\n", " 640 0.1850 23.2% 0.7m\n", " 650 0.1550 23.2% 0.7m\n", " 660 0.1820 23.2% 0.6m\n", " 670 0.1605 23.2% 0.6m\n", " 680 0.1755 23.2% 0.6m\n", " 690 0.1706 23.2% 0.5m\n", " 700 0.1707 23.2% 0.5m\n", " 710 0.1580 23.2% 0.5m\n", " 720 0.1910 23.2% 0.4m\n", " 730 0.1789 23.2% 0.4m\n", " 740 0.1227 23.2% 0.4m\n", " 750 0.1293 23.2% 0.4m\n", " 760 0.1718 23.2% 0.3m\n", " 770 0.1474 23.2% 0.3m\n", " 780 0.1910 23.2% 0.3m\n", " 790 0.1459 23.2% 0.2m\n", " 800 0.1549 23.2% 0.2m\n", " 810 0.1245 23.2% 0.2m\n", " 820 0.1780 23.2% 0.1m\n", " 830 0.1346 23.2% 0.1m\n", " 840 0.1598 23.2% 0.1m\n", " 850 0.2060 23.2% 0.0m\n", " 860 0.1606 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 46 COMPLETE in 161s\n", "\n", "โœ… EPOCH 46 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16686 โ†’ Val=0.16452\n", " ๐Ÿ“Š Acc: EC=81.5% EL=75.4% EJ=86.6% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 153m\n", "\n", "๐Ÿ”„ Epoch 47/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.2202 23.2% 2.7m\n", " 10 0.1547 23.2% 2.6m\n", " 20 0.1801 23.2% 2.6m\n", " 30 0.1675 23.2% 2.6m\n", " 40 0.1539 23.2% 2.6m\n", " 50 0.1607 23.2% 2.5m\n", " 60 0.1615 23.2% 2.5m\n", " 70 0.1521 23.2% 2.5m\n", " 80 0.1629 23.2% 2.4m\n", " 90 0.1203 23.2% 2.4m\n", " 100 0.1775 23.2% 2.4m\n", " 110 0.1737 23.2% 2.4m\n", " 120 0.1402 23.2% 2.3m\n", " 130 0.1739 23.2% 2.3m\n", " 140 0.1603 23.2% 2.3m\n", " 150 0.1635 23.2% 2.2m\n", " 160 0.1732 23.2% 2.2m\n", " 170 0.1769 23.2% 2.2m\n", " 180 0.2079 23.2% 2.1m\n", " 190 0.1484 23.2% 2.1m\n", " 200 0.1449 23.2% 2.1m\n", " 210 0.1240 23.2% 2.0m\n", " 220 0.1493 23.2% 2.0m\n", " 230 0.1935 23.2% 2.0m\n", " 240 0.1219 23.2% 1.9m\n", " 250 0.1600 23.2% 1.9m\n", " 260 0.1370 23.2% 1.9m\n", " 270 0.1520 23.2% 1.9m\n", " 280 0.1519 23.2% 1.8m\n", " 290 0.1418 23.2% 1.8m\n", " 300 0.1661 23.2% 1.8m\n", " 310 0.1564 23.2% 1.7m\n", " 320 0.1862 23.2% 1.7m\n", " 330 0.1609 23.2% 1.7m\n", " 340 0.1363 23.2% 1.6m\n", " 350 0.1944 23.2% 1.6m\n", " 360 0.1450 23.2% 1.6m\n", " 370 0.1617 23.2% 1.5m\n", " 380 0.1685 23.2% 1.5m\n", " 390 0.1714 23.2% 1.5m\n", " 400 0.1449 23.2% 1.4m\n", " 410 0.1197 23.2% 1.4m\n", " 420 0.1826 23.2% 1.4m\n", " 430 0.1786 23.2% 1.4m\n", " 440 0.1759 23.2% 1.3m\n", " 450 0.1760 23.2% 1.3m\n", " 460 0.1633 23.2% 1.3m\n", " 470 0.1868 23.2% 1.2m\n", " 480 0.1829 23.2% 1.2m\n", " 490 0.1623 23.2% 1.2m\n", " 500 0.1463 23.2% 1.1m\n", " 510 0.1669 23.2% 1.1m\n", " 520 0.1405 23.2% 1.1m\n", " 530 0.1412 23.2% 1.0m\n", " 540 0.1316 23.2% 1.0m\n", " 550 0.1455 23.2% 1.0m\n", " 560 0.1340 23.2% 0.9m\n", " 570 0.2020 23.2% 0.9m\n", " 580 0.1514 23.2% 0.9m\n", " 590 0.1919 23.2% 0.9m\n", " 600 0.1545 23.2% 0.8m\n", " 610 0.1876 23.2% 0.8m\n", " 620 0.1940 23.2% 0.8m\n", " 630 0.2004 23.2% 0.7m\n", " 640 0.1513 23.2% 0.7m\n", " 650 0.1385 23.2% 0.7m\n", " 660 0.1868 23.2% 0.6m\n", " 670 0.1478 23.2% 0.6m\n", " 680 0.1630 23.2% 0.6m\n", " 690 0.1344 23.2% 0.5m\n", " 700 0.2115 23.2% 0.5m\n", " 710 0.2033 23.2% 0.5m\n", " 720 0.1842 23.2% 0.4m\n", " 730 0.1576 23.2% 0.4m\n", " 740 0.1438 23.2% 0.4m\n", " 750 0.1679 23.2% 0.4m\n", " 760 0.1788 23.2% 0.3m\n", " 770 0.1432 23.2% 0.3m\n", " 780 0.1775 23.2% 0.3m\n", " 790 0.1677 23.2% 0.2m\n", " 800 0.1689 23.2% 0.2m\n", " 810 0.1838 23.2% 0.2m\n", " 820 0.1628 23.2% 0.1m\n", " 830 0.1529 23.2% 0.1m\n", " 840 0.1619 23.2% 0.1m\n", " 850 0.1927 23.2% 0.0m\n", " 860 0.1799 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 47 COMPLETE in 162s\n", "\n", "โœ… EPOCH 47 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16675 โ†’ Val=0.16421\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.2%\n", " โš ๏ธ No improvement for 20/30\n", " โฑ๏ธ Remaining ETA โ‰ˆ 150m\n", "\n", "๐Ÿ”„ Epoch 48/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1634 23.2% 2.7m\n", " 10 0.1571 23.2% 2.6m\n", " 20 0.1670 23.2% 2.7m\n", " 30 0.1434 23.2% 2.6m\n", " 40 0.2027 23.2% 2.6m\n", " 50 0.1908 23.2% 2.6m\n", " 60 0.1969 23.2% 2.5m\n", " 70 0.1647 23.2% 2.5m\n", " 80 0.1632 23.2% 2.4m\n", " 90 0.1777 23.2% 2.4m\n", " 100 0.1550 23.2% 2.4m\n", " 110 0.1801 23.2% 2.4m\n", " 120 0.1679 23.2% 2.3m\n", " 130 0.1423 23.2% 2.3m\n", " 140 0.1975 23.2% 2.3m\n", " 150 0.1602 23.2% 2.2m\n", " 160 0.1775 23.2% 2.2m\n", " 170 0.1636 23.2% 2.2m\n", " 180 0.1446 23.2% 2.1m\n", " 190 0.1761 23.2% 2.1m\n", " 200 0.1385 23.2% 2.1m\n", " 210 0.1750 23.2% 2.1m\n", " 220 0.1688 23.2% 2.0m\n", " 230 0.1621 23.2% 2.0m\n", " 240 0.1681 23.2% 2.0m\n", " 250 0.1692 23.2% 1.9m\n", " 260 0.1554 23.2% 1.9m\n", " 270 0.1893 23.2% 1.9m\n", " 280 0.1411 23.2% 1.8m\n", " 290 0.1074 23.2% 1.8m\n", " 300 0.1476 23.2% 1.8m\n", " 310 0.1729 23.2% 1.7m\n", " 320 0.1886 23.2% 1.7m\n", " 330 0.1970 23.2% 1.7m\n", " 340 0.2117 23.2% 1.6m\n", " 350 0.1606 23.2% 1.6m\n", " 360 0.1861 23.2% 1.6m\n", " 370 0.1816 23.2% 1.5m\n", " 380 0.1874 23.2% 1.5m\n", " 390 0.1559 23.2% 1.5m\n", " 400 0.1818 23.2% 1.5m\n", " 410 0.1638 23.2% 1.4m\n", " 420 0.2009 23.2% 1.4m\n", " 430 0.1671 23.2% 1.4m\n", " 440 0.1635 23.2% 1.3m\n", " 450 0.1647 23.2% 1.3m\n", " 460 0.1953 23.2% 1.3m\n", " 470 0.1452 23.2% 1.2m\n", " 480 0.1560 23.2% 1.2m\n", " 490 0.2376 23.2% 1.2m\n", " 500 0.1871 23.2% 1.1m\n", " 510 0.1445 23.2% 1.1m\n", " 520 0.1689 23.2% 1.1m\n", " 530 0.1560 23.2% 1.0m\n", " 540 0.1567 23.2% 1.0m\n", " 550 0.1644 23.2% 1.0m\n", " 560 0.1631 23.2% 0.9m\n", " 570 0.1907 23.2% 0.9m\n", " 580 0.1410 23.2% 0.9m\n", " 590 0.1531 23.2% 0.9m\n", " 600 0.1546 23.2% 0.8m\n", " 610 0.1652 23.2% 0.8m\n", " 620 0.1429 23.2% 0.8m\n", " 630 0.1623 23.2% 0.7m\n", " 640 0.1951 23.2% 0.7m\n", " 650 0.1813 23.2% 0.7m\n", " 660 0.1718 23.2% 0.6m\n", " 670 0.1677 23.2% 0.6m\n", " 680 0.1375 23.2% 0.6m\n", " 690 0.1263 23.2% 0.5m\n", " 700 0.1154 23.2% 0.5m\n", " 710 0.1531 23.2% 0.5m\n", " 720 0.1589 23.2% 0.4m\n", " 730 0.1562 23.2% 0.4m\n", " 740 0.1796 23.2% 0.4m\n", " 750 0.1866 23.2% 0.4m\n", " 760 0.1987 23.2% 0.3m\n", " 770 0.1832 23.2% 0.3m\n", " 780 0.1344 23.2% 0.3m\n", " 790 0.1486 23.2% 0.2m\n", " 800 0.1531 23.2% 0.2m\n", " 810 0.1411 23.2% 0.2m\n", " 820 0.1398 23.2% 0.1m\n", " 830 0.1718 23.2% 0.1m\n", " 840 0.1515 23.2% 0.1m\n", " 850 0.1652 23.2% 0.0m\n", " 860 0.1706 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 48 COMPLETE in 162s\n", "\n", "โœ… EPOCH 48 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16666 โ†’ Val=0.16419\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 148m\n", "\n", "๐Ÿ”„ Epoch 49/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1861 23.2% 2.7m\n", " 10 0.1468 23.2% 2.7m\n", " 20 0.1236 23.2% 2.6m\n", " 30 0.1559 23.2% 2.6m\n", " 40 0.1083 23.2% 2.6m\n", " 50 0.1703 23.2% 2.5m\n", " 60 0.1804 23.2% 2.5m\n", " 70 0.1582 23.2% 2.5m\n", " 80 0.1642 23.2% 2.5m\n", " 90 0.1702 23.2% 2.4m\n", " 100 0.1616 23.2% 2.4m\n", " 110 0.1767 23.2% 2.4m\n", " 120 0.1902 23.2% 2.3m\n", " 130 0.1989 23.2% 2.3m\n", " 140 0.1462 23.2% 2.3m\n", " 150 0.1462 23.2% 2.2m\n", " 160 0.1634 23.2% 2.2m\n", " 170 0.2023 23.2% 2.2m\n", " 180 0.1885 23.2% 2.1m\n", " 190 0.1514 23.2% 2.1m\n", " 200 0.1293 23.2% 2.1m\n", " 210 0.1416 23.2% 2.0m\n", " 220 0.1952 23.2% 2.0m\n", " 230 0.1928 23.2% 2.0m\n", " 240 0.1591 23.2% 2.0m\n", " 250 0.1599 23.2% 1.9m\n", " 260 0.1454 23.2% 1.9m\n", " 270 0.1467 23.2% 1.9m\n", " 280 0.1435 23.2% 1.8m\n", " 290 0.1627 23.2% 1.8m\n", " 300 0.1430 23.2% 1.8m\n", " 310 0.1415 23.2% 1.7m\n", " 320 0.1667 23.2% 1.7m\n", " 330 0.1453 23.2% 1.7m\n", " 340 0.1569 23.2% 1.6m\n", " 350 0.1906 23.2% 1.6m\n", " 360 0.1690 23.2% 1.6m\n", " 370 0.1528 23.2% 1.5m\n", " 380 0.1802 23.2% 1.5m\n", " 390 0.1824 23.2% 1.5m\n", " 400 0.1525 23.2% 1.4m\n", " 410 0.1702 23.2% 1.4m\n", " 420 0.1829 23.2% 1.4m\n", " 430 0.1327 23.2% 1.4m\n", " 440 0.1801 23.2% 1.3m\n", " 450 0.1872 23.2% 1.3m\n", " 460 0.1571 23.2% 1.3m\n", " 470 0.1486 23.2% 1.2m\n", " 480 0.1623 23.2% 1.2m\n", " 490 0.1815 23.2% 1.2m\n", " 500 0.1687 23.2% 1.1m\n", " 510 0.1588 23.2% 1.1m\n", " 520 0.1871 23.2% 1.1m\n", " 530 0.1844 23.2% 1.0m\n", " 540 0.1747 23.2% 1.0m\n", " 550 0.1905 23.2% 1.0m\n", " 560 0.1745 23.2% 0.9m\n", " 570 0.1401 23.2% 0.9m\n", " 580 0.1465 23.2% 0.9m\n", " 590 0.1847 23.2% 0.9m\n", " 600 0.1389 23.2% 0.8m\n", " 610 0.1636 23.2% 0.8m\n", " 620 0.1771 23.2% 0.8m\n", " 630 0.1481 23.2% 0.7m\n", " 640 0.1688 23.2% 0.7m\n", " 650 0.1686 23.2% 0.7m\n", " 660 0.1502 23.2% 0.6m\n", " 670 0.1550 23.2% 0.6m\n", " 680 0.1877 23.2% 0.6m\n", " 690 0.1984 23.2% 0.5m\n", " 700 0.1707 23.2% 0.5m\n", " 710 0.1337 23.2% 0.5m\n", " 720 0.1886 23.2% 0.4m\n", " 730 0.1658 23.2% 0.4m\n", " 740 0.2102 23.2% 0.4m\n", " 750 0.1419 23.2% 0.4m\n", " 760 0.1489 23.2% 0.3m\n", " 770 0.1627 23.2% 0.3m\n", " 780 0.1764 23.2% 0.3m\n", " 790 0.1511 23.2% 0.2m\n", " 800 0.1628 23.2% 0.2m\n", " 810 0.1241 23.2% 0.2m\n", " 820 0.1659 23.2% 0.1m\n", " 830 0.1711 23.2% 0.1m\n", " 840 0.1491 23.2% 0.1m\n", " 850 0.1910 23.2% 0.0m\n", " 860 0.1544 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 49 COMPLETE in 162s\n", "\n", "โœ… EPOCH 49 SUMMARY\n", " โฑ๏ธ Time: 171s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16682 โ†’ Val=0.16438\n", " ๐Ÿ“Š Acc: EC=81.5% EL=75.4% EJ=86.6% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 145m\n", "\n", "๐Ÿ”„ Epoch 50/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.2047 23.2% 2.7m\n", " 10 0.1427 23.2% 2.7m\n", " 20 0.1596 23.2% 2.6m\n", " 30 0.1963 23.2% 2.6m\n", " 40 0.1631 23.2% 2.6m\n", " 50 0.1339 23.2% 2.6m\n", " 60 0.1807 23.2% 2.5m\n", " 70 0.1812 23.2% 2.5m\n", " 80 0.1530 23.2% 2.5m\n", " 90 0.1871 23.2% 2.4m\n", " 100 0.1634 23.2% 2.4m\n", " 110 0.1790 23.2% 2.4m\n", " 120 0.1951 23.2% 2.3m\n", " 130 0.1597 23.2% 2.3m\n", " 140 0.1427 23.2% 2.3m\n", " 150 0.1901 23.2% 2.2m\n", " 160 0.1495 23.2% 2.2m\n", " 170 0.1620 23.2% 2.2m\n", " 180 0.1138 23.2% 2.1m\n", " 190 0.1536 23.2% 2.1m\n", " 200 0.1620 23.2% 2.1m\n", " 210 0.1586 23.2% 2.0m\n", " 220 0.1299 23.2% 2.0m\n", " 230 0.1323 23.2% 2.0m\n", " 240 0.1313 23.2% 1.9m\n", " 250 0.1487 23.2% 1.9m\n", " 260 0.1742 23.2% 1.9m\n", " 270 0.1631 23.2% 1.9m\n", " 280 0.1343 23.2% 1.8m\n", " 290 0.1469 23.2% 1.8m\n", " 300 0.1522 23.2% 1.8m\n", " 310 0.2166 23.2% 1.7m\n", " 320 0.1569 23.2% 1.7m\n", " 330 0.1646 23.2% 1.7m\n", " 340 0.1628 23.2% 1.6m\n", " 350 0.1984 23.2% 1.6m\n", " 360 0.1791 23.2% 1.6m\n", " 370 0.1797 23.2% 1.5m\n", " 380 0.1665 23.2% 1.5m\n", " 390 0.1837 23.2% 1.5m\n", " 400 0.1593 23.2% 1.5m\n", " 410 0.1468 23.2% 1.4m\n", " 420 0.1657 23.2% 1.4m\n", " 430 0.1769 23.2% 1.4m\n", " 440 0.1540 23.2% 1.3m\n", " 450 0.1928 23.2% 1.3m\n", " 460 0.2016 23.2% 1.3m\n", " 470 0.1476 23.2% 1.2m\n", " 480 0.2153 23.2% 1.2m\n", " 490 0.1352 23.2% 1.2m\n", " 500 0.1430 23.2% 1.1m\n", " 510 0.1935 23.2% 1.1m\n", " 520 0.1696 23.2% 1.1m\n", " 530 0.1895 23.2% 1.0m\n", " 540 0.1581 23.2% 1.0m\n", " 550 0.1690 23.2% 1.0m\n", " 560 0.1682 23.2% 0.9m\n", " 570 0.1682 23.2% 0.9m\n", " 580 0.2040 23.2% 0.9m\n", " 590 0.1432 23.2% 0.9m\n", " 600 0.1931 23.2% 0.8m\n", " 610 0.1790 23.2% 0.8m\n", " 620 0.1591 23.2% 0.8m\n", " 630 0.1362 23.2% 0.7m\n", " 640 0.1661 23.2% 0.7m\n", " 650 0.1650 23.2% 0.7m\n", " 660 0.2035 23.2% 0.6m\n", " 670 0.1470 23.2% 0.6m\n", " 680 0.1682 23.2% 0.6m\n", " 690 0.1458 23.2% 0.5m\n", " 700 0.1498 23.2% 0.5m\n", " 710 0.1864 23.2% 0.5m\n", " 720 0.1814 23.2% 0.4m\n", " 730 0.1419 23.2% 0.4m\n", " 740 0.1714 23.2% 0.4m\n", " 750 0.1582 23.2% 0.4m\n", " 760 0.1762 23.2% 0.3m\n", " 770 0.2078 23.2% 0.3m\n", " 780 0.1521 23.2% 0.3m\n", " 790 0.1450 23.2% 0.2m\n", " 800 0.2110 23.2% 0.2m\n", " 810 0.2000 23.2% 0.2m\n", " 820 0.1935 23.2% 0.1m\n", " 830 0.1777 23.2% 0.1m\n", " 840 0.1913 23.2% 0.1m\n", " 850 0.1640 23.2% 0.0m\n", " 860 0.1429 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 50 COMPLETE in 162s\n", "\n", "โœ… EPOCH 50 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16672 โ†’ Val=0.16416\n", " ๐Ÿ“Š Acc: EC=81.5% EL=75.4% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 142m\n", "\n", "๐Ÿ”„ Epoch 51/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1297 23.2% 2.6m\n", " 10 0.1714 23.2% 2.7m\n", " 20 0.1958 23.2% 2.6m\n", " 30 0.1414 23.2% 2.6m\n", " 40 0.1519 23.2% 2.6m\n", " 50 0.1641 23.2% 2.5m\n", " 60 0.1623 23.2% 2.5m\n", " 70 0.1468 23.2% 2.5m\n", " 80 0.1464 23.2% 2.4m\n", " 90 0.1449 23.2% 2.4m\n", " 100 0.1751 23.2% 2.4m\n", " 110 0.1535 23.2% 2.3m\n", " 120 0.1571 23.2% 2.3m\n", " 130 0.1724 23.2% 2.3m\n", " 140 0.1360 23.2% 2.3m\n", " 150 0.1524 23.2% 2.2m\n", " 160 0.1787 23.2% 2.2m\n", " 170 0.1673 23.2% 2.2m\n", " 180 0.1592 23.2% 2.1m\n", " 190 0.1441 23.2% 2.1m\n", " 200 0.1499 23.2% 2.1m\n", " 210 0.1558 23.2% 2.0m\n", " 220 0.1473 23.2% 2.0m\n", " 230 0.1530 23.2% 2.0m\n", " 240 0.1437 23.2% 1.9m\n", " 250 0.1640 23.2% 1.9m\n", " 260 0.1914 23.2% 1.9m\n", " 270 0.1544 23.2% 1.8m\n", " 280 0.1702 23.2% 1.8m\n", " 290 0.1504 23.2% 1.8m\n", " 300 0.1822 23.2% 1.8m\n", " 310 0.1517 23.2% 1.7m\n", " 320 0.1450 23.2% 1.7m\n", " 330 0.1617 23.2% 1.7m\n", " 340 0.1290 23.2% 1.6m\n", " 350 0.1621 23.2% 1.6m\n", " 360 0.1640 23.2% 1.6m\n", " 370 0.1745 23.2% 1.5m\n", " 380 0.1913 23.2% 1.5m\n", " 390 0.1354 23.2% 1.5m\n", " 400 0.1680 23.2% 1.4m\n", " 410 0.1542 23.2% 1.4m\n", " 420 0.1723 23.2% 1.4m\n", " 430 0.1661 23.2% 1.3m\n", " 440 0.1842 23.2% 1.3m\n", " 450 0.1703 23.2% 1.3m\n", " 460 0.1656 23.2% 1.3m\n", " 470 0.1792 23.2% 1.2m\n", " 480 0.1587 23.2% 1.2m\n", " 490 0.1573 23.2% 1.2m\n", " 500 0.1672 23.2% 1.1m\n", " 510 0.1569 23.2% 1.1m\n", " 520 0.1626 23.2% 1.1m\n", " 530 0.1575 23.2% 1.0m\n", " 540 0.1903 23.2% 1.0m\n", " 550 0.1931 23.2% 1.0m\n", " 560 0.1650 23.2% 0.9m\n", " 570 0.1592 23.2% 0.9m\n", " 580 0.1984 23.2% 0.9m\n", " 590 0.1628 23.2% 0.9m\n", " 600 0.1759 23.2% 0.8m\n", " 610 0.1745 23.2% 0.8m\n", " 620 0.1180 23.2% 0.8m\n", " 630 0.1597 23.2% 0.7m\n", " 640 0.1698 23.2% 0.7m\n", " 650 0.1386 23.2% 0.7m\n", " 660 0.1621 23.2% 0.6m\n", " 670 0.1892 23.2% 0.6m\n", " 680 0.1608 23.2% 0.6m\n", " 690 0.1682 23.2% 0.5m\n", " 700 0.1622 23.2% 0.5m\n", " 710 0.1528 23.2% 0.5m\n", " 720 0.1938 23.2% 0.4m\n", " 730 0.1805 23.2% 0.4m\n", " 740 0.1533 23.2% 0.4m\n", " 750 0.1667 23.2% 0.4m\n", " 760 0.1637 23.2% 0.3m\n", " 770 0.1839 23.2% 0.3m\n", " 780 0.1809 23.2% 0.3m\n", " 790 0.2384 23.2% 0.2m\n", " 800 0.1690 23.2% 0.2m\n", " 810 0.1483 23.2% 0.2m\n", " 820 0.1959 23.2% 0.1m\n", " 830 0.1913 23.2% 0.1m\n", " 840 0.1891 23.2% 0.1m\n", " 850 0.1525 23.2% 0.0m\n", " 860 0.1840 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 51 COMPLETE in 161s\n", "\n", "โœ… EPOCH 51 SUMMARY\n", " โฑ๏ธ Time: 169s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16685 โ†’ Val=0.16413\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 138m\n", "\n", "๐Ÿ”„ Epoch 52/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1527 23.2% 2.6m\n", " 10 0.1858 23.2% 2.7m\n", " 20 0.1898 23.2% 2.6m\n", " 30 0.1805 23.2% 2.6m\n", " 40 0.1713 23.2% 2.6m\n", " 50 0.1488 23.2% 2.6m\n", " 60 0.1192 23.2% 2.5m\n", " 70 0.1517 23.2% 2.5m\n", " 80 0.1878 23.2% 2.5m\n", " 90 0.1432 23.2% 2.4m\n", " 100 0.1843 23.2% 2.4m\n", " 110 0.1939 23.2% 2.4m\n", " 120 0.1668 23.2% 2.3m\n", " 130 0.1816 23.2% 2.3m\n", " 140 0.1563 23.2% 2.3m\n", " 150 0.1904 23.2% 2.2m\n", " 160 0.1836 23.2% 2.2m\n", " 170 0.1410 23.2% 2.2m\n", " 180 0.1601 23.2% 2.1m\n", " 190 0.1784 23.2% 2.1m\n", " 200 0.1455 23.2% 2.1m\n", " 210 0.1606 23.2% 2.0m\n", " 220 0.2166 23.2% 2.0m\n", " 230 0.1754 23.2% 2.0m\n", " 240 0.1695 23.2% 1.9m\n", " 250 0.1649 23.2% 1.9m\n", " 260 0.1702 23.2% 1.9m\n", " 270 0.1893 23.2% 1.8m\n", " 280 0.1658 23.2% 1.8m\n", " 290 0.1874 23.2% 1.8m\n", " 300 0.1650 23.2% 1.8m\n", " 310 0.1779 23.2% 1.7m\n", " 320 0.1574 23.2% 1.7m\n", " 330 0.1416 23.2% 1.7m\n", " 340 0.1286 23.2% 1.6m\n", " 350 0.1657 23.2% 1.6m\n", " 360 0.1667 23.2% 1.6m\n", " 370 0.2033 23.2% 1.5m\n", " 380 0.1563 23.2% 1.5m\n", " 390 0.1806 23.2% 1.5m\n", " 400 0.1765 23.2% 1.4m\n", " 410 0.1835 23.2% 1.4m\n", " 420 0.2048 23.2% 1.4m\n", " 430 0.1751 23.2% 1.3m\n", " 440 0.1682 23.2% 1.3m\n", " 450 0.1684 23.2% 1.3m\n", " 460 0.1652 23.2% 1.3m\n", " 470 0.1405 23.2% 1.2m\n", " 480 0.1713 23.2% 1.2m\n", " 490 0.1712 23.2% 1.2m\n", " 500 0.1639 23.2% 1.1m\n", " 510 0.1725 23.2% 1.1m\n", " 520 0.1519 23.2% 1.1m\n", " 530 0.1722 23.2% 1.0m\n", " 540 0.1640 23.2% 1.0m\n", " 550 0.1417 23.2% 1.0m\n", " 560 0.1354 23.2% 0.9m\n", " 570 0.1944 23.2% 0.9m\n", " 580 0.1330 23.2% 0.9m\n", " 590 0.1550 23.2% 0.9m\n", " 600 0.1490 23.2% 0.8m\n", " 610 0.1791 23.2% 0.8m\n", " 620 0.2019 23.2% 0.8m\n", " 630 0.1855 23.2% 0.7m\n", " 640 0.1681 23.2% 0.7m\n", " 650 0.1829 23.2% 0.7m\n", " 660 0.1529 23.2% 0.6m\n", " 670 0.1969 23.2% 0.6m\n", " 680 0.1709 23.2% 0.6m\n", " 690 0.1610 23.2% 0.5m\n", " 700 0.1682 23.2% 0.5m\n", " 710 0.1413 23.2% 0.5m\n", " 720 0.1596 23.2% 0.4m\n", " 730 0.1605 23.2% 0.4m\n", " 740 0.1875 23.2% 0.4m\n", " 750 0.1878 23.2% 0.4m\n", " 760 0.1688 23.2% 0.3m\n", " 770 0.1775 23.2% 0.3m\n", " 780 0.1707 23.2% 0.3m\n", " 790 0.1602 23.2% 0.2m\n", " 800 0.1627 23.2% 0.2m\n", " 810 0.1934 23.2% 0.2m\n", " 820 0.1438 23.2% 0.1m\n", " 830 0.1469 23.2% 0.1m\n", " 840 0.1433 23.2% 0.1m\n", " 850 0.1620 23.2% 0.0m\n", " 860 0.1743 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 52 COMPLETE in 162s\n", "\n", "โœ… EPOCH 52 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16664 โ†’ Val=0.16436\n", " ๐Ÿ“Š Acc: EC=81.5% EL=75.4% EJ=86.6% Overall=81.2%\n", " โš ๏ธ No improvement for 25/30\n", " โฑ๏ธ Remaining ETA โ‰ˆ 136m\n", "\n", "๐Ÿ”„ Epoch 53/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1303 23.2% 2.7m\n", " 10 0.1677 23.2% 2.7m\n", " 20 0.1742 23.2% 2.6m\n", " 30 0.1611 23.2% 2.6m\n", " 40 0.1661 23.2% 2.6m\n", " 50 0.2062 23.2% 2.5m\n", " 60 0.2194 23.2% 2.5m\n", " 70 0.1598 23.2% 2.5m\n", " 80 0.1682 23.2% 2.4m\n", " 90 0.1472 23.2% 2.4m\n", " 100 0.2113 23.2% 2.4m\n", " 110 0.1748 23.2% 2.4m\n", " 120 0.1660 23.2% 2.3m\n", " 130 0.2183 23.2% 2.3m\n", " 140 0.1896 23.2% 2.3m\n", " 150 0.1670 23.2% 2.2m\n", " 160 0.1531 23.2% 2.2m\n", " 170 0.1589 23.2% 2.2m\n", " 180 0.1795 23.2% 2.1m\n", " 190 0.2099 23.2% 2.1m\n", " 200 0.1666 23.2% 2.1m\n", " 210 0.1633 23.2% 2.0m\n", " 220 0.1712 23.2% 2.0m\n", " 230 0.1644 23.2% 2.0m\n", " 240 0.1740 23.2% 1.9m\n", " 250 0.1842 23.2% 1.9m\n", " 260 0.1630 23.2% 1.9m\n", " 270 0.1846 23.2% 1.8m\n", " 280 0.1099 23.2% 1.8m\n", " 290 0.1720 23.2% 1.8m\n", " 300 0.2259 23.2% 1.8m\n", " 310 0.1749 23.2% 1.7m\n", " 320 0.1515 23.2% 1.7m\n", " 330 0.1602 23.2% 1.7m\n", " 340 0.1712 23.2% 1.6m\n", " 350 0.1503 23.2% 1.6m\n", " 360 0.1533 23.2% 1.6m\n", " 370 0.1603 23.2% 1.5m\n", " 380 0.1647 23.2% 1.5m\n", " 390 0.2030 23.2% 1.5m\n", " 400 0.1770 23.2% 1.4m\n", " 410 0.1768 23.2% 1.4m\n", " 420 0.1852 23.2% 1.4m\n", " 430 0.1801 23.2% 1.4m\n", " 440 0.1701 23.2% 1.3m\n", " 450 0.1601 23.2% 1.3m\n", " 460 0.1723 23.2% 1.3m\n", " 470 0.1586 23.2% 1.2m\n", " 480 0.1767 23.2% 1.2m\n", " 490 0.1611 23.2% 1.2m\n", " 500 0.1977 23.2% 1.1m\n", " 510 0.1652 23.2% 1.1m\n", " 520 0.1770 23.2% 1.1m\n", " 530 0.1768 23.2% 1.0m\n", " 540 0.2250 23.2% 1.0m\n", " 550 0.2119 23.2% 1.0m\n", " 560 0.1602 23.2% 0.9m\n", " 570 0.1453 23.2% 0.9m\n", " 580 0.1382 23.2% 0.9m\n", " 590 0.1556 23.2% 0.9m\n", " 600 0.1603 23.2% 0.8m\n", " 610 0.1626 23.2% 0.8m\n", " 620 0.2032 23.2% 0.8m\n", " 630 0.1620 23.2% 0.7m\n", " 640 0.1650 23.2% 0.7m\n", " 650 0.1685 23.2% 0.7m\n", " 660 0.1349 23.2% 0.6m\n", " 670 0.1703 23.2% 0.6m\n", " 680 0.1839 23.2% 0.6m\n", " 690 0.1718 23.2% 0.5m\n", " 700 0.1804 23.2% 0.5m\n", " 710 0.1293 23.2% 0.5m\n", " 720 0.1738 23.2% 0.4m\n", " 730 0.1369 23.2% 0.4m\n", " 740 0.2137 23.2% 0.4m\n", " 750 0.1783 23.2% 0.4m\n", " 760 0.1971 23.2% 0.3m\n", " 770 0.1896 23.2% 0.3m\n", " 780 0.1789 23.2% 0.3m\n", " 790 0.1502 23.2% 0.2m\n", " 800 0.2096 23.2% 0.2m\n", " 810 0.1542 23.2% 0.2m\n", " 820 0.1560 23.2% 0.1m\n", " 830 0.1510 23.2% 0.1m\n", " 840 0.1567 23.2% 0.1m\n", " 850 0.1493 23.2% 0.0m\n", " 860 0.1303 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 53 COMPLETE in 162s\n", "\n", "โœ… EPOCH 53 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16673 โ†’ Val=0.16406\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 133m\n", "\n", "๐Ÿ”„ Epoch 54/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1719 23.2% 2.9m\n", " 10 0.1752 23.2% 2.6m\n", " 20 0.1501 23.2% 2.6m\n", " 30 0.2110 23.2% 2.6m\n", " 40 0.1647 23.2% 2.6m\n", " 50 0.2085 23.2% 2.6m\n", " 60 0.1486 23.2% 2.5m\n", " 70 0.1738 23.2% 2.5m\n", " 80 0.1782 23.2% 2.5m\n", " 90 0.1509 23.2% 2.4m\n", " 100 0.1870 23.2% 2.4m\n", " 110 0.1678 23.2% 2.4m\n", " 120 0.1323 23.2% 2.3m\n", " 130 0.1745 23.2% 2.3m\n", " 140 0.1707 23.2% 2.3m\n", " 150 0.1687 23.2% 2.2m\n", " 160 0.1347 23.2% 2.2m\n", " 170 0.1338 23.2% 2.2m\n", " 180 0.1478 23.2% 2.1m\n", " 190 0.2004 23.2% 2.1m\n", " 200 0.1585 23.2% 2.1m\n", " 210 0.1715 23.2% 2.1m\n", " 220 0.1808 23.2% 2.0m\n", " 230 0.1867 23.2% 2.0m\n", " 240 0.1719 23.2% 2.0m\n", " 250 0.1444 23.2% 1.9m\n", " 260 0.1935 23.2% 1.9m\n", " 270 0.1608 23.2% 1.9m\n", " 280 0.1435 23.2% 1.8m\n", " 290 0.1765 23.2% 1.8m\n", " 300 0.1872 23.2% 1.8m\n", " 310 0.1704 23.2% 1.7m\n", " 320 0.1210 23.2% 1.7m\n", " 330 0.1440 23.2% 1.7m\n", " 340 0.1608 23.2% 1.6m\n", " 350 0.1664 23.2% 1.6m\n", " 360 0.1742 23.2% 1.6m\n", " 370 0.1998 23.2% 1.5m\n", " 380 0.1581 23.2% 1.5m\n", " 390 0.1607 23.2% 1.5m\n", " 400 0.1477 23.2% 1.5m\n", " 410 0.1571 23.2% 1.4m\n", " 420 0.1718 23.2% 1.4m\n", " 430 0.1408 23.2% 1.4m\n", " 440 0.1727 23.2% 1.3m\n", " 450 0.1624 23.2% 1.3m\n", " 460 0.1555 23.2% 1.3m\n", " 470 0.1431 23.2% 1.2m\n", " 480 0.1598 23.2% 1.2m\n", " 490 0.1527 23.2% 1.2m\n", " 500 0.1508 23.2% 1.1m\n", " 510 0.1562 23.2% 1.1m\n", " 520 0.1538 23.2% 1.1m\n", " 530 0.1634 23.2% 1.0m\n", " 540 0.1420 23.2% 1.0m\n", " 550 0.1594 23.2% 1.0m\n", " 560 0.1925 23.2% 0.9m\n", " 570 0.2026 23.2% 0.9m\n", " 580 0.1784 23.2% 0.9m\n", " 590 0.1453 23.2% 0.9m\n", " 600 0.1720 23.2% 0.8m\n", " 610 0.1929 23.2% 0.8m\n", " 620 0.1485 23.2% 0.8m\n", " 630 0.1621 23.2% 0.7m\n", " 640 0.2041 23.2% 0.7m\n", " 650 0.1379 23.2% 0.7m\n", " 660 0.1739 23.2% 0.6m\n", " 670 0.1457 23.2% 0.6m\n", " 680 0.1509 23.2% 0.6m\n", " 690 0.1701 23.2% 0.5m\n", " 700 0.1735 23.2% 0.5m\n", " 710 0.2307 23.2% 0.5m\n", " 720 0.2016 23.2% 0.4m\n", " 730 0.1782 23.2% 0.4m\n", " 740 0.1693 23.2% 0.4m\n", " 750 0.1714 23.2% 0.4m\n", " 760 0.1430 23.2% 0.3m\n", " 770 0.1595 23.2% 0.3m\n", " 780 0.1286 23.2% 0.3m\n", " 790 0.1728 23.2% 0.2m\n", " 800 0.2263 23.2% 0.2m\n", " 810 0.1371 23.2% 0.2m\n", " 820 0.1764 23.2% 0.1m\n", " 830 0.1953 23.2% 0.1m\n", " 840 0.1409 23.2% 0.1m\n", " 850 0.2235 23.2% 0.0m\n", " 860 0.1004 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 54 COMPLETE in 162s\n", "\n", "โœ… EPOCH 54 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16667 โ†’ Val=0.16404\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 130m\n", "\n", "๐Ÿ”„ Epoch 55/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1304 23.2% 2.7m\n", " 10 0.1609 23.2% 2.6m\n", " 20 0.1322 23.2% 2.6m\n", " 30 0.1739 23.2% 2.6m\n", " 40 0.1760 23.2% 2.5m\n", " 50 0.1643 23.2% 2.5m\n", " 60 0.1547 23.2% 2.5m\n", " 70 0.1660 23.2% 2.5m\n", " 80 0.1478 23.2% 2.4m\n", " 90 0.1548 23.2% 2.4m\n", " 100 0.1784 23.2% 2.4m\n", " 110 0.1468 23.2% 2.3m\n", " 120 0.1750 23.2% 2.3m\n", " 130 0.1953 23.2% 2.3m\n", " 140 0.1599 23.2% 2.3m\n", " 150 0.1710 23.2% 2.2m\n", " 160 0.1930 23.2% 2.2m\n", " 170 0.1710 23.2% 2.2m\n", " 180 0.1596 23.2% 2.1m\n", " 190 0.1511 23.2% 2.1m\n", " 200 0.1706 23.2% 2.1m\n", " 210 0.1497 23.2% 2.0m\n", " 220 0.1528 23.2% 2.0m\n", " 230 0.1534 23.2% 2.0m\n", " 240 0.1067 23.2% 1.9m\n", " 250 0.1519 23.2% 1.9m\n", " 260 0.1621 23.2% 1.9m\n", " 270 0.1283 23.2% 1.9m\n", " 280 0.1603 23.2% 1.8m\n", " 290 0.1499 23.2% 1.8m\n", " 300 0.1334 23.2% 1.8m\n", " 310 0.1628 23.2% 1.7m\n", " 320 0.1822 23.2% 1.7m\n", " 330 0.1663 23.2% 1.7m\n", " 340 0.1652 23.2% 1.6m\n", " 350 0.1572 23.2% 1.6m\n", " 360 0.1732 23.2% 1.6m\n", " 370 0.1553 23.2% 1.5m\n", " 380 0.1779 23.2% 1.5m\n", " 390 0.1871 23.2% 1.5m\n", " 400 0.1575 23.2% 1.4m\n", " 410 0.1504 23.2% 1.4m\n", " 420 0.1538 23.2% 1.4m\n", " 430 0.1132 23.2% 1.4m\n", " 440 0.1411 23.2% 1.3m\n", " 450 0.1717 23.2% 1.3m\n", " 460 0.1422 23.2% 1.3m\n", " 470 0.1306 23.2% 1.2m\n", " 480 0.1772 23.2% 1.2m\n", " 490 0.1530 23.2% 1.2m\n", " 500 0.1755 23.2% 1.1m\n", " 510 0.1355 23.2% 1.1m\n", " 520 0.1379 23.2% 1.1m\n", " 530 0.1653 23.2% 1.0m\n", " 540 0.1736 23.2% 1.0m\n", " 550 0.1647 23.2% 1.0m\n", " 560 0.1871 23.2% 0.9m\n", " 570 0.1968 23.2% 0.9m\n", " 580 0.1855 23.2% 0.9m\n", " 590 0.1695 23.2% 0.9m\n", " 600 0.1735 23.2% 0.8m\n", " 610 0.1587 23.2% 0.8m\n", " 620 0.1804 23.2% 0.8m\n", " 630 0.1303 23.2% 0.7m\n", " 640 0.1726 23.2% 0.7m\n", " 650 0.1439 23.2% 0.7m\n", " 660 0.1679 23.2% 0.6m\n", " 670 0.1394 23.2% 0.6m\n", " 680 0.1735 23.2% 0.6m\n", " 690 0.1739 23.2% 0.5m\n", " 700 0.1590 23.2% 0.5m\n", " 710 0.1813 23.2% 0.5m\n", " 720 0.1617 23.2% 0.4m\n", " 730 0.1580 23.2% 0.4m\n", " 740 0.1621 23.2% 0.4m\n", " 750 0.1526 23.2% 0.4m\n", " 760 0.1675 23.2% 0.3m\n", " 770 0.1462 23.2% 0.3m\n", " 780 0.1977 23.2% 0.3m\n", " 790 0.1657 23.2% 0.2m\n", " 800 0.1711 23.2% 0.2m\n", " 810 0.1860 23.2% 0.2m\n", " 820 0.1413 23.2% 0.1m\n", " 830 0.1772 23.2% 0.1m\n", " 840 0.1412 23.2% 0.1m\n", " 850 0.1755 23.2% 0.0m\n", " 860 0.1405 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 55 COMPLETE in 161s\n", "\n", "โœ… EPOCH 55 SUMMARY\n", " โฑ๏ธ Time: 169s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16669 โ†’ Val=0.16407\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 127m\n", "\n", "๐Ÿ”„ Epoch 56/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1600 23.2% 2.7m\n", " 10 0.1490 23.2% 2.6m\n", " 20 0.2142 23.2% 2.6m\n", " 30 0.1677 23.2% 2.6m\n", " 40 0.1587 23.2% 2.6m\n", " 50 0.2010 23.2% 2.5m\n", " 60 0.1885 23.2% 2.5m\n", " 70 0.1760 23.2% 2.5m\n", " 80 0.1811 23.2% 2.4m\n", " 90 0.1616 23.2% 2.4m\n", " 100 0.1821 23.2% 2.4m\n", " 110 0.1537 23.2% 2.4m\n", " 120 0.1337 23.2% 2.3m\n", " 130 0.1557 23.2% 2.3m\n", " 140 0.1626 23.2% 2.3m\n", " 150 0.1730 23.2% 2.2m\n", " 160 0.1984 23.2% 2.2m\n", " 170 0.1809 23.2% 2.2m\n", " 180 0.1845 23.2% 2.1m\n", " 190 0.2071 23.2% 2.1m\n", " 200 0.1600 23.2% 2.1m\n", " 210 0.1765 23.2% 2.0m\n", " 220 0.1867 23.2% 2.0m\n", " 230 0.2149 23.2% 2.0m\n", " 240 0.1071 23.2% 1.9m\n", " 250 0.1585 23.2% 1.9m\n", " 260 0.1550 23.2% 1.9m\n", " 270 0.1732 23.2% 1.8m\n", " 280 0.1252 23.2% 1.8m\n", " 290 0.1232 23.2% 1.8m\n", " 300 0.1610 23.2% 1.8m\n", " 310 0.1589 23.2% 1.7m\n", " 320 0.1858 23.2% 1.7m\n", " 330 0.1759 23.2% 1.7m\n", " 340 0.1268 23.2% 1.6m\n", " 350 0.1756 23.2% 1.6m\n", " 360 0.1878 23.2% 1.6m\n", " 370 0.1668 23.2% 1.5m\n", " 380 0.1859 23.2% 1.5m\n", " 390 0.1730 23.2% 1.5m\n", " 400 0.1470 23.2% 1.4m\n", " 410 0.1743 23.2% 1.4m\n", " 420 0.1706 23.2% 1.4m\n", " 430 0.2061 23.2% 1.3m\n", " 440 0.1829 23.2% 1.3m\n", " 450 0.1418 23.2% 1.3m\n", " 460 0.1562 23.2% 1.3m\n", " 470 0.1866 23.2% 1.2m\n", " 480 0.1668 23.2% 1.2m\n", " 490 0.1950 23.2% 1.2m\n", " 500 0.1495 23.2% 1.1m\n", " 510 0.1639 23.2% 1.1m\n", " 520 0.1886 23.2% 1.1m\n", " 530 0.1538 23.2% 1.0m\n", " 540 0.1841 23.2% 1.0m\n", " 550 0.1560 23.2% 1.0m\n", " 560 0.1700 23.2% 0.9m\n", " 570 0.1604 23.2% 0.9m\n", " 580 0.1562 23.2% 0.9m\n", " 590 0.1841 23.2% 0.8m\n", " 600 0.1599 23.2% 0.8m\n", " 610 0.1427 23.2% 0.8m\n", " 620 0.1639 23.2% 0.8m\n", " 630 0.1683 23.2% 0.7m\n", " 640 0.1835 23.2% 0.7m\n", " 650 0.1591 23.2% 0.7m\n", " 660 0.1690 23.2% 0.6m\n", " 670 0.1809 23.2% 0.6m\n", " 680 0.1832 23.2% 0.6m\n", " 690 0.1693 23.2% 0.5m\n", " 700 0.1701 23.2% 0.5m\n", " 710 0.1679 23.2% 0.5m\n", " 720 0.2075 23.2% 0.4m\n", " 730 0.1630 23.2% 0.4m\n", " 740 0.1691 23.2% 0.4m\n", " 750 0.1250 23.2% 0.4m\n", " 760 0.1623 23.2% 0.3m\n", " 770 0.1800 23.2% 0.3m\n", " 780 0.1777 23.2% 0.3m\n", " 790 0.1630 23.2% 0.2m\n", " 800 0.1773 23.2% 0.2m\n", " 810 0.1638 23.2% 0.2m\n", " 820 0.1213 23.2% 0.1m\n", " 830 0.2062 23.2% 0.1m\n", " 840 0.1583 23.2% 0.1m\n", " 850 0.2057 23.2% 0.0m\n", " 860 0.1501 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 56 COMPLETE in 161s\n", "\n", "โœ… EPOCH 56 SUMMARY\n", " โฑ๏ธ Time: 169s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16665 โ†’ Val=0.16408\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 124m\n", "\n", "๐Ÿ”„ Epoch 57/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1816 23.2% 2.7m\n", " 10 0.1606 23.2% 2.7m\n", " 20 0.1607 23.2% 2.7m\n", " 30 0.1511 23.2% 2.6m\n", " 40 0.2000 23.2% 2.6m\n", " 50 0.1401 23.2% 2.6m\n", " 60 0.2117 23.2% 2.5m\n", " 70 0.1622 23.2% 2.5m\n", " 80 0.1644 23.2% 2.5m\n", " 90 0.1510 23.2% 2.4m\n", " 100 0.2003 23.2% 2.4m\n", " 110 0.1572 23.2% 2.4m\n", " 120 0.2166 23.2% 2.3m\n", " 130 0.1439 23.2% 2.3m\n", " 140 0.1651 23.2% 2.3m\n", " 150 0.1702 23.2% 2.2m\n", " 160 0.1323 23.2% 2.2m\n", " 170 0.1507 23.2% 2.2m\n", " 180 0.1652 23.2% 2.2m\n", " 190 0.1570 23.2% 2.1m\n", " 200 0.1839 23.2% 2.1m\n", " 210 0.1583 23.2% 2.1m\n", " 220 0.1617 23.2% 2.0m\n", " 230 0.1381 23.2% 2.0m\n", " 240 0.1410 23.2% 2.0m\n", " 250 0.1951 23.2% 1.9m\n", " 260 0.1670 23.2% 1.9m\n", " 270 0.1851 23.2% 1.9m\n", " 280 0.2040 23.2% 1.8m\n", " 290 0.1825 23.2% 1.8m\n", " 300 0.1727 23.2% 1.8m\n", " 310 0.1202 23.2% 1.7m\n", " 320 0.1682 23.2% 1.7m\n", " 330 0.1485 23.2% 1.7m\n", " 340 0.1660 23.2% 1.6m\n", " 350 0.2154 23.2% 1.6m\n", " 360 0.1614 23.2% 1.6m\n", " 370 0.1783 23.2% 1.6m\n", " 380 0.1544 23.2% 1.5m\n", " 390 0.1463 23.2% 1.5m\n", " 400 0.1352 23.2% 1.5m\n", " 410 0.1842 23.2% 1.4m\n", " 420 0.1945 23.2% 1.4m\n", " 430 0.1672 23.2% 1.4m\n", " 440 0.1700 23.2% 1.3m\n", " 450 0.1462 23.2% 1.3m\n", " 460 0.1861 23.2% 1.3m\n", " 470 0.1630 23.2% 1.2m\n", " 480 0.1615 23.2% 1.2m\n", " 490 0.2109 23.2% 1.2m\n", " 500 0.1573 23.2% 1.1m\n", " 510 0.1770 23.2% 1.1m\n", " 520 0.1934 23.2% 1.1m\n", " 530 0.2170 23.2% 1.0m\n", " 540 0.1648 23.2% 1.0m\n", " 550 0.2179 23.2% 1.0m\n", " 560 0.1799 23.2% 1.0m\n", " 570 0.1585 23.2% 0.9m\n", " 580 0.1768 23.2% 0.9m\n", " 590 0.1307 23.2% 0.9m\n", " 600 0.1854 23.2% 0.8m\n", " 610 0.1826 23.2% 0.8m\n", " 620 0.1898 23.2% 0.8m\n", " 630 0.1756 23.2% 0.7m\n", " 640 0.1894 23.2% 0.7m\n", " 650 0.1530 23.2% 0.7m\n", " 660 0.1608 23.2% 0.6m\n", " 670 0.1618 23.2% 0.6m\n", " 680 0.1387 23.2% 0.6m\n", " 690 0.1349 23.2% 0.5m\n", " 700 0.1754 23.2% 0.5m\n", " 710 0.1707 23.2% 0.5m\n", " 720 0.1706 23.2% 0.4m\n", " 730 0.1822 23.2% 0.4m\n", " 740 0.1314 23.2% 0.4m\n", " 750 0.1497 23.2% 0.4m\n", " 760 0.1821 23.2% 0.3m\n", " 770 0.1302 23.2% 0.3m\n", " 780 0.1417 23.2% 0.3m\n", " 790 0.1683 23.2% 0.2m\n", " 800 0.1920 23.2% 0.2m\n", " 810 0.1771 23.2% 0.2m\n", " 820 0.1429 23.2% 0.1m\n", " 830 0.1717 23.2% 0.1m\n", " 840 0.1752 23.2% 0.1m\n", " 850 0.1386 23.2% 0.0m\n", " 860 0.1722 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 57 COMPLETE in 163s\n", "\n", "โœ… EPOCH 57 SUMMARY\n", " โฑ๏ธ Time: 171s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16672 โ†’ Val=0.16393\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.3%\n", " โญ NEW BEST MODEL SAVED (val_loss=0.16393)\n", " โฑ๏ธ Remaining ETA โ‰ˆ 122m\n", "\n", "๐Ÿ”„ Epoch 58/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1805 23.2% 2.7m\n", " 10 0.1533 23.2% 2.7m\n", " 20 0.1760 23.2% 2.7m\n", " 30 0.1390 23.2% 2.7m\n", " 40 0.1423 23.2% 2.7m\n", " 50 0.2147 23.2% 2.6m\n", " 60 0.1676 23.2% 2.6m\n", " 70 0.1635 23.2% 2.5m\n", " 80 0.1496 23.2% 2.5m\n", " 90 0.1604 23.2% 2.5m\n", " 100 0.1827 23.2% 2.4m\n", " 110 0.1429 23.2% 2.4m\n", " 120 0.1557 23.2% 2.4m\n", " 130 0.1418 23.2% 2.3m\n", " 140 0.1470 23.2% 2.3m\n", " 150 0.1457 23.2% 2.3m\n", " 160 0.1664 23.2% 2.2m\n", " 170 0.1664 23.2% 2.2m\n", " 180 0.1695 23.2% 2.2m\n", " 190 0.1630 23.2% 2.1m\n", " 200 0.1460 23.2% 2.1m\n", " 210 0.1921 23.2% 2.1m\n", " 220 0.1789 23.2% 2.0m\n", " 230 0.1533 23.2% 2.0m\n", " 240 0.1790 23.2% 2.0m\n", " 250 0.1624 23.2% 1.9m\n", " 260 0.1662 23.2% 1.9m\n", " 270 0.1982 23.2% 1.9m\n", " 280 0.1499 23.2% 1.8m\n", " 290 0.1605 23.2% 1.8m\n", " 300 0.1490 23.2% 1.8m\n", " 310 0.1861 23.2% 1.7m\n", " 320 0.2172 23.2% 1.7m\n", " 330 0.1455 23.2% 1.7m\n", " 340 0.1662 23.2% 1.6m\n", " 350 0.1323 23.2% 1.6m\n", " 360 0.1672 23.2% 1.6m\n", " 370 0.1848 23.2% 1.6m\n", " 380 0.1881 23.2% 1.5m\n", " 390 0.1721 23.2% 1.5m\n", " 400 0.1486 23.2% 1.5m\n", " 410 0.1446 23.2% 1.4m\n", " 420 0.1414 23.2% 1.4m\n", " 430 0.1904 23.2% 1.4m\n", " 440 0.1690 23.2% 1.3m\n", " 450 0.1423 23.2% 1.3m\n", " 460 0.1767 23.2% 1.3m\n", " 470 0.1817 23.2% 1.2m\n", " 480 0.1581 23.2% 1.2m\n", " 490 0.1196 23.2% 1.2m\n", " 500 0.1852 23.2% 1.1m\n", " 510 0.1435 23.2% 1.1m\n", " 520 0.1607 23.2% 1.1m\n", " 530 0.1774 23.2% 1.0m\n", " 540 0.1436 23.2% 1.0m\n", " 550 0.1807 23.2% 1.0m\n", " 560 0.1624 23.2% 1.0m\n", " 570 0.1578 23.2% 0.9m\n", " 580 0.2074 23.2% 0.9m\n", " 590 0.1508 23.2% 0.9m\n", " 600 0.1459 23.2% 0.8m\n", " 610 0.1473 23.2% 0.8m\n", " 620 0.1239 23.2% 0.8m\n", " 630 0.1763 23.2% 0.7m\n", " 640 0.1703 23.2% 0.7m\n", " 650 0.1468 23.2% 0.7m\n", " 660 0.1739 23.2% 0.6m\n", " 670 0.1605 23.2% 0.6m\n", " 680 0.1810 23.2% 0.6m\n", " 690 0.1699 23.2% 0.5m\n", " 700 0.1816 23.2% 0.5m\n", " 710 0.1726 23.2% 0.5m\n", " 720 0.1286 23.2% 0.4m\n", " 730 0.1286 23.2% 0.4m\n", " 740 0.1931 23.2% 0.4m\n", " 750 0.1932 23.2% 0.4m\n", " 760 0.1655 23.2% 0.3m\n", " 770 0.1722 23.2% 0.3m\n", " 780 0.1741 23.2% 0.3m\n", " 790 0.1230 23.2% 0.2m\n", " 800 0.1914 23.2% 0.2m\n", " 810 0.1796 23.2% 0.2m\n", " 820 0.1488 23.2% 0.1m\n", " 830 0.1578 23.2% 0.1m\n", " 840 0.1857 23.2% 0.1m\n", " 850 0.1528 23.2% 0.0m\n", " 860 0.1718 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 58 COMPLETE in 162s\n", "\n", "โœ… EPOCH 58 SUMMARY\n", " โฑ๏ธ Time: 171s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16668 โ†’ Val=0.16396\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.3%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 119m\n", "\n", "๐Ÿ”„ Epoch 59/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1857 23.2% 2.7m\n", " 10 0.2005 23.2% 2.7m\n", " 20 0.1689 23.2% 2.6m\n", " 30 0.1472 23.2% 2.6m\n", " 40 0.1742 23.2% 2.6m\n", " 50 0.1909 23.2% 2.5m\n", " 60 0.2001 23.2% 2.5m\n", " 70 0.1393 23.2% 2.5m\n", " 80 0.1696 23.2% 2.4m\n", " 90 0.1688 23.2% 2.4m\n", " 100 0.1860 23.2% 2.4m\n", " 110 0.1501 23.2% 2.4m\n", " 120 0.1596 23.2% 2.3m\n", " 130 0.1543 23.2% 2.3m\n", " 140 0.1775 23.2% 2.3m\n", " 150 0.1560 23.2% 2.2m\n", " 160 0.1588 23.2% 2.2m\n", " 170 0.1627 23.2% 2.2m\n", " 180 0.1617 23.2% 2.1m\n", " 190 0.1942 23.2% 2.1m\n", " 200 0.2044 23.2% 2.1m\n", " 210 0.1568 23.2% 2.0m\n", " 220 0.1468 23.2% 2.0m\n", " 230 0.1753 23.2% 2.0m\n", " 240 0.1557 23.2% 1.9m\n", " 250 0.1549 23.2% 1.9m\n", " 260 0.1796 23.2% 1.9m\n", " 270 0.1688 23.2% 1.8m\n", " 280 0.1801 23.2% 1.8m\n", " 290 0.1473 23.2% 1.8m\n", " 300 0.2012 23.2% 1.8m\n", " 310 0.1586 23.2% 1.7m\n", " 320 0.1724 23.2% 1.7m\n", " 330 0.1549 23.2% 1.7m\n", " 340 0.1714 23.2% 1.6m\n", " 350 0.1766 23.2% 1.6m\n", " 360 0.1535 23.2% 1.6m\n", " 370 0.1705 23.2% 1.5m\n", " 380 0.1806 23.2% 1.5m\n", " 390 0.1548 23.2% 1.5m\n", " 400 0.1405 23.2% 1.4m\n", " 410 0.1674 23.2% 1.4m\n", " 420 0.1567 23.2% 1.4m\n", " 430 0.1475 23.2% 1.4m\n", " 440 0.1709 23.2% 1.3m\n", " 450 0.1877 23.2% 1.3m\n", " 460 0.1635 23.2% 1.3m\n", " 470 0.1513 23.2% 1.2m\n", " 480 0.1298 23.2% 1.2m\n", " 490 0.1789 23.2% 1.2m\n", " 500 0.1323 23.2% 1.1m\n", " 510 0.1765 23.2% 1.1m\n", " 520 0.1898 23.2% 1.1m\n", " 530 0.1221 23.2% 1.0m\n", " 540 0.1391 23.2% 1.0m\n", " 550 0.1877 23.2% 1.0m\n", " 560 0.1565 23.2% 0.9m\n", " 570 0.1613 23.2% 0.9m\n", " 580 0.1628 23.2% 0.9m\n", " 590 0.1717 23.2% 0.9m\n", " 600 0.1580 23.2% 0.8m\n", " 610 0.1701 23.2% 0.8m\n", " 620 0.1386 23.2% 0.8m\n", " 630 0.1509 23.2% 0.7m\n", " 640 0.1746 23.2% 0.7m\n", " 650 0.1763 23.2% 0.7m\n", " 660 0.1279 23.2% 0.6m\n", " 670 0.1492 23.2% 0.6m\n", " 680 0.1627 23.2% 0.6m\n", " 690 0.1569 23.2% 0.5m\n", " 700 0.1849 23.2% 0.5m\n", " 710 0.1933 23.2% 0.5m\n", " 720 0.1214 23.2% 0.4m\n", " 730 0.1595 23.2% 0.4m\n", " 740 0.1827 23.2% 0.4m\n", " 750 0.2011 23.2% 0.4m\n", " 760 0.1905 23.2% 0.3m\n", " 770 0.1497 23.2% 0.3m\n", " 780 0.1830 23.2% 0.3m\n", " 790 0.1529 23.2% 0.2m\n", " 800 0.1657 23.2% 0.2m\n", " 810 0.1551 23.2% 0.2m\n", " 820 0.1831 23.2% 0.1m\n", " 830 0.1800 23.2% 0.1m\n", " 840 0.2052 23.2% 0.1m\n", " 850 0.1707 23.2% 0.0m\n", " 860 0.1808 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 59 COMPLETE in 162s\n", "\n", "โœ… EPOCH 59 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16659 โ†’ Val=0.16415\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 116m\n", "\n", "๐Ÿ”„ Epoch 60/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1700 23.2% 2.7m\n", " 10 0.1300 23.2% 2.7m\n", " 20 0.1633 23.2% 2.7m\n", " 30 0.1520 23.2% 2.6m\n", " 40 0.1526 23.2% 2.6m\n", " 50 0.1821 23.2% 2.5m\n", " 60 0.1514 23.2% 2.5m\n", " 70 0.1776 23.2% 2.5m\n", " 80 0.1361 23.2% 2.4m\n", " 90 0.1424 23.2% 2.4m\n", " 100 0.1443 23.2% 2.4m\n", " 110 0.2094 23.2% 2.3m\n", " 120 0.1589 23.2% 2.3m\n", " 130 0.1820 23.2% 2.3m\n", " 140 0.1421 23.2% 2.3m\n", " 150 0.1814 23.2% 2.2m\n", " 160 0.1480 23.2% 2.2m\n", " 170 0.1036 23.2% 2.2m\n", " 180 0.1336 23.2% 2.1m\n", " 190 0.1637 23.2% 2.1m\n", " 200 0.1946 23.2% 2.1m\n", " 210 0.1452 23.2% 2.0m\n", " 220 0.1852 23.2% 2.0m\n", " 230 0.1762 23.2% 2.0m\n", " 240 0.1445 23.2% 1.9m\n", " 250 0.1458 23.2% 1.9m\n", " 260 0.1592 23.2% 1.9m\n", " 270 0.1712 23.2% 1.9m\n", " 280 0.1689 23.2% 1.8m\n", " 290 0.1452 23.2% 1.8m\n", " 300 0.1481 23.2% 1.8m\n", " 310 0.1831 23.2% 1.7m\n", " 320 0.1714 23.2% 1.7m\n", " 330 0.1911 23.2% 1.7m\n", " 340 0.1704 23.2% 1.6m\n", " 350 0.1761 23.2% 1.6m\n", " 360 0.1593 23.2% 1.6m\n", " 370 0.1836 23.2% 1.5m\n", " 380 0.1571 23.2% 1.5m\n", " 390 0.1380 23.2% 1.5m\n", " 400 0.2413 23.2% 1.5m\n", " 410 0.1709 23.2% 1.4m\n", " 420 0.1738 23.2% 1.4m\n", " 430 0.1470 23.2% 1.4m\n", " 440 0.1308 23.2% 1.3m\n", " 450 0.1856 23.2% 1.3m\n", " 460 0.1222 23.2% 1.3m\n", " 470 0.1734 23.2% 1.2m\n", " 480 0.2228 23.2% 1.2m\n", " 490 0.1349 23.2% 1.2m\n", " 500 0.1642 23.2% 1.1m\n", " 510 0.1732 23.2% 1.1m\n", " 520 0.1492 23.2% 1.1m\n", " 530 0.1664 23.2% 1.0m\n", " 540 0.1679 23.2% 1.0m\n", " 550 0.1575 23.2% 1.0m\n", " 560 0.1765 23.2% 0.9m\n", " 570 0.1482 23.2% 0.9m\n", " 580 0.1583 23.2% 0.9m\n", " 590 0.1968 23.2% 0.9m\n", " 600 0.1839 23.2% 0.8m\n", " 610 0.1750 23.2% 0.8m\n", " 620 0.1915 23.2% 0.8m\n", " 630 0.1829 23.2% 0.7m\n", " 640 0.1779 23.2% 0.7m\n", " 650 0.1903 23.2% 0.7m\n", " 660 0.1582 23.2% 0.6m\n", " 670 0.1651 23.2% 0.6m\n", " 680 0.1631 23.2% 0.6m\n", " 690 0.1663 23.2% 0.5m\n", " 700 0.2024 23.2% 0.5m\n", " 710 0.1568 23.2% 0.5m\n", " 720 0.1638 23.2% 0.4m\n", " 730 0.1774 23.2% 0.4m\n", " 740 0.1762 23.2% 0.4m\n", " 750 0.1815 23.2% 0.4m\n", " 760 0.1857 23.2% 0.3m\n", " 770 0.1511 23.2% 0.3m\n", " 780 0.1329 23.2% 0.3m\n", " 790 0.2023 23.2% 0.2m\n", " 800 0.1790 23.2% 0.2m\n", " 810 0.1434 23.2% 0.2m\n", " 820 0.1847 23.2% 0.1m\n", " 830 0.1608 23.2% 0.1m\n", " 840 0.1548 23.2% 0.1m\n", " 850 0.1361 23.2% 0.0m\n", " 860 0.1536 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 60 COMPLETE in 161s\n", "\n", "โœ… EPOCH 60 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16666 โ†’ Val=0.16479\n", " ๐Ÿ“Š Acc: EC=81.5% EL=75.4% EJ=86.6% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 113m\n", "\n", "๐Ÿ”„ Epoch 61/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1917 23.2% 2.6m\n", " 10 0.1465 23.2% 2.6m\n", " 20 0.1421 23.2% 2.6m\n", " 30 0.1211 23.2% 2.6m\n", " 40 0.1644 23.2% 2.6m\n", " 50 0.1882 23.2% 2.5m\n", " 60 0.1439 23.2% 2.5m\n", " 70 0.2124 23.2% 2.5m\n", " 80 0.1227 23.2% 2.4m\n", " 90 0.1368 23.2% 2.4m\n", " 100 0.1683 23.2% 2.4m\n", " 110 0.1607 23.2% 2.4m\n", " 120 0.1807 23.2% 2.3m\n", " 130 0.1608 23.2% 2.3m\n", " 140 0.2018 23.2% 2.3m\n", " 150 0.1703 23.2% 2.2m\n", " 160 0.1639 23.2% 2.2m\n", " 170 0.1735 23.2% 2.2m\n", " 180 0.1432 23.2% 2.1m\n", " 190 0.1481 23.2% 2.1m\n", " 200 0.1816 23.2% 2.1m\n", " 210 0.1888 23.2% 2.0m\n", " 220 0.1689 23.2% 2.0m\n", " 230 0.1634 23.2% 2.0m\n", " 240 0.1818 23.2% 1.9m\n", " 250 0.1354 23.2% 1.9m\n", " 260 0.1891 23.2% 1.9m\n", " 270 0.2137 23.2% 1.9m\n", " 280 0.1592 23.2% 1.8m\n", " 290 0.1656 23.2% 1.8m\n", " 300 0.1681 23.2% 1.8m\n", " 310 0.1781 23.2% 1.7m\n", " 320 0.1363 23.2% 1.7m\n", " 330 0.1632 23.2% 1.7m\n", " 340 0.1440 23.2% 1.6m\n", " 350 0.2139 23.2% 1.6m\n", " 360 0.1604 23.2% 1.6m\n", " 370 0.1651 23.2% 1.5m\n", " 380 0.1411 23.2% 1.5m\n", " 390 0.1484 23.2% 1.5m\n", " 400 0.1162 23.2% 1.4m\n", " 410 0.1491 23.2% 1.4m\n", " 420 0.1257 23.2% 1.4m\n", " 430 0.1451 23.2% 1.4m\n", " 440 0.1977 23.2% 1.3m\n", " 450 0.1590 23.2% 1.3m\n", " 460 0.1267 23.2% 1.3m\n", " 470 0.1766 23.2% 1.2m\n", " 480 0.1840 23.2% 1.2m\n", " 490 0.1906 23.2% 1.2m\n", " 500 0.1732 23.2% 1.1m\n", " 510 0.1422 23.2% 1.1m\n", " 520 0.1704 23.2% 1.1m\n", " 530 0.2108 23.2% 1.0m\n", " 540 0.1768 23.2% 1.0m\n", " 550 0.1669 23.2% 1.0m\n", " 560 0.1598 23.2% 0.9m\n", " 570 0.1750 23.2% 0.9m\n", " 580 0.1383 23.2% 0.9m\n", " 590 0.1786 23.2% 0.9m\n", " 600 0.1887 23.2% 0.8m\n", " 610 0.1361 23.2% 0.8m\n", " 620 0.1457 23.2% 0.8m\n", " 630 0.1857 23.2% 0.7m\n", " 640 0.1987 23.2% 0.7m\n", " 650 0.1432 23.2% 0.7m\n", " 660 0.1399 23.2% 0.6m\n", " 670 0.2035 23.2% 0.6m\n", " 680 0.2007 23.2% 0.6m\n", " 690 0.2008 23.2% 0.5m\n", " 700 0.1693 23.2% 0.5m\n", " 710 0.1439 23.2% 0.5m\n", " 720 0.1550 23.2% 0.4m\n", " 730 0.1651 23.2% 0.4m\n", " 740 0.1985 23.2% 0.4m\n", " 750 0.1735 23.2% 0.4m\n", " 760 0.1626 23.2% 0.3m\n", " 770 0.1656 23.2% 0.3m\n", " 780 0.1697 23.2% 0.3m\n", " 790 0.1729 23.2% 0.2m\n", " 800 0.1951 23.2% 0.2m\n", " 810 0.1622 23.2% 0.2m\n", " 820 0.2239 23.2% 0.1m\n", " 830 0.1393 23.2% 0.1m\n", " 840 0.1483 23.2% 0.1m\n", " 850 0.1384 23.2% 0.0m\n", " 860 0.1995 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 61 COMPLETE in 162s\n", "\n", "โœ… EPOCH 61 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16664 โ†’ Val=0.16383\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.5% EJ=86.7% Overall=81.3%\n", " โญ NEW BEST MODEL SAVED (val_loss=0.16383)\n", " โฑ๏ธ Remaining ETA โ‰ˆ 111m\n", "\n", "๐Ÿ”„ Epoch 62/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1784 23.2% 2.7m\n", " 10 0.1716 23.2% 2.7m\n", " 20 0.1580 23.2% 2.7m\n", " 30 0.1623 23.2% 2.6m\n", " 40 0.1605 23.2% 2.6m\n", " 50 0.1758 23.2% 2.6m\n", " 60 0.1813 23.2% 2.5m\n", " 70 0.1720 23.2% 2.5m\n", " 80 0.1528 23.2% 2.5m\n", " 90 0.1459 23.2% 2.4m\n", " 100 0.1602 23.2% 2.4m\n", " 110 0.1466 23.2% 2.4m\n", " 120 0.1164 23.2% 2.4m\n", " 130 0.1943 23.2% 2.3m\n", " 140 0.1475 23.2% 2.3m\n", " 150 0.1384 23.2% 2.3m\n", " 160 0.1885 23.2% 2.2m\n", " 170 0.1945 23.2% 2.2m\n", " 180 0.2034 23.2% 2.2m\n", " 190 0.1835 23.2% 2.1m\n", " 200 0.1872 23.2% 2.1m\n", " 210 0.1817 23.2% 2.1m\n", " 220 0.1860 23.2% 2.0m\n", " 230 0.1697 23.2% 2.0m\n", " 240 0.1999 23.2% 2.0m\n", " 250 0.1818 23.2% 1.9m\n", " 260 0.1691 23.2% 1.9m\n", " 270 0.1565 23.2% 1.9m\n", " 280 0.1915 23.2% 1.8m\n", " 290 0.2024 23.2% 1.8m\n", " 300 0.1694 23.2% 1.8m\n", " 310 0.1534 23.2% 1.7m\n", " 320 0.1809 23.2% 1.7m\n", " 330 0.1649 23.2% 1.7m\n", " 340 0.1469 23.2% 1.6m\n", " 350 0.1698 23.2% 1.6m\n", " 360 0.1610 23.2% 1.6m\n", " 370 0.1771 23.2% 1.6m\n", " 380 0.1594 23.2% 1.5m\n", " 390 0.1938 23.2% 1.5m\n", " 400 0.1858 23.2% 1.5m\n", " 410 0.1513 23.2% 1.4m\n", " 420 0.1482 23.2% 1.4m\n", " 430 0.1768 23.2% 1.4m\n", " 440 0.1600 23.2% 1.3m\n", " 450 0.1723 23.2% 1.3m\n", " 460 0.1560 23.2% 1.3m\n", " 470 0.2107 23.2% 1.2m\n", " 480 0.1556 23.2% 1.2m\n", " 490 0.1607 23.2% 1.2m\n", " 500 0.1495 23.2% 1.1m\n", " 510 0.1382 23.2% 1.1m\n", " 520 0.1750 23.2% 1.1m\n", " 530 0.1503 23.2% 1.0m\n", " 540 0.1718 23.2% 1.0m\n", " 550 0.1429 23.2% 1.0m\n", " 560 0.2241 23.2% 1.0m\n", " 570 0.1659 23.2% 0.9m\n", " 580 0.1700 23.2% 0.9m\n", " 590 0.1895 23.2% 0.9m\n", " 600 0.1780 23.2% 0.8m\n", " 610 0.1693 23.2% 0.8m\n", " 620 0.1710 23.2% 0.8m\n", " 630 0.1830 23.2% 0.7m\n", " 640 0.1587 23.2% 0.7m\n", " 650 0.1551 23.2% 0.7m\n", " 660 0.1462 23.2% 0.6m\n", " 670 0.1504 23.2% 0.6m\n", " 680 0.1468 23.2% 0.6m\n", " 690 0.1332 23.2% 0.5m\n", " 700 0.1260 23.2% 0.5m\n", " 710 0.1187 23.2% 0.5m\n", " 720 0.1556 23.2% 0.5m\n", " 730 0.1573 23.2% 0.4m\n", " 740 0.1933 23.2% 0.4m\n", " 750 0.1728 23.2% 0.4m\n", " 760 0.2077 23.2% 0.3m\n", " 770 0.1570 23.2% 0.3m\n", " 780 0.1974 23.2% 0.3m\n", " 790 0.1227 23.2% 0.2m\n", " 800 0.1823 23.2% 0.2m\n", " 810 0.1781 23.2% 0.2m\n", " 820 0.1654 23.2% 0.1m\n", " 830 0.1354 23.2% 0.1m\n", " 840 0.1989 23.2% 0.1m\n", " 850 0.1620 23.2% 0.0m\n", " 860 0.1752 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 62 COMPLETE in 163s\n", "\n", "โœ… EPOCH 62 SUMMARY\n", " โฑ๏ธ Time: 171s (2.9m)\n", " ๐Ÿ“‰ Loss: Train=0.16654 โ†’ Val=0.16476\n", " ๐Ÿ“Š Acc: EC=81.5% EL=75.4% EJ=86.6% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 108m\n", "\n", "๐Ÿ”„ Epoch 63/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1730 23.2% 3.0m\n", " 10 0.1466 23.2% 2.7m\n", " 20 0.1656 23.2% 2.7m\n", " 30 0.1974 23.2% 2.6m\n", " 40 0.1929 23.2% 2.6m\n", " 50 0.1635 23.2% 2.5m\n", " 60 0.1577 23.2% 2.5m\n", " 70 0.1678 23.2% 2.5m\n", " 80 0.1884 23.2% 2.5m\n", " 90 0.1683 23.2% 2.4m\n", " 100 0.1340 23.2% 2.4m\n", " 110 0.1227 23.2% 2.4m\n", " 120 0.1685 23.2% 2.3m\n", " 130 0.1191 23.2% 2.3m\n", " 140 0.1490 23.2% 2.3m\n", " 150 0.1743 23.2% 2.2m\n", " 160 0.1493 23.2% 2.2m\n", " 170 0.1604 23.2% 2.2m\n", " 180 0.1930 23.2% 2.1m\n", " 190 0.1677 23.2% 2.1m\n", " 200 0.1669 23.2% 2.1m\n", " 210 0.1527 23.2% 2.0m\n", " 220 0.1412 23.2% 2.0m\n", " 230 0.1895 23.2% 2.0m\n", " 240 0.1646 23.2% 1.9m\n", " 250 0.1773 23.2% 1.9m\n", " 260 0.1683 23.2% 1.9m\n", " 270 0.1860 23.2% 1.9m\n", " 280 0.1783 23.2% 1.8m\n", " 290 0.1639 23.2% 1.8m\n", " 300 0.2027 23.2% 1.8m\n", " 310 0.1786 23.2% 1.7m\n", " 320 0.1964 23.2% 1.7m\n", " 330 0.1590 23.2% 1.7m\n", " 340 0.1969 23.2% 1.6m\n", " 350 0.1663 23.2% 1.6m\n", " 360 0.1773 23.2% 1.6m\n", " 370 0.1135 23.2% 1.5m\n", " 380 0.1421 23.2% 1.5m\n", " 390 0.1503 23.2% 1.5m\n", " 400 0.1906 23.2% 1.4m\n", " 410 0.1349 23.2% 1.4m\n", " 420 0.1657 23.2% 1.4m\n", " 430 0.1349 23.2% 1.4m\n", " 440 0.1756 23.2% 1.3m\n", " 450 0.1501 23.2% 1.3m\n", " 460 0.1482 23.2% 1.3m\n", " 470 0.1577 23.2% 1.2m\n", " 480 0.1499 23.2% 1.2m\n", " 490 0.1251 23.2% 1.2m\n", " 500 0.1327 23.2% 1.1m\n", " 510 0.1828 23.2% 1.1m\n", " 520 0.1536 23.2% 1.1m\n", " 530 0.1974 23.2% 1.0m\n", " 540 0.1741 23.2% 1.0m\n", " 550 0.1732 23.2% 1.0m\n", " 560 0.1803 23.2% 0.9m\n", " 570 0.1586 23.2% 0.9m\n", " 580 0.1682 23.2% 0.9m\n", " 590 0.1858 23.2% 0.9m\n", " 600 0.1951 23.2% 0.8m\n", " 610 0.1938 23.2% 0.8m\n", " 620 0.1511 23.2% 0.8m\n", " 630 0.1774 23.2% 0.7m\n", " 640 0.1920 23.2% 0.7m\n", " 650 0.1600 23.2% 0.7m\n", " 660 0.1894 23.2% 0.6m\n", " 670 0.1417 23.2% 0.6m\n", " 680 0.1553 23.2% 0.6m\n", " 690 0.1631 23.2% 0.5m\n", " 700 0.2005 23.2% 0.5m\n", " 710 0.1414 23.2% 0.5m\n", " 720 0.1252 23.2% 0.4m\n", " 730 0.1787 23.2% 0.4m\n", " 740 0.1928 23.2% 0.4m\n", " 750 0.1531 23.2% 0.4m\n", " 760 0.1421 23.2% 0.3m\n", " 770 0.1688 23.2% 0.3m\n", " 780 0.1894 23.2% 0.3m\n", " 790 0.2329 23.2% 0.2m\n", " 800 0.1660 23.2% 0.2m\n", " 810 0.1463 23.2% 0.2m\n", " 820 0.1815 23.2% 0.1m\n", " 830 0.1795 23.2% 0.1m\n", " 840 0.1751 23.2% 0.1m\n", " 850 0.1766 23.2% 0.0m\n", " 860 0.1618 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 63 COMPLETE in 162s\n", "\n", "โœ… EPOCH 63 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16670 โ†’ Val=0.16395\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.3%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 105m\n", "\n", "๐Ÿ”„ Epoch 64/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1844 23.2% 2.7m\n", " 10 0.1650 23.2% 2.7m\n", " 20 0.2116 23.2% 2.7m\n", " 30 0.1603 23.2% 2.7m\n", " 40 0.2244 23.2% 2.6m\n", " 50 0.1894 23.2% 2.6m\n", " 60 0.1731 23.2% 2.5m\n", " 70 0.1483 23.2% 2.5m\n", " 80 0.2023 23.2% 2.5m\n", " 90 0.1732 23.2% 2.4m\n", " 100 0.1690 23.2% 2.4m\n", " 110 0.1857 23.2% 2.4m\n", " 120 0.1718 23.2% 2.3m\n", " 130 0.1274 23.2% 2.3m\n", " 140 0.2050 23.2% 2.3m\n", " 150 0.1678 23.2% 2.2m\n", " 160 0.1844 23.2% 2.2m\n", " 170 0.1793 23.2% 2.2m\n", " 180 0.1568 23.2% 2.1m\n", " 190 0.1683 23.2% 2.1m\n", " 200 0.1537 23.2% 2.1m\n", " 210 0.1712 23.2% 2.1m\n", " 220 0.2206 23.2% 2.0m\n", " 230 0.1758 23.2% 2.0m\n", " 240 0.1590 23.2% 2.0m\n", " 250 0.1608 23.2% 1.9m\n", " 260 0.1446 23.2% 1.9m\n", " 270 0.1851 23.2% 1.9m\n", " 280 0.1640 23.2% 1.8m\n", " 290 0.1589 23.2% 1.8m\n", " 300 0.1994 23.2% 1.8m\n", " 310 0.1334 23.2% 1.7m\n", " 320 0.1635 23.2% 1.7m\n", " 330 0.1459 23.2% 1.7m\n", " 340 0.1694 23.2% 1.6m\n", " 350 0.1541 23.2% 1.6m\n", " 360 0.2121 23.2% 1.6m\n", " 370 0.1709 23.2% 1.6m\n", " 380 0.1787 23.2% 1.5m\n", " 390 0.1678 23.2% 1.5m\n", " 400 0.1944 23.2% 1.5m\n", " 410 0.1758 23.2% 1.4m\n", " 420 0.2177 23.2% 1.4m\n", " 430 0.1940 23.2% 1.4m\n", " 440 0.1594 23.2% 1.3m\n", " 450 0.1503 23.2% 1.3m\n", " 460 0.1750 23.2% 1.3m\n", " 470 0.1846 23.2% 1.2m\n", " 480 0.1284 23.2% 1.2m\n", " 490 0.1328 23.2% 1.2m\n", " 500 0.1970 23.2% 1.1m\n", " 510 0.1446 23.2% 1.1m\n", " 520 0.1471 23.2% 1.1m\n", " 530 0.1642 23.2% 1.0m\n", " 540 0.1936 23.2% 1.0m\n", " 550 0.1791 23.2% 1.0m\n", " 560 0.1296 23.2% 1.0m\n", " 570 0.1761 23.2% 0.9m\n", " 580 0.1138 23.2% 0.9m\n", " 590 0.1745 23.2% 0.9m\n", " 600 0.2255 23.2% 0.8m\n", " 610 0.1779 23.2% 0.8m\n", " 620 0.1353 23.2% 0.8m\n", " 630 0.1636 23.2% 0.7m\n", " 640 0.1731 23.2% 0.7m\n", " 650 0.1528 23.2% 0.7m\n", " 660 0.1753 23.2% 0.6m\n", " 670 0.1798 23.2% 0.6m\n", " 680 0.1708 23.2% 0.6m\n", " 690 0.1957 23.2% 0.5m\n", " 700 0.1326 23.2% 0.5m\n", " 710 0.1631 23.2% 0.5m\n", " 720 0.1160 23.2% 0.4m\n", " 730 0.1866 23.2% 0.4m\n", " 740 0.1702 23.2% 0.4m\n", " 750 0.2170 23.2% 0.4m\n", " 760 0.1860 23.2% 0.3m\n", " 770 0.1764 23.2% 0.3m\n", " 780 0.1234 23.2% 0.3m\n", " 790 0.1529 23.2% 0.2m\n", " 800 0.1106 23.2% 0.2m\n", " 810 0.1924 23.2% 0.2m\n", " 820 0.1505 23.2% 0.1m\n", " 830 0.1521 23.2% 0.1m\n", " 840 0.1826 23.2% 0.1m\n", " 850 0.1971 23.2% 0.0m\n", " 860 0.1815 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 64 COMPLETE in 162s\n", "\n", "โœ… EPOCH 64 SUMMARY\n", " โฑ๏ธ Time: 171s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16671 โ†’ Val=0.16392\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.5% EJ=86.7% Overall=81.3%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 102m\n", "\n", "๐Ÿ”„ Epoch 65/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1760 23.2% 2.7m\n", " 10 0.1809 23.2% 2.7m\n", " 20 0.1602 23.2% 2.6m\n", " 30 0.1323 23.2% 2.6m\n", " 40 0.1644 23.2% 2.6m\n", " 50 0.1950 23.2% 2.5m\n", " 60 0.1875 23.2% 2.5m\n", " 70 0.2017 23.2% 2.5m\n", " 80 0.1611 23.2% 2.4m\n", " 90 0.1864 23.2% 2.4m\n", " 100 0.1694 23.2% 2.4m\n", " 110 0.1565 23.2% 2.4m\n", " 120 0.1444 23.2% 2.3m\n", " 130 0.1796 23.2% 2.3m\n", " 140 0.1800 23.2% 2.3m\n", " 150 0.1661 23.2% 2.2m\n", " 160 0.1659 23.2% 2.2m\n", " 170 0.1963 23.2% 2.2m\n", " 180 0.1222 23.2% 2.1m\n", " 190 0.1497 23.2% 2.1m\n", " 200 0.1364 23.2% 2.1m\n", " 210 0.1915 23.2% 2.0m\n", " 220 0.1890 23.2% 2.0m\n", " 230 0.1740 23.2% 2.0m\n", " 240 0.1841 23.2% 1.9m\n", " 250 0.2019 23.2% 1.9m\n", " 260 0.1251 23.2% 1.9m\n", " 270 0.1861 23.2% 1.8m\n", " 280 0.1465 23.2% 1.8m\n", " 290 0.1408 23.2% 1.8m\n", " 300 0.1745 23.2% 1.8m\n", " 310 0.1589 23.2% 1.7m\n", " 320 0.1879 23.2% 1.7m\n", " 330 0.1745 23.2% 1.7m\n", " 340 0.1554 23.2% 1.6m\n", " 350 0.1604 23.2% 1.6m\n", " 360 0.1485 23.2% 1.6m\n", " 370 0.1851 23.2% 1.5m\n", " 380 0.1650 23.2% 1.5m\n", " 390 0.1736 23.2% 1.5m\n", " 400 0.2103 23.2% 1.4m\n", " 410 0.1731 23.2% 1.4m\n", " 420 0.1484 23.2% 1.4m\n", " 430 0.1406 23.2% 1.4m\n", " 440 0.1525 23.2% 1.3m\n", " 450 0.1756 23.2% 1.3m\n", " 460 0.1971 23.2% 1.3m\n", " 470 0.1666 23.2% 1.2m\n", " 480 0.1541 23.2% 1.2m\n", " 490 0.1631 23.2% 1.2m\n", " 500 0.1943 23.2% 1.1m\n", " 510 0.1673 23.2% 1.1m\n", " 520 0.1595 23.2% 1.1m\n", " 530 0.1650 23.2% 1.0m\n", " 540 0.1922 23.2% 1.0m\n", " 550 0.1454 23.2% 1.0m\n", " 560 0.1940 23.2% 0.9m\n", " 570 0.1414 23.2% 0.9m\n", " 580 0.1972 23.2% 0.9m\n", " 590 0.1349 23.2% 0.9m\n", " 600 0.1622 23.2% 0.8m\n", " 610 0.1982 23.2% 0.8m\n", " 620 0.1445 23.2% 0.8m\n", " 630 0.1803 23.2% 0.7m\n", " 640 0.2003 23.2% 0.7m\n", " 650 0.1265 23.2% 0.7m\n", " 660 0.1694 23.2% 0.6m\n", " 670 0.1782 23.2% 0.6m\n", " 680 0.1953 23.2% 0.6m\n", " 690 0.1742 23.2% 0.5m\n", " 700 0.1847 23.2% 0.5m\n", " 710 0.1795 23.2% 0.5m\n", " 720 0.1930 23.2% 0.4m\n", " 730 0.1955 23.2% 0.4m\n", " 740 0.1904 23.2% 0.4m\n", " 750 0.1527 23.2% 0.4m\n", " 760 0.1938 23.2% 0.3m\n", " 770 0.1711 23.2% 0.3m\n", " 780 0.1658 23.2% 0.3m\n", " 790 0.1207 23.2% 0.2m\n", " 800 0.1667 23.2% 0.2m\n", " 810 0.1369 23.2% 0.2m\n", " 820 0.1863 23.2% 0.1m\n", " 830 0.1404 23.2% 0.1m\n", " 840 0.1844 23.2% 0.1m\n", " 850 0.1572 23.2% 0.0m\n", " 860 0.1914 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 65 COMPLETE in 162s\n", "\n", "โœ… EPOCH 65 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16659 โ†’ Val=0.16393\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.3%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 99m\n", "\n", "๐Ÿ”„ Epoch 66/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1368 23.2% 2.7m\n", " 10 0.1750 23.2% 2.6m\n", " 20 0.1832 23.2% 2.6m\n", " 30 0.1355 23.2% 2.6m\n", " 40 0.1555 23.2% 2.6m\n", " 50 0.2031 23.2% 2.5m\n", " 60 0.2294 23.2% 2.5m\n", " 70 0.1442 23.2% 2.5m\n", " 80 0.2189 23.2% 2.4m\n", " 90 0.1749 23.2% 2.4m\n", " 100 0.1777 23.2% 2.4m\n", " 110 0.1316 23.2% 2.4m\n", " 120 0.1652 23.2% 2.3m\n", " 130 0.1739 23.2% 2.3m\n", " 140 0.1423 23.2% 2.3m\n", " 150 0.1802 23.2% 2.2m\n", " 160 0.1618 23.2% 2.2m\n", " 170 0.1564 23.2% 2.2m\n", " 180 0.1307 23.2% 2.1m\n", " 190 0.1709 23.2% 2.1m\n", " 200 0.1593 23.2% 2.1m\n", " 210 0.1434 23.2% 2.1m\n", " 220 0.1608 23.2% 2.0m\n", " 230 0.2078 23.2% 2.0m\n", " 240 0.1963 23.2% 2.0m\n", " 250 0.1895 23.2% 1.9m\n", " 260 0.1642 23.2% 1.9m\n", " 270 0.1460 23.2% 1.9m\n", " 280 0.1693 23.2% 1.8m\n", " 290 0.2244 23.2% 1.8m\n", " 300 0.1368 23.2% 1.8m\n", " 310 0.1817 23.2% 1.7m\n", " 320 0.2000 23.2% 1.7m\n", " 330 0.1644 23.2% 1.7m\n", " 340 0.1931 23.2% 1.6m\n", " 350 0.1235 23.2% 1.6m\n", " 360 0.1888 23.2% 1.6m\n", " 370 0.0987 23.2% 1.5m\n", " 380 0.1647 23.2% 1.5m\n", " 390 0.1720 23.2% 1.5m\n", " 400 0.1471 23.2% 1.5m\n", " 410 0.1565 23.2% 1.4m\n", " 420 0.1996 23.2% 1.4m\n", " 430 0.1902 23.2% 1.4m\n", " 440 0.1481 23.2% 1.3m\n", " 450 0.1720 23.2% 1.3m\n", " 460 0.1683 23.2% 1.3m\n", " 470 0.1589 23.2% 1.2m\n", " 480 0.1584 23.2% 1.2m\n", " 490 0.1878 23.2% 1.2m\n", " 500 0.1567 23.2% 1.1m\n", " 510 0.1760 23.2% 1.1m\n", " 520 0.1679 23.2% 1.1m\n", " 530 0.1678 23.2% 1.0m\n", " 540 0.1480 23.2% 1.0m\n", " 550 0.1626 23.2% 1.0m\n", " 560 0.1491 23.2% 1.0m\n", " 570 0.1769 23.2% 0.9m\n", " 580 0.1824 23.2% 0.9m\n", " 590 0.1706 23.2% 0.9m\n", " 600 0.1531 23.2% 0.8m\n", " 610 0.1385 23.2% 0.8m\n", " 620 0.1680 23.2% 0.8m\n", " 630 0.2185 23.2% 0.7m\n", " 640 0.1855 23.2% 0.7m\n", " 650 0.1920 23.2% 0.7m\n", " 660 0.1739 23.2% 0.6m\n", " 670 0.1525 23.2% 0.6m\n", " 680 0.1442 23.2% 0.6m\n", " 690 0.1953 23.2% 0.5m\n", " 700 0.2097 23.2% 0.5m\n", " 710 0.1734 23.2% 0.5m\n", " 720 0.2091 23.2% 0.4m\n", " 730 0.1625 23.2% 0.4m\n", " 740 0.1857 23.2% 0.4m\n", " 750 0.1612 23.2% 0.4m\n", " 760 0.1557 23.2% 0.3m\n", " 770 0.1849 23.2% 0.3m\n", " 780 0.1595 23.2% 0.3m\n", " 790 0.1614 23.2% 0.2m\n", " 800 0.1761 23.2% 0.2m\n", " 810 0.1455 23.2% 0.2m\n", " 820 0.1966 23.2% 0.1m\n", " 830 0.1708 23.2% 0.1m\n", " 840 0.1596 23.2% 0.1m\n", " 850 0.1507 23.2% 0.0m\n", " 860 0.1868 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 66 COMPLETE in 163s\n", "\n", "โœ… EPOCH 66 SUMMARY\n", " โฑ๏ธ Time: 171s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16658 โ†’ Val=0.16399\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.2%\n", " โš ๏ธ No improvement for 5/30\n", " โฑ๏ธ Remaining ETA โ‰ˆ 97m\n", "\n", "๐Ÿ”„ Epoch 67/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1238 23.2% 2.6m\n", " 10 0.1678 23.2% 2.6m\n", " 20 0.1443 23.2% 2.6m\n", " 30 0.1524 23.2% 2.6m\n", " 40 0.1927 23.2% 2.6m\n", " 50 0.1444 23.2% 2.6m\n", " 60 0.2063 23.2% 2.5m\n", " 70 0.1850 23.2% 2.5m\n", " 80 0.1517 23.2% 2.5m\n", " 90 0.1609 23.2% 2.4m\n", " 100 0.1594 23.2% 2.4m\n", " 110 0.1666 23.2% 2.4m\n", " 120 0.1657 23.2% 2.3m\n", " 130 0.1294 23.2% 2.3m\n", " 140 0.1591 23.2% 2.3m\n", " 150 0.1890 23.2% 2.2m\n", " 160 0.1669 23.2% 2.2m\n", " 170 0.1300 23.2% 2.2m\n", " 180 0.1708 23.2% 2.1m\n", " 190 0.2005 23.2% 2.1m\n", " 200 0.1136 23.2% 2.1m\n", " 210 0.1631 23.2% 2.0m\n", " 220 0.1848 23.2% 2.0m\n", " 230 0.1499 23.2% 2.0m\n", " 240 0.1667 23.2% 2.0m\n", " 250 0.1506 23.2% 1.9m\n", " 260 0.2018 23.2% 1.9m\n", " 270 0.1963 23.2% 1.9m\n", " 280 0.1511 23.2% 1.8m\n", " 290 0.1904 23.2% 1.8m\n", " 300 0.1321 23.2% 1.8m\n", " 310 0.1351 23.2% 1.7m\n", " 320 0.1914 23.2% 1.7m\n", " 330 0.1439 23.2% 1.7m\n", " 340 0.1602 23.2% 1.6m\n", " 350 0.1500 23.2% 1.6m\n", " 360 0.1683 23.2% 1.6m\n", " 370 0.1153 23.2% 1.5m\n", " 380 0.1782 23.2% 1.5m\n", " 390 0.2033 23.2% 1.5m\n", " 400 0.2078 23.2% 1.4m\n", " 410 0.1588 23.2% 1.4m\n", " 420 0.1411 23.2% 1.4m\n", " 430 0.2008 23.2% 1.4m\n", " 440 0.1800 23.2% 1.3m\n", " 450 0.1968 23.2% 1.3m\n", " 460 0.1716 23.2% 1.3m\n", " 470 0.1466 23.2% 1.2m\n", " 480 0.2054 23.2% 1.2m\n", " 490 0.1805 23.2% 1.2m\n", " 500 0.1465 23.2% 1.1m\n", " 510 0.1619 23.2% 1.1m\n", " 520 0.1344 23.2% 1.1m\n", " 530 0.1629 23.2% 1.0m\n", " 540 0.1608 23.2% 1.0m\n", " 550 0.1558 23.2% 1.0m\n", " 560 0.1827 23.2% 0.9m\n", " 570 0.1411 23.2% 0.9m\n", " 580 0.1693 23.2% 0.9m\n", " 590 0.1866 23.2% 0.9m\n", " 600 0.1952 23.2% 0.8m\n", " 610 0.1910 23.2% 0.8m\n", " 620 0.1470 23.2% 0.8m\n", " 630 0.1344 23.2% 0.7m\n", " 640 0.1626 23.2% 0.7m\n", " 650 0.1942 23.2% 0.7m\n", " 660 0.1196 23.2% 0.6m\n", " 670 0.2022 23.2% 0.6m\n", " 680 0.1745 23.2% 0.6m\n", " 690 0.1587 23.2% 0.5m\n", " 700 0.1597 23.2% 0.5m\n", " 710 0.1773 23.2% 0.5m\n", " 720 0.1435 23.2% 0.4m\n", " 730 0.1544 23.2% 0.4m\n", " 740 0.1920 23.2% 0.4m\n", " 750 0.1435 23.2% 0.4m\n", " 760 0.1786 23.2% 0.3m\n", " 770 0.1807 23.2% 0.3m\n", " 780 0.2178 23.2% 0.3m\n", " 790 0.1759 23.2% 0.2m\n", " 800 0.1708 23.2% 0.2m\n", " 810 0.1245 23.2% 0.2m\n", " 820 0.1499 23.2% 0.1m\n", " 830 0.1457 23.2% 0.1m\n", " 840 0.1482 23.2% 0.1m\n", " 850 0.1627 23.2% 0.0m\n", " 860 0.1474 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 67 COMPLETE in 162s\n", "\n", "โœ… EPOCH 67 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16659 โ†’ Val=0.16395\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.8% Overall=81.3%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 94m\n", "\n", "๐Ÿ”„ Epoch 68/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1739 23.2% 2.8m\n", " 10 0.1590 23.2% 2.7m\n", " 20 0.1825 23.2% 2.6m\n", " 30 0.1629 23.2% 2.6m\n", " 40 0.1621 23.2% 2.6m\n", " 50 0.1796 23.2% 2.5m\n", " 60 0.1753 23.2% 2.5m\n", " 70 0.1834 23.2% 2.5m\n", " 80 0.1643 23.2% 2.5m\n", " 90 0.1957 23.2% 2.4m\n", " 100 0.1989 23.2% 2.4m\n", " 110 0.1706 23.2% 2.4m\n", " 120 0.1634 23.2% 2.3m\n", " 130 0.1566 23.2% 2.3m\n", " 140 0.2042 23.2% 2.3m\n", " 150 0.1727 23.2% 2.2m\n", " 160 0.1677 23.2% 2.2m\n", " 170 0.1652 23.2% 2.2m\n", " 180 0.1414 23.2% 2.1m\n", " 190 0.1766 23.2% 2.1m\n", " 200 0.1330 23.2% 2.1m\n", " 210 0.1687 23.2% 2.1m\n", " 220 0.1562 23.2% 2.0m\n", " 230 0.1599 23.2% 2.0m\n", " 240 0.1644 23.2% 2.0m\n", " 250 0.1759 23.2% 1.9m\n", " 260 0.1961 23.2% 1.9m\n", " 270 0.1575 23.2% 1.9m\n", " 280 0.1622 23.2% 1.8m\n", " 290 0.1607 23.2% 1.8m\n", " 300 0.1846 23.2% 1.8m\n", " 310 0.1674 23.2% 1.7m\n", " 320 0.1544 23.2% 1.7m\n", " 330 0.1647 23.2% 1.7m\n", " 340 0.1548 23.2% 1.6m\n", " 350 0.1900 23.2% 1.6m\n", " 360 0.1782 23.2% 1.6m\n", " 370 0.1633 23.2% 1.6m\n", " 380 0.1545 23.2% 1.5m\n", " 390 0.1581 23.2% 1.5m\n", " 400 0.1513 23.2% 1.5m\n", " 410 0.1541 23.2% 1.4m\n", " 420 0.1562 23.2% 1.4m\n", " 430 0.1706 23.2% 1.4m\n", " 440 0.1530 23.2% 1.3m\n", " 450 0.1786 23.2% 1.3m\n", " 460 0.1833 23.2% 1.3m\n", " 470 0.1557 23.2% 1.2m\n", " 480 0.2081 23.2% 1.2m\n", " 490 0.1649 23.2% 1.2m\n", " 500 0.2003 23.2% 1.1m\n", " 510 0.1527 23.2% 1.1m\n", " 520 0.1598 23.2% 1.1m\n", " 530 0.1505 23.2% 1.1m\n", " 540 0.1625 23.2% 1.0m\n", " 550 0.1413 23.2% 1.0m\n", " 560 0.1439 23.2% 1.0m\n", " 570 0.1816 23.2% 0.9m\n", " 580 0.1683 23.2% 0.9m\n", " 590 0.1608 23.2% 0.9m\n", " 600 0.2359 23.2% 0.8m\n", " 610 0.1737 23.2% 0.8m\n", " 620 0.1609 23.2% 0.8m\n", " 630 0.1479 23.2% 0.7m\n", " 640 0.1877 23.2% 0.7m\n", " 650 0.1641 23.2% 0.7m\n", " 660 0.1718 23.2% 0.6m\n", " 670 0.1678 23.2% 0.6m\n", " 680 0.1739 23.2% 0.6m\n", " 690 0.1750 23.2% 0.5m\n", " 700 0.2218 23.2% 0.5m\n", " 710 0.1402 23.2% 0.5m\n", " 720 0.1670 23.2% 0.5m\n", " 730 0.1894 23.2% 0.4m\n", " 740 0.1716 23.2% 0.4m\n", " 750 0.1340 23.2% 0.4m\n", " 760 0.1680 23.2% 0.3m\n", " 770 0.1287 23.2% 0.3m\n", " 780 0.1926 23.2% 0.3m\n", " 790 0.1758 23.2% 0.2m\n", " 800 0.1423 23.2% 0.2m\n", " 810 0.1608 23.2% 0.2m\n", " 820 0.1709 23.2% 0.1m\n", " 830 0.1245 23.2% 0.1m\n", " 840 0.1850 23.2% 0.1m\n", " 850 0.1638 23.2% 0.0m\n", " 860 0.1992 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 68 COMPLETE in 163s\n", "\n", "โœ… EPOCH 68 SUMMARY\n", " โฑ๏ธ Time: 171s (2.9m)\n", " ๐Ÿ“‰ Loss: Train=0.16660 โ†’ Val=0.16411\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 91m\n", "\n", "๐Ÿ”„ Epoch 69/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1917 23.2% 2.6m\n", " 10 0.1844 23.2% 2.7m\n", " 20 0.1528 23.2% 2.7m\n", " 30 0.1822 23.2% 2.6m\n", " 40 0.1625 23.2% 2.6m\n", " 50 0.1505 23.2% 2.6m\n", " 60 0.1666 23.2% 2.5m\n", " 70 0.2017 23.2% 2.5m\n", " 80 0.1858 23.2% 2.5m\n", " 90 0.1384 23.2% 2.4m\n", " 100 0.1755 23.2% 2.4m\n", " 110 0.1880 23.2% 2.4m\n", " 120 0.2013 23.2% 2.3m\n", " 130 0.1884 23.2% 2.3m\n", " 140 0.1698 23.2% 2.3m\n", " 150 0.1798 23.2% 2.2m\n", " 160 0.1451 23.2% 2.2m\n", " 170 0.1435 23.2% 2.2m\n", " 180 0.1752 23.2% 2.1m\n", " 190 0.1997 23.2% 2.1m\n", " 200 0.1442 23.2% 2.1m\n", " 210 0.1278 23.2% 2.1m\n", " 220 0.2023 23.2% 2.0m\n", " 230 0.1309 23.2% 2.0m\n", " 240 0.1553 23.2% 2.0m\n", " 250 0.1656 23.2% 1.9m\n", " 260 0.1381 23.2% 1.9m\n", " 270 0.1286 23.2% 1.9m\n", " 280 0.1696 23.2% 1.8m\n", " 290 0.1670 23.2% 1.8m\n", " 300 0.1629 23.2% 1.8m\n", " 310 0.1580 23.2% 1.7m\n", " 320 0.1678 23.2% 1.7m\n", " 330 0.1275 23.2% 1.7m\n", " 340 0.1356 23.2% 1.7m\n", " 350 0.1779 23.2% 1.6m\n", " 360 0.1597 23.2% 1.6m\n", " 370 0.1637 23.2% 1.6m\n", " 380 0.1932 23.2% 1.5m\n", " 390 0.1596 23.2% 1.5m\n", " 400 0.1625 23.2% 1.5m\n", " 410 0.1507 23.2% 1.4m\n", " 420 0.2231 23.2% 1.4m\n", " 430 0.2116 23.2% 1.4m\n", " 440 0.1815 23.2% 1.3m\n", " 450 0.1631 23.2% 1.3m\n", " 460 0.1293 23.2% 1.3m\n", " 470 0.1737 23.2% 1.2m\n", " 480 0.1807 23.2% 1.2m\n", " 490 0.1463 23.2% 1.2m\n", " 500 0.1538 23.2% 1.1m\n", " 510 0.1412 23.2% 1.1m\n", " 520 0.1785 23.2% 1.1m\n", " 530 0.1822 23.2% 1.0m\n", " 540 0.1778 23.2% 1.0m\n", " 550 0.1588 23.2% 1.0m\n", " 560 0.1402 23.2% 1.0m\n", " 570 0.1598 23.2% 0.9m\n", " 580 0.1919 23.2% 0.9m\n", " 590 0.1759 23.2% 0.9m\n", " 600 0.1662 23.2% 0.8m\n", " 610 0.1757 23.2% 0.8m\n", " 620 0.1586 23.2% 0.8m\n", " 630 0.1572 23.2% 0.7m\n", " 640 0.1682 23.2% 0.7m\n", " 650 0.1601 23.2% 0.7m\n", " 660 0.1304 23.2% 0.6m\n", " 670 0.1806 23.2% 0.6m\n", " 680 0.1610 23.2% 0.6m\n", " 690 0.1455 23.2% 0.5m\n", " 700 0.1971 23.2% 0.5m\n", " 710 0.1647 23.2% 0.5m\n", " 720 0.1346 23.2% 0.5m\n", " 730 0.1708 23.2% 0.4m\n", " 740 0.1622 23.2% 0.4m\n", " 750 0.1894 23.2% 0.4m\n", " 760 0.2017 23.2% 0.3m\n", " 770 0.1629 23.2% 0.3m\n", " 780 0.1676 23.2% 0.3m\n", " 790 0.1697 23.2% 0.2m\n", " 800 0.1447 23.2% 0.2m\n", " 810 0.1626 23.2% 0.2m\n", " 820 0.1593 23.2% 0.1m\n", " 830 0.1707 23.2% 0.1m\n", " 840 0.1193 23.2% 0.1m\n", " 850 0.1420 23.2% 0.0m\n", " 860 0.1883 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 69 COMPLETE in 163s\n", "\n", "โœ… EPOCH 69 SUMMARY\n", " โฑ๏ธ Time: 171s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16666 โ†’ Val=0.16400\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.8% Overall=81.3%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 88m\n", "\n", "๐Ÿ”„ Epoch 70/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1772 23.2% 2.7m\n", " 10 0.1727 23.2% 2.7m\n", " 20 0.2127 23.2% 2.6m\n", " 30 0.1893 23.2% 2.6m\n", " 40 0.1864 23.2% 2.6m\n", " 50 0.1829 23.2% 2.6m\n", " 60 0.1516 23.2% 2.5m\n", " 70 0.2114 23.2% 2.5m\n", " 80 0.1676 23.2% 2.5m\n", " 90 0.1705 23.2% 2.4m\n", " 100 0.1600 23.2% 2.4m\n", " 110 0.1754 23.2% 2.4m\n", " 120 0.1496 23.2% 2.3m\n", " 130 0.1826 23.2% 2.3m\n", " 140 0.1750 23.2% 2.3m\n", " 150 0.1690 23.2% 2.2m\n", " 160 0.1395 23.2% 2.2m\n", " 170 0.1547 23.2% 2.2m\n", " 180 0.1554 23.2% 2.1m\n", " 190 0.1321 23.2% 2.1m\n", " 200 0.1394 23.2% 2.1m\n", " 210 0.1790 23.2% 2.0m\n", " 220 0.1312 23.2% 2.0m\n", " 230 0.2099 23.2% 2.0m\n", " 240 0.1468 23.2% 2.0m\n", " 250 0.1931 23.2% 1.9m\n", " 260 0.1533 23.2% 1.9m\n", " 270 0.1478 23.2% 1.9m\n", " 280 0.1645 23.2% 1.8m\n", " 290 0.1651 23.2% 1.8m\n", " 300 0.1175 23.2% 1.8m\n", " 310 0.1908 23.2% 1.7m\n", " 320 0.1796 23.2% 1.7m\n", " 330 0.1892 23.2% 1.7m\n", " 340 0.1592 23.2% 1.6m\n", " 350 0.1908 23.2% 1.6m\n", " 360 0.1507 23.2% 1.6m\n", " 370 0.1371 23.2% 1.5m\n", " 380 0.1464 23.2% 1.5m\n", " 390 0.1449 23.2% 1.5m\n", " 400 0.1826 23.2% 1.4m\n", " 410 0.1636 23.2% 1.4m\n", " 420 0.1505 23.2% 1.4m\n", " 430 0.1808 23.2% 1.4m\n", " 440 0.1465 23.2% 1.3m\n", " 450 0.1740 23.2% 1.3m\n", " 460 0.2131 23.2% 1.3m\n", " 470 0.1766 23.2% 1.2m\n", " 480 0.1891 23.2% 1.2m\n", " 490 0.1359 23.2% 1.2m\n", " 500 0.1424 23.2% 1.1m\n", " 510 0.1754 23.2% 1.1m\n", " 520 0.1692 23.2% 1.1m\n", " 530 0.1675 23.2% 1.0m\n", " 540 0.1909 23.2% 1.0m\n", " 550 0.1602 23.2% 1.0m\n", " 560 0.2042 23.2% 0.9m\n", " 570 0.1363 23.2% 0.9m\n", " 580 0.1750 23.2% 0.9m\n", " 590 0.1717 23.2% 0.9m\n", " 600 0.1704 23.2% 0.8m\n", " 610 0.1351 23.2% 0.8m\n", " 620 0.1956 23.2% 0.8m\n", " 630 0.1864 23.2% 0.7m\n", " 640 0.1582 23.2% 0.7m\n", " 650 0.1676 23.2% 0.7m\n", " 660 0.1489 23.2% 0.6m\n", " 670 0.1487 23.2% 0.6m\n", " 680 0.1525 23.2% 0.6m\n", " 690 0.1558 23.2% 0.5m\n", " 700 0.1555 23.2% 0.5m\n", " 710 0.1942 23.2% 0.5m\n", " 720 0.1423 23.2% 0.4m\n", " 730 0.1758 23.2% 0.4m\n", " 740 0.1605 23.2% 0.4m\n", " 750 0.1931 23.2% 0.4m\n", " 760 0.1722 23.2% 0.3m\n", " 770 0.1650 23.2% 0.3m\n", " 780 0.1626 23.2% 0.3m\n", " 790 0.2032 23.2% 0.2m\n", " 800 0.1462 23.2% 0.2m\n", " 810 0.1535 23.2% 0.2m\n", " 820 0.1722 23.2% 0.1m\n", " 830 0.1745 23.2% 0.1m\n", " 840 0.1530 23.2% 0.1m\n", " 850 0.1818 23.2% 0.0m\n", " 860 0.1733 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 70 COMPLETE in 162s\n", "\n", "โœ… EPOCH 70 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16652 โ†’ Val=0.16387\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.5% EJ=86.7% Overall=81.3%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 85m\n", "\n", "๐Ÿ”„ Epoch 71/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1770 23.2% 2.6m\n", " 10 0.1651 23.2% 2.6m\n", " 20 0.1470 23.2% 2.7m\n", " 30 0.1716 23.2% 2.6m\n", " 40 0.1622 23.2% 2.6m\n", " 50 0.1921 23.2% 2.6m\n", " 60 0.1488 23.2% 2.5m\n", " 70 0.1850 23.2% 2.5m\n", " 80 0.1413 23.2% 2.5m\n", " 90 0.1767 23.2% 2.4m\n", " 100 0.1410 23.2% 2.4m\n", " 110 0.1748 23.2% 2.4m\n", " 120 0.1473 23.2% 2.3m\n", " 130 0.1441 23.2% 2.3m\n", " 140 0.1386 23.2% 2.3m\n", " 150 0.1584 23.2% 2.2m\n", " 160 0.1289 23.2% 2.2m\n", " 170 0.1383 23.2% 2.2m\n", " 180 0.1929 23.2% 2.1m\n", " 190 0.1520 23.2% 2.1m\n", " 200 0.1417 23.2% 2.1m\n", " 210 0.1717 23.2% 2.0m\n", " 220 0.1452 23.2% 2.0m\n", " 230 0.1795 23.2% 2.0m\n", " 240 0.1993 23.2% 2.0m\n", " 250 0.1933 23.2% 1.9m\n", " 260 0.2023 23.2% 1.9m\n", " 270 0.1696 23.2% 1.9m\n", " 280 0.1623 23.2% 1.8m\n", " 290 0.1796 23.2% 1.8m\n", " 300 0.1970 23.2% 1.8m\n", " 310 0.1595 23.2% 1.7m\n", " 320 0.1862 23.2% 1.7m\n", " 330 0.1697 23.2% 1.7m\n", " 340 0.2134 23.2% 1.7m\n", " 350 0.1801 23.2% 1.6m\n", " 360 0.1746 23.2% 1.6m\n", " 370 0.2078 23.2% 1.6m\n", " 380 0.1512 23.2% 1.5m\n", " 390 0.1730 23.2% 1.5m\n", " 400 0.1400 23.2% 1.5m\n", " 410 0.1850 23.2% 1.4m\n", " 420 0.1903 23.2% 1.4m\n", " 430 0.1496 23.2% 1.4m\n", " 440 0.1723 23.2% 1.3m\n", " 450 0.2130 23.2% 1.3m\n", " 460 0.1500 23.2% 1.3m\n", " 470 0.1317 23.2% 1.2m\n", " 480 0.1608 23.2% 1.2m\n", " 490 0.1797 23.2% 1.2m\n", " 500 0.2055 23.2% 1.1m\n", " 510 0.1529 23.2% 1.1m\n", " 520 0.1802 23.2% 1.1m\n", " 530 0.1704 23.2% 1.1m\n", " 540 0.1735 23.2% 1.0m\n", " 550 0.1674 23.2% 1.0m\n", " 560 0.1982 23.2% 1.0m\n", " 570 0.1699 23.2% 0.9m\n", " 580 0.1377 23.2% 0.9m\n", " 590 0.1854 23.2% 0.9m\n", " 600 0.1437 23.2% 0.8m\n", " 610 0.2036 23.2% 0.8m\n", " 620 0.1487 23.2% 0.8m\n", " 630 0.1651 23.2% 0.7m\n", " 640 0.1419 23.2% 0.7m\n", " 650 0.1774 23.2% 0.7m\n", " 660 0.1664 23.2% 0.6m\n", " 670 0.1366 23.2% 0.6m\n", " 680 0.1462 23.2% 0.6m\n", " 690 0.1484 23.2% 0.5m\n", " 700 0.1905 23.2% 0.5m\n", " 710 0.1560 23.2% 0.5m\n", " 720 0.1417 23.2% 0.5m\n", " 730 0.1550 23.2% 0.4m\n", " 740 0.1843 23.2% 0.4m\n", " 750 0.1680 23.2% 0.4m\n", " 760 0.1700 23.2% 0.3m\n", " 770 0.1999 23.2% 0.3m\n", " 780 0.1984 23.2% 0.3m\n", " 790 0.1558 23.2% 0.2m\n", " 800 0.1708 23.2% 0.2m\n", " 810 0.1624 23.2% 0.2m\n", " 820 0.1615 23.2% 0.1m\n", " 830 0.1293 23.2% 0.1m\n", " 840 0.1399 23.2% 0.1m\n", " 850 0.1609 23.2% 0.0m\n", " 860 0.1773 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 71 COMPLETE in 163s\n", "\n", "โœ… EPOCH 71 SUMMARY\n", " โฑ๏ธ Time: 171s (2.9m)\n", " ๐Ÿ“‰ Loss: Train=0.16663 โ†’ Val=0.16383\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.5% EJ=86.7% Overall=81.3%\n", " โš ๏ธ No improvement for 10/30\n", " โฑ๏ธ Remaining ETA โ‰ˆ 83m\n", "\n", "๐Ÿ”„ Epoch 72/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1460 23.2% 2.7m\n", " 10 0.1374 23.2% 2.7m\n", " 20 0.1602 23.2% 2.7m\n", " 30 0.1898 23.2% 2.6m\n", " 40 0.2084 23.2% 2.6m\n", " 50 0.1346 23.2% 2.6m\n", " 60 0.1593 23.2% 2.5m\n", " 70 0.1589 23.2% 2.5m\n", " 80 0.1585 23.2% 2.5m\n", " 90 0.2105 23.2% 2.4m\n", " 100 0.1581 23.2% 2.4m\n", " 110 0.1958 23.2% 2.4m\n", " 120 0.1698 23.2% 2.3m\n", " 130 0.1708 23.2% 2.3m\n", " 140 0.1737 23.2% 2.3m\n", " 150 0.1876 23.2% 2.2m\n", " 160 0.1612 23.2% 2.2m\n", " 170 0.1680 23.2% 2.2m\n", " 180 0.1490 23.2% 2.2m\n", " 190 0.1500 23.2% 2.1m\n", " 200 0.1962 23.2% 2.1m\n", " 210 0.1407 23.2% 2.1m\n", " 220 0.2218 23.2% 2.0m\n", " 230 0.1766 23.2% 2.0m\n", " 240 0.1820 23.2% 2.0m\n", " 250 0.1661 23.2% 1.9m\n", " 260 0.1481 23.2% 1.9m\n", " 270 0.1739 23.2% 1.9m\n", " 280 0.1648 23.2% 1.8m\n", " 290 0.1528 23.2% 1.8m\n", " 300 0.1370 23.2% 1.8m\n", " 310 0.1865 23.2% 1.7m\n", " 320 0.1654 23.2% 1.7m\n", " 330 0.1512 23.2% 1.7m\n", " 340 0.1600 23.2% 1.7m\n", " 350 0.1776 23.2% 1.6m\n", " 360 0.1983 23.2% 1.6m\n", " 370 0.1520 23.2% 1.6m\n", " 380 0.1932 23.2% 1.5m\n", " 390 0.1351 23.2% 1.5m\n", " 400 0.1469 23.2% 1.5m\n", " 410 0.1897 23.2% 1.4m\n", " 420 0.1552 23.2% 1.4m\n", " 430 0.1816 23.2% 1.4m\n", " 440 0.1582 23.2% 1.3m\n", " 450 0.1392 23.2% 1.3m\n", " 460 0.1631 23.2% 1.3m\n", " 470 0.2059 23.2% 1.2m\n", " 480 0.1914 23.2% 1.2m\n", " 490 0.1350 23.2% 1.2m\n", " 500 0.1901 23.2% 1.1m\n", " 510 0.1862 23.2% 1.1m\n", " 520 0.1740 23.2% 1.1m\n", " 530 0.1892 23.2% 1.1m\n", " 540 0.1656 23.2% 1.0m\n", " 550 0.1644 23.2% 1.0m\n", " 560 0.1158 23.2% 1.0m\n", " 570 0.1543 23.2% 0.9m\n", " 580 0.1824 23.2% 0.9m\n", " 590 0.1692 23.2% 0.9m\n", " 600 0.1668 23.2% 0.8m\n", " 610 0.1778 23.2% 0.8m\n", " 620 0.2197 23.2% 0.8m\n", " 630 0.1731 23.2% 0.7m\n", " 640 0.1330 23.2% 0.7m\n", " 650 0.1994 23.2% 0.7m\n", " 660 0.1980 23.2% 0.6m\n", " 670 0.1741 23.2% 0.6m\n", " 680 0.1669 23.2% 0.6m\n", " 690 0.1704 23.2% 0.5m\n", " 700 0.1280 23.2% 0.5m\n", " 710 0.1460 23.2% 0.5m\n", " 720 0.1382 23.2% 0.5m\n", " 730 0.1461 23.2% 0.4m\n", " 740 0.1564 23.2% 0.4m\n", " 750 0.1463 23.2% 0.4m\n", " 760 0.1633 23.2% 0.3m\n", " 770 0.2151 23.2% 0.3m\n", " 780 0.1613 23.2% 0.3m\n", " 790 0.1315 23.2% 0.2m\n", " 800 0.1729 23.2% 0.2m\n", " 810 0.1232 23.2% 0.2m\n", " 820 0.1823 23.2% 0.1m\n", " 830 0.1615 23.2% 0.1m\n", " 840 0.1443 23.2% 0.1m\n", " 850 0.1931 23.2% 0.0m\n", " 860 0.1549 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 72 COMPLETE in 164s\n", "\n", "โœ… EPOCH 72 SUMMARY\n", " โฑ๏ธ Time: 172s (2.9m)\n", " ๐Ÿ“‰ Loss: Train=0.16644 โ†’ Val=0.16399\n", " ๐Ÿ“Š Acc: EC=81.5% EL=75.5% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 80m\n", "\n", "๐Ÿ”„ Epoch 73/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1530 23.2% 2.6m\n", " 10 0.1559 23.2% 2.8m\n", " 20 0.1547 23.2% 2.7m\n", " 30 0.1237 23.2% 2.7m\n", " 40 0.1712 23.2% 2.6m\n", " 50 0.1759 23.2% 2.6m\n", " 60 0.1792 23.2% 2.6m\n", " 70 0.1742 23.2% 2.5m\n", " 80 0.1645 23.2% 2.5m\n", " 90 0.1537 23.2% 2.5m\n", " 100 0.1711 23.2% 2.4m\n", " 110 0.1771 23.2% 2.4m\n", " 120 0.1572 23.2% 2.3m\n", " 130 0.1916 23.2% 2.3m\n", " 140 0.2037 23.2% 2.3m\n", " 150 0.1558 23.2% 2.3m\n", " 160 0.1598 23.2% 2.2m\n", " 170 0.1565 23.2% 2.2m\n", " 180 0.1490 23.2% 2.2m\n", " 190 0.1685 23.2% 2.1m\n", " 200 0.1625 23.2% 2.1m\n", " 210 0.2056 23.2% 2.1m\n", " 220 0.1657 23.2% 2.0m\n", " 230 0.1371 23.2% 2.0m\n", " 240 0.1641 23.2% 2.0m\n", " 250 0.1665 23.2% 1.9m\n", " 260 0.1719 23.2% 1.9m\n", " 270 0.2004 23.2% 1.9m\n", " 280 0.1347 23.2% 1.8m\n", " 290 0.1733 23.2% 1.8m\n", " 300 0.1918 23.2% 1.8m\n", " 310 0.1789 23.2% 1.8m\n", " 320 0.1862 23.2% 1.7m\n", " 330 0.1564 23.2% 1.7m\n", " 340 0.1835 23.2% 1.7m\n", " 350 0.1505 23.2% 1.6m\n", " 360 0.1702 23.2% 1.6m\n", " 370 0.1786 23.2% 1.6m\n", " 380 0.1897 23.2% 1.5m\n", " 390 0.1708 23.2% 1.5m\n", " 400 0.1784 23.2% 1.5m\n", " 410 0.1777 23.2% 1.4m\n", " 420 0.1348 23.2% 1.4m\n", " 430 0.1413 23.2% 1.4m\n", " 440 0.2023 23.2% 1.3m\n", " 450 0.1982 23.2% 1.3m\n", " 460 0.1876 23.2% 1.3m\n", " 470 0.1432 23.2% 1.2m\n", " 480 0.1781 23.2% 1.2m\n", " 490 0.1242 23.2% 1.2m\n", " 500 0.1469 23.2% 1.1m\n", " 510 0.1604 23.2% 1.1m\n", " 520 0.1704 23.2% 1.1m\n", " 530 0.1165 23.2% 1.1m\n", " 540 0.1727 23.2% 1.0m\n", " 550 0.1307 23.2% 1.0m\n", " 560 0.1442 23.2% 1.0m\n", " 570 0.1827 23.2% 0.9m\n", " 580 0.1624 23.2% 0.9m\n", " 590 0.1583 23.2% 0.9m\n", " 600 0.1422 23.2% 0.8m\n", " 610 0.1233 23.2% 0.8m\n", " 620 0.1532 23.2% 0.8m\n", " 630 0.1638 23.2% 0.7m\n", " 640 0.1763 23.2% 0.7m\n", " 650 0.1746 23.2% 0.7m\n", " 660 0.1553 23.2% 0.6m\n", " 670 0.1659 23.2% 0.6m\n", " 680 0.1887 23.2% 0.6m\n", " 690 0.2282 23.2% 0.5m\n", " 700 0.1568 23.2% 0.5m\n", " 710 0.1874 23.2% 0.5m\n", " 720 0.1491 23.2% 0.5m\n", " 730 0.1502 23.2% 0.4m\n", " 740 0.1637 23.2% 0.4m\n", " 750 0.1711 23.2% 0.4m\n", " 760 0.2078 23.2% 0.3m\n", " 770 0.1662 23.2% 0.3m\n", " 780 0.1394 23.2% 0.3m\n", " 790 0.1751 23.2% 0.2m\n", " 800 0.1679 23.2% 0.2m\n", " 810 0.1904 23.2% 0.2m\n", " 820 0.1514 23.2% 0.1m\n", " 830 0.1584 23.2% 0.1m\n", " 840 0.1403 23.2% 0.1m\n", " 850 0.1833 23.2% 0.0m\n", " 860 0.1472 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 73 COMPLETE in 163s\n", "\n", "โœ… EPOCH 73 SUMMARY\n", " โฑ๏ธ Time: 172s (2.9m)\n", " ๐Ÿ“‰ Loss: Train=0.16660 โ†’ Val=0.16385\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.5% EJ=86.7% Overall=81.3%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 77m\n", "\n", "๐Ÿ”„ Epoch 74/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1854 23.2% 2.7m\n", " 10 0.1937 23.2% 2.7m\n", " 20 0.1798 23.2% 2.6m\n", " 30 0.1679 23.2% 2.6m\n", " 40 0.1565 23.2% 2.6m\n", " 50 0.1292 23.2% 2.5m\n", " 60 0.1664 23.2% 2.5m\n", " 70 0.1861 23.2% 2.5m\n", " 80 0.1593 23.2% 2.5m\n", " 90 0.1319 23.2% 2.4m\n", " 100 0.1464 23.2% 2.4m\n", " 110 0.1870 23.2% 2.4m\n", " 120 0.1893 23.2% 2.3m\n", " 130 0.1982 23.2% 2.3m\n", " 140 0.1733 23.2% 2.3m\n", " 150 0.1295 23.2% 2.3m\n", " 160 0.1558 23.2% 2.2m\n", " 170 0.1303 23.2% 2.2m\n", " 180 0.1430 23.2% 2.2m\n", " 190 0.2001 23.2% 2.1m\n", " 200 0.1753 23.2% 2.1m\n", " 210 0.1839 23.2% 2.1m\n", " 220 0.1769 23.2% 2.0m\n", " 230 0.1567 23.2% 2.0m\n", " 240 0.1864 23.2% 2.0m\n", " 250 0.1599 23.2% 1.9m\n", " 260 0.1187 23.2% 1.9m\n", " 270 0.1408 23.2% 1.9m\n", " 280 0.1633 23.2% 1.8m\n", " 290 0.2154 23.2% 1.8m\n", " 300 0.1733 23.2% 1.8m\n", " 310 0.1547 23.2% 1.8m\n", " 320 0.1498 23.2% 1.7m\n", " 330 0.1402 23.2% 1.7m\n", " 340 0.2145 23.2% 1.7m\n", " 350 0.1639 23.2% 1.6m\n", " 360 0.1718 23.2% 1.6m\n", " 370 0.2012 23.2% 1.6m\n", " 380 0.1581 23.2% 1.5m\n", " 390 0.1540 23.2% 1.5m\n", " 400 0.1331 23.2% 1.5m\n", " 410 0.1885 23.2% 1.4m\n", " 420 0.1698 23.2% 1.4m\n", " 430 0.1838 23.2% 1.4m\n", " 440 0.1537 23.2% 1.3m\n", " 450 0.1739 23.2% 1.3m\n", " 460 0.1859 23.2% 1.3m\n", " 470 0.1400 23.2% 1.2m\n", " 480 0.1172 23.2% 1.2m\n", " 490 0.1438 23.2% 1.2m\n", " 500 0.1836 23.2% 1.1m\n", " 510 0.1814 23.2% 1.1m\n", " 520 0.1659 23.2% 1.1m\n", " 530 0.1464 23.2% 1.1m\n", " 540 0.1705 23.2% 1.0m\n", " 550 0.1791 23.2% 1.0m\n", " 560 0.1514 23.2% 1.0m\n", " 570 0.1653 23.2% 0.9m\n", " 580 0.1639 23.2% 0.9m\n", " 590 0.1686 23.2% 0.9m\n", " 600 0.1560 23.2% 0.8m\n", " 610 0.1677 23.2% 0.8m\n", " 620 0.2097 23.2% 0.8m\n", " 630 0.1249 23.2% 0.7m\n", " 640 0.1435 23.2% 0.7m\n", " 650 0.1592 23.2% 0.7m\n", " 660 0.1681 23.2% 0.6m\n", " 670 0.1855 23.2% 0.6m\n", " 680 0.1950 23.2% 0.6m\n", " 690 0.2005 23.2% 0.5m\n", " 700 0.1628 23.2% 0.5m\n", " 710 0.1648 23.2% 0.5m\n", " 720 0.1939 23.2% 0.5m\n", " 730 0.1538 23.2% 0.4m\n", " 740 0.1840 23.2% 0.4m\n", " 750 0.1282 23.2% 0.4m\n", " 760 0.1757 23.2% 0.3m\n", " 770 0.1278 23.2% 0.3m\n", " 780 0.1672 23.2% 0.3m\n", " 790 0.1691 23.2% 0.2m\n", " 800 0.1617 23.2% 0.2m\n", " 810 0.1839 23.2% 0.2m\n", " 820 0.1521 23.2% 0.1m\n", " 830 0.1359 23.2% 0.1m\n", " 840 0.1768 23.2% 0.1m\n", " 850 0.1586 23.2% 0.0m\n", " 860 0.1807 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 74 COMPLETE in 164s\n", "\n", "โœ… EPOCH 74 SUMMARY\n", " โฑ๏ธ Time: 172s (2.9m)\n", " ๐Ÿ“‰ Loss: Train=0.16643 โ†’ Val=0.16398\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.5% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 74m\n", "\n", "๐Ÿ”„ Epoch 75/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1348 23.2% 2.8m\n", " 10 0.1661 23.2% 2.6m\n", " 20 0.1641 23.2% 2.6m\n", " 30 0.1605 23.2% 2.6m\n", " 40 0.1486 23.2% 2.6m\n", " 50 0.1560 23.2% 2.5m\n", " 60 0.2036 23.2% 2.5m\n", " 70 0.1772 23.2% 2.5m\n", " 80 0.1519 23.2% 2.4m\n", " 90 0.1897 23.2% 2.4m\n", " 100 0.1337 23.2% 2.4m\n", " 110 0.1510 23.2% 2.4m\n", " 120 0.1208 23.2% 2.3m\n", " 130 0.1406 23.2% 2.3m\n", " 140 0.1727 23.2% 2.3m\n", " 150 0.1663 23.2% 2.2m\n", " 160 0.1448 23.2% 2.2m\n", " 170 0.1451 23.2% 2.2m\n", " 180 0.1745 23.2% 2.1m\n", " 190 0.1352 23.2% 2.1m\n", " 200 0.1599 23.2% 2.1m\n", " 210 0.2069 23.2% 2.0m\n", " 220 0.1652 23.2% 2.0m\n", " 230 0.1898 23.2% 2.0m\n", " 240 0.1564 23.2% 1.9m\n", " 250 0.1997 23.2% 1.9m\n", " 260 0.1717 23.2% 1.9m\n", " 270 0.1949 23.2% 1.9m\n", " 280 0.1976 23.2% 1.8m\n", " 290 0.1963 23.2% 1.8m\n", " 300 0.1496 23.2% 1.8m\n", " 310 0.1628 23.2% 1.7m\n", " 320 0.2011 23.2% 1.7m\n", " 330 0.1705 23.2% 1.7m\n", " 340 0.1339 23.2% 1.6m\n", " 350 0.1087 23.2% 1.6m\n", " 360 0.1618 23.2% 1.6m\n", " 370 0.1727 23.2% 1.6m\n", " 380 0.2023 23.2% 1.5m\n", " 390 0.1783 23.2% 1.5m\n", " 400 0.1583 23.2% 1.5m\n", " 410 0.1453 23.2% 1.4m\n", " 420 0.1703 23.2% 1.4m\n", " 430 0.1885 23.2% 1.4m\n", " 440 0.1643 23.2% 1.3m\n", " 450 0.1600 23.2% 1.3m\n", " 460 0.2062 23.2% 1.3m\n", " 470 0.1724 23.2% 1.2m\n", " 480 0.1652 23.2% 1.2m\n", " 490 0.1844 23.2% 1.2m\n", " 500 0.1716 23.2% 1.1m\n", " 510 0.1437 23.2% 1.1m\n", " 520 0.1692 23.2% 1.1m\n", " 530 0.1661 23.2% 1.0m\n", " 540 0.1662 23.2% 1.0m\n", " 550 0.1950 23.2% 1.0m\n", " 560 0.1593 23.2% 1.0m\n", " 570 0.1964 23.2% 0.9m\n", " 580 0.1304 23.2% 0.9m\n", " 590 0.1669 23.2% 0.9m\n", " 600 0.1820 23.2% 0.8m\n", " 610 0.1414 23.2% 0.8m\n", " 620 0.1449 23.2% 0.8m\n", " 630 0.1507 23.2% 0.7m\n", " 640 0.1519 23.2% 0.7m\n", " 650 0.1707 23.2% 0.7m\n", " 660 0.1791 23.2% 0.6m\n", " 670 0.1912 23.2% 0.6m\n", " 680 0.1780 23.2% 0.6m\n", " 690 0.1987 23.2% 0.5m\n", " 700 0.1729 23.2% 0.5m\n", " 710 0.1531 23.2% 0.5m\n", " 720 0.1278 23.2% 0.4m\n", " 730 0.1589 23.2% 0.4m\n", " 740 0.1716 23.2% 0.4m\n", " 750 0.1807 23.2% 0.4m\n", " 760 0.1420 23.2% 0.3m\n", " 770 0.1814 23.2% 0.3m\n", " 780 0.1421 23.2% 0.3m\n", " 790 0.1812 23.2% 0.2m\n", " 800 0.1553 23.2% 0.2m\n", " 810 0.1572 23.2% 0.2m\n", " 820 0.1356 23.2% 0.1m\n", " 830 0.1953 23.2% 0.1m\n", " 840 0.1615 23.2% 0.1m\n", " 850 0.1806 23.2% 0.0m\n", " 860 0.1711 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 75 COMPLETE in 162s\n", "\n", "โœ… EPOCH 75 SUMMARY\n", " โฑ๏ธ Time: 171s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16651 โ†’ Val=0.16383\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.5% EJ=86.7% Overall=81.3%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 71m\n", "\n", "๐Ÿ”„ Epoch 76/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.2037 23.2% 2.7m\n", " 10 0.1669 23.2% 2.7m\n", " 20 0.1869 23.2% 2.6m\n", " 30 0.1143 23.2% 2.6m\n", " 40 0.1668 23.2% 2.6m\n", " 50 0.1278 23.2% 2.5m\n", " 60 0.2015 23.2% 2.5m\n", " 70 0.1422 23.2% 2.5m\n", " 80 0.1795 23.2% 2.4m\n", " 90 0.1566 23.2% 2.4m\n", " 100 0.1378 23.2% 2.4m\n", " 110 0.1734 23.2% 2.4m\n", " 120 0.1332 23.2% 2.3m\n", " 130 0.1560 23.2% 2.3m\n", " 140 0.1951 23.2% 2.3m\n", " 150 0.1609 23.2% 2.2m\n", " 160 0.1549 23.2% 2.2m\n", " 170 0.1579 23.2% 2.2m\n", " 180 0.1751 23.2% 2.1m\n", " 190 0.1469 23.2% 2.1m\n", " 200 0.1397 23.2% 2.1m\n", " 210 0.1311 23.2% 2.0m\n", " 220 0.1873 23.2% 2.0m\n", " 230 0.1644 23.2% 2.0m\n", " 240 0.1896 23.2% 1.9m\n", " 250 0.1793 23.2% 1.9m\n", " 260 0.1711 23.2% 1.9m\n", " 270 0.1580 23.2% 1.9m\n", " 280 0.1691 23.2% 1.8m\n", " 290 0.1373 23.2% 1.8m\n", " 300 0.1553 23.2% 1.8m\n", " 310 0.1233 23.2% 1.7m\n", " 320 0.1334 23.2% 1.7m\n", " 330 0.1715 23.2% 1.7m\n", " 340 0.1669 23.2% 1.6m\n", " 350 0.1756 23.2% 1.6m\n", " 360 0.1686 23.2% 1.6m\n", " 370 0.1876 23.2% 1.5m\n", " 380 0.1714 23.2% 1.5m\n", " 390 0.1665 23.2% 1.5m\n", " 400 0.1606 23.2% 1.4m\n", " 410 0.1346 23.2% 1.4m\n", " 420 0.1988 23.2% 1.4m\n", " 430 0.1508 23.2% 1.4m\n", " 440 0.1486 23.2% 1.3m\n", " 450 0.1521 23.2% 1.3m\n", " 460 0.1230 23.2% 1.3m\n", " 470 0.1558 23.2% 1.2m\n", " 480 0.1801 23.2% 1.2m\n", " 490 0.1715 23.2% 1.2m\n", " 500 0.1881 23.2% 1.1m\n", " 510 0.1775 23.2% 1.1m\n", " 520 0.1811 23.2% 1.1m\n", " 530 0.1591 23.2% 1.0m\n", " 540 0.1756 23.2% 1.0m\n", " 550 0.1557 23.2% 1.0m\n", " 560 0.1532 23.2% 0.9m\n", " 570 0.1360 23.2% 0.9m\n", " 580 0.1578 23.2% 0.9m\n", " 590 0.1953 23.2% 0.9m\n", " 600 0.1547 23.2% 0.8m\n", " 610 0.1636 23.2% 0.8m\n", " 620 0.1919 23.2% 0.8m\n", " 630 0.1796 23.2% 0.7m\n", " 640 0.1479 23.2% 0.7m\n", " 650 0.1487 23.2% 0.7m\n", " 660 0.1799 23.2% 0.6m\n", " 670 0.1561 23.2% 0.6m\n", " 680 0.1759 23.2% 0.6m\n", " 690 0.1659 23.2% 0.5m\n", " 700 0.1814 23.2% 0.5m\n", " 710 0.1783 23.2% 0.5m\n", " 720 0.1875 23.2% 0.4m\n", " 730 0.1580 23.2% 0.4m\n", " 740 0.1896 23.2% 0.4m\n", " 750 0.1487 23.2% 0.4m\n", " 760 0.1709 23.2% 0.3m\n", " 770 0.1853 23.2% 0.3m\n", " 780 0.1658 23.2% 0.3m\n", " 790 0.1385 23.2% 0.2m\n", " 800 0.1845 23.2% 0.2m\n", " 810 0.1262 23.2% 0.2m\n", " 820 0.1838 23.2% 0.1m\n", " 830 0.1604 23.2% 0.1m\n", " 840 0.1177 23.2% 0.1m\n", " 850 0.2075 23.2% 0.0m\n", " 860 0.1993 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 76 COMPLETE in 162s\n", "\n", "โœ… EPOCH 76 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16653 โ†’ Val=0.16405\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.2%\n", " โš ๏ธ No improvement for 15/30\n", " โฑ๏ธ Remaining ETA โ‰ˆ 68m\n", "\n", "๐Ÿ”„ Epoch 77/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1798 23.2% 2.7m\n", " 10 0.1751 23.2% 2.6m\n", " 20 0.1804 23.2% 2.6m\n", " 30 0.2148 23.2% 2.6m\n", " 40 0.1326 23.2% 2.6m\n", " 50 0.1738 23.2% 2.5m\n", " 60 0.1900 23.2% 2.5m\n", " 70 0.1890 23.2% 2.5m\n", " 80 0.1439 23.2% 2.4m\n", " 90 0.1755 23.2% 2.4m\n", " 100 0.1515 23.2% 2.4m\n", " 110 0.1823 23.2% 2.4m\n", " 120 0.1975 23.2% 2.3m\n", " 130 0.1786 23.2% 2.3m\n", " 140 0.1462 23.2% 2.3m\n", " 150 0.1516 23.2% 2.2m\n", " 160 0.1807 23.2% 2.2m\n", " 170 0.1990 23.2% 2.2m\n", " 180 0.1732 23.2% 2.1m\n", " 190 0.1819 23.2% 2.1m\n", " 200 0.1429 23.2% 2.1m\n", " 210 0.1629 23.2% 2.0m\n", " 220 0.1357 23.2% 2.0m\n", " 230 0.1663 23.2% 2.0m\n", " 240 0.1919 23.2% 1.9m\n", " 250 0.1703 23.2% 1.9m\n", " 260 0.1364 23.2% 1.9m\n", " 270 0.1414 23.2% 1.9m\n", " 280 0.1764 23.2% 1.8m\n", " 290 0.1405 23.2% 1.8m\n", " 300 0.1938 23.2% 1.8m\n", " 310 0.1808 23.2% 1.7m\n", " 320 0.1786 23.2% 1.7m\n", " 330 0.1984 23.2% 1.7m\n", " 340 0.1628 23.2% 1.6m\n", " 350 0.1894 23.2% 1.6m\n", " 360 0.1567 23.2% 1.6m\n", " 370 0.1705 23.2% 1.5m\n", " 380 0.1451 23.2% 1.5m\n", " 390 0.1432 23.2% 1.5m\n", " 400 0.1674 23.2% 1.4m\n", " 410 0.1528 23.2% 1.4m\n", " 420 0.1455 23.2% 1.4m\n", " 430 0.1896 23.2% 1.4m\n", " 440 0.1353 23.2% 1.3m\n", " 450 0.1626 23.2% 1.3m\n", " 460 0.1281 23.2% 1.3m\n", " 470 0.1764 23.2% 1.2m\n", " 480 0.1484 23.2% 1.2m\n", " 490 0.1847 23.2% 1.2m\n", " 500 0.1536 23.2% 1.1m\n", " 510 0.1771 23.2% 1.1m\n", " 520 0.1886 23.2% 1.1m\n", " 530 0.2017 23.2% 1.0m\n", " 540 0.1902 23.2% 1.0m\n", " 550 0.1580 23.2% 1.0m\n", " 560 0.1302 23.2% 0.9m\n", " 570 0.1989 23.2% 0.9m\n", " 580 0.1744 23.2% 0.9m\n", " 590 0.1519 23.2% 0.9m\n", " 600 0.1923 23.2% 0.8m\n", " 610 0.1786 23.2% 0.8m\n", " 620 0.1568 23.2% 0.8m\n", " 630 0.1961 23.2% 0.7m\n", " 640 0.1428 23.2% 0.7m\n", " 650 0.1798 23.2% 0.7m\n", " 660 0.1342 23.2% 0.6m\n", " 670 0.1386 23.2% 0.6m\n", " 680 0.1494 23.2% 0.6m\n", " 690 0.2066 23.2% 0.5m\n", " 700 0.2313 23.2% 0.5m\n", " 710 0.1557 23.2% 0.5m\n", " 720 0.1199 23.2% 0.4m\n", " 730 0.1434 23.2% 0.4m\n", " 740 0.1493 23.2% 0.4m\n", " 750 0.1681 23.2% 0.4m\n", " 760 0.2189 23.2% 0.3m\n", " 770 0.1352 23.2% 0.3m\n", " 780 0.1227 23.2% 0.3m\n", " 790 0.1951 23.2% 0.2m\n", " 800 0.1770 23.2% 0.2m\n", " 810 0.1724 23.2% 0.2m\n", " 820 0.1819 23.2% 0.1m\n", " 830 0.1714 23.2% 0.1m\n", " 840 0.1627 23.2% 0.1m\n", " 850 0.1381 23.2% 0.0m\n", " 860 0.1486 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 77 COMPLETE in 162s\n", "\n", "โœ… EPOCH 77 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16662 โ†’ Val=0.16388\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.5% EJ=86.7% Overall=81.3%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 65m\n", "\n", "๐Ÿ”„ Epoch 78/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1824 23.2% 2.7m\n", " 10 0.1967 23.2% 2.7m\n", " 20 0.1647 23.2% 2.6m\n", " 30 0.2025 23.2% 2.6m\n", " 40 0.1466 23.2% 2.6m\n", " 50 0.1983 23.2% 2.5m\n", " 60 0.1946 23.2% 2.5m\n", " 70 0.1583 23.2% 2.5m\n", " 80 0.1624 23.2% 2.4m\n", " 90 0.1833 23.2% 2.4m\n", " 100 0.1712 23.2% 2.4m\n", " 110 0.1471 23.2% 2.3m\n", " 120 0.1741 23.2% 2.3m\n", " 130 0.1675 23.2% 2.3m\n", " 140 0.1823 23.2% 2.3m\n", " 150 0.1681 23.2% 2.2m\n", " 160 0.1104 23.2% 2.2m\n", " 170 0.1678 23.2% 2.2m\n", " 180 0.1787 23.2% 2.1m\n", " 190 0.1473 23.2% 2.1m\n", " 200 0.1752 23.2% 2.1m\n", " 210 0.1301 23.2% 2.0m\n", " 220 0.1538 23.2% 2.0m\n", " 230 0.1710 23.2% 2.0m\n", " 240 0.1476 23.2% 1.9m\n", " 250 0.1587 23.2% 1.9m\n", " 260 0.1672 23.2% 1.9m\n", " 270 0.1664 23.2% 1.9m\n", " 280 0.1546 23.2% 1.8m\n", " 290 0.1344 23.2% 1.8m\n", " 300 0.1911 23.2% 1.8m\n", " 310 0.1652 23.2% 1.7m\n", " 320 0.2135 23.2% 1.7m\n", " 330 0.1640 23.2% 1.7m\n", " 340 0.1398 23.2% 1.6m\n", " 350 0.1833 23.2% 1.6m\n", " 360 0.1926 23.2% 1.6m\n", " 370 0.1547 23.2% 1.5m\n", " 380 0.2029 23.2% 1.5m\n", " 390 0.1787 23.2% 1.5m\n", " 400 0.1685 23.2% 1.4m\n", " 410 0.1120 23.2% 1.4m\n", " 420 0.1796 23.2% 1.4m\n", " 430 0.1672 23.2% 1.4m\n", " 440 0.1863 23.2% 1.3m\n", " 450 0.1562 23.2% 1.3m\n", " 460 0.1580 23.2% 1.3m\n", " 470 0.1322 23.2% 1.2m\n", " 480 0.1613 23.2% 1.2m\n", " 490 0.1461 23.2% 1.2m\n", " 500 0.1406 23.2% 1.1m\n", " 510 0.1466 23.2% 1.1m\n", " 520 0.1741 23.2% 1.1m\n", " 530 0.1856 23.2% 1.0m\n", " 540 0.1829 23.2% 1.0m\n", " 550 0.1706 23.2% 1.0m\n", " 560 0.1835 23.2% 0.9m\n", " 570 0.1644 23.2% 0.9m\n", " 580 0.1788 23.2% 0.9m\n", " 590 0.1676 23.2% 0.9m\n", " 600 0.1545 23.2% 0.8m\n", " 610 0.1873 23.2% 0.8m\n", " 620 0.2365 23.2% 0.8m\n", " 630 0.1748 23.2% 0.7m\n", " 640 0.1594 23.2% 0.7m\n", " 650 0.1771 23.2% 0.7m\n", " 660 0.1478 23.2% 0.6m\n", " 670 0.1662 23.2% 0.6m\n", " 680 0.1862 23.2% 0.6m\n", " 690 0.1618 23.2% 0.5m\n", " 700 0.1991 23.2% 0.5m\n", " 710 0.1905 23.2% 0.5m\n", " 720 0.1609 23.2% 0.4m\n", " 730 0.1463 23.2% 0.4m\n", " 740 0.1653 23.2% 0.4m\n", " 750 0.1506 23.2% 0.4m\n", " 760 0.1450 23.2% 0.3m\n", " 770 0.1453 23.2% 0.3m\n", " 780 0.1877 23.2% 0.3m\n", " 790 0.1444 23.2% 0.2m\n", " 800 0.1438 23.2% 0.2m\n", " 810 0.1962 23.2% 0.2m\n", " 820 0.1912 23.2% 0.1m\n", " 830 0.1197 23.2% 0.1m\n", " 840 0.1370 23.2% 0.1m\n", " 850 0.1571 23.2% 0.0m\n", " 860 0.1633 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 78 COMPLETE in 163s\n", "\n", "โœ… EPOCH 78 SUMMARY\n", " โฑ๏ธ Time: 171s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16641 โ†’ Val=0.16401\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.5% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 63m\n", "\n", "๐Ÿ”„ Epoch 79/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1544 23.2% 2.7m\n", " 10 0.1667 23.2% 2.8m\n", " 20 0.1957 23.2% 2.7m\n", " 30 0.2024 23.2% 2.7m\n", " 40 0.1744 23.2% 2.6m\n", " 50 0.1680 23.2% 2.6m\n", " 60 0.1170 23.2% 2.6m\n", " 70 0.1793 23.2% 2.5m\n", " 80 0.1252 23.2% 2.5m\n", " 90 0.1689 23.2% 2.5m\n", " 100 0.1656 23.2% 2.4m\n", " 110 0.1716 23.2% 2.4m\n", " 120 0.1433 23.2% 2.4m\n", " 130 0.1499 23.2% 2.3m\n", " 140 0.1560 23.2% 2.3m\n", " 150 0.1407 23.2% 2.3m\n", " 160 0.1162 23.2% 2.2m\n", " 170 0.1979 23.2% 2.2m\n", " 180 0.2258 23.2% 2.2m\n", " 190 0.1888 23.2% 2.1m\n", " 200 0.1729 23.2% 2.1m\n", " 210 0.1514 23.2% 2.1m\n", " 220 0.1950 23.2% 2.0m\n", " 230 0.1774 23.2% 2.0m\n", " 240 0.2106 23.2% 2.0m\n", " 250 0.1561 23.2% 1.9m\n", " 260 0.1069 23.2% 1.9m\n", " 270 0.1616 23.2% 1.9m\n", " 280 0.1912 23.2% 1.9m\n", " 290 0.2264 23.2% 1.8m\n", " 300 0.1882 23.2% 1.8m\n", " 310 0.1570 23.2% 1.8m\n", " 320 0.1541 23.2% 1.7m\n", " 330 0.1350 23.2% 1.7m\n", " 340 0.1661 23.2% 1.7m\n", " 350 0.2033 23.2% 1.6m\n", " 360 0.1638 23.2% 1.6m\n", " 370 0.1729 23.2% 1.6m\n", " 380 0.1376 23.2% 1.5m\n", " 390 0.1508 23.2% 1.5m\n", " 400 0.1655 23.2% 1.5m\n", " 410 0.1700 23.2% 1.4m\n", " 420 0.1493 23.2% 1.4m\n", " 430 0.1445 23.2% 1.4m\n", " 440 0.1949 23.2% 1.3m\n", " 450 0.1896 23.2% 1.3m\n", " 460 0.1279 23.2% 1.3m\n", " 470 0.1323 23.2% 1.2m\n", " 480 0.1505 23.2% 1.2m\n", " 490 0.1708 23.2% 1.2m\n", " 500 0.1616 23.2% 1.2m\n", " 510 0.1328 23.2% 1.1m\n", " 520 0.1582 23.2% 1.1m\n", " 530 0.1460 23.2% 1.1m\n", " 540 0.1882 23.2% 1.0m\n", " 550 0.1387 23.2% 1.0m\n", " 560 0.1766 23.2% 1.0m\n", " 570 0.1780 23.2% 0.9m\n", " 580 0.2185 23.2% 0.9m\n", " 590 0.1718 23.2% 0.9m\n", " 600 0.1903 23.2% 0.8m\n", " 610 0.1646 23.2% 0.8m\n", " 620 0.1385 23.2% 0.8m\n", " 630 0.1913 23.2% 0.7m\n", " 640 0.2172 23.2% 0.7m\n", " 650 0.1629 23.2% 0.7m\n", " 660 0.1854 23.2% 0.6m\n", " 670 0.1500 23.2% 0.6m\n", " 680 0.1713 23.2% 0.6m\n", " 690 0.1726 23.2% 0.5m\n", " 700 0.1583 23.2% 0.5m\n", " 710 0.1601 23.2% 0.5m\n", " 720 0.1917 23.2% 0.5m\n", " 730 0.1866 23.2% 0.4m\n", " 740 0.1631 23.2% 0.4m\n", " 750 0.1991 23.2% 0.4m\n", " 760 0.1595 23.2% 0.3m\n", " 770 0.2224 23.2% 0.3m\n", " 780 0.1774 23.2% 0.3m\n", " 790 0.1644 23.2% 0.2m\n", " 800 0.1798 23.2% 0.2m\n", " 810 0.1756 23.2% 0.2m\n", " 820 0.1766 23.2% 0.1m\n", " 830 0.1557 23.2% 0.1m\n", " 840 0.1728 23.2% 0.1m\n", " 850 0.1486 23.2% 0.0m\n", " 860 0.1786 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 79 COMPLETE in 164s\n", "\n", "โœ… EPOCH 79 SUMMARY\n", " โฑ๏ธ Time: 173s (2.9m)\n", " ๐Ÿ“‰ Loss: Train=0.16661 โ†’ Val=0.16400\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.5% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 60m\n", "\n", "๐Ÿ”„ Epoch 80/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1145 23.2% 2.7m\n", " 10 0.1377 23.2% 2.7m\n", " 20 0.1785 23.2% 2.6m\n", " 30 0.1435 23.2% 2.6m\n", " 40 0.2029 23.2% 2.6m\n", " 50 0.1298 23.2% 2.6m\n", " 60 0.1778 23.2% 2.5m\n", " 70 0.1517 23.2% 2.5m\n", " 80 0.1739 23.2% 2.5m\n", " 90 0.1570 23.2% 2.4m\n", " 100 0.1989 23.2% 2.4m\n", " 110 0.2048 23.2% 2.4m\n", " 120 0.1880 23.2% 2.3m\n", " 130 0.1962 23.2% 2.3m\n", " 140 0.1374 23.2% 2.3m\n", " 150 0.1320 23.2% 2.2m\n", " 160 0.1995 23.2% 2.2m\n", " 170 0.1314 23.2% 2.2m\n", " 180 0.2035 23.2% 2.1m\n", " 190 0.1898 23.2% 2.1m\n", " 200 0.1866 23.2% 2.1m\n", " 210 0.1441 23.2% 2.1m\n", " 220 0.1825 23.2% 2.0m\n", " 230 0.1545 23.2% 2.0m\n", " 240 0.1576 23.2% 2.0m\n", " 250 0.1930 23.2% 1.9m\n", " 260 0.2070 23.2% 1.9m\n", " 270 0.1374 23.2% 1.9m\n", " 280 0.1814 23.2% 1.8m\n", " 290 0.1471 23.2% 1.8m\n", " 300 0.1686 23.2% 1.8m\n", " 310 0.1863 23.2% 1.7m\n", " 320 0.1584 23.2% 1.7m\n", " 330 0.1196 23.2% 1.7m\n", " 340 0.1634 23.2% 1.6m\n", " 350 0.1668 23.2% 1.6m\n", " 360 0.1660 23.2% 1.6m\n", " 370 0.1800 23.2% 1.5m\n", " 380 0.2121 23.2% 1.5m\n", " 390 0.1809 23.2% 1.5m\n", " 400 0.1916 23.2% 1.5m\n", " 410 0.1419 23.2% 1.4m\n", " 420 0.1849 23.2% 1.4m\n", " 430 0.1699 23.2% 1.4m\n", " 440 0.2032 23.2% 1.3m\n", " 450 0.1612 23.2% 1.3m\n", " 460 0.1666 23.2% 1.3m\n", " 470 0.1657 23.2% 1.2m\n", " 480 0.1576 23.2% 1.2m\n", " 490 0.1344 23.2% 1.2m\n", " 500 0.1182 23.2% 1.1m\n", " 510 0.1594 23.2% 1.1m\n", " 520 0.1594 23.2% 1.1m\n", " 530 0.1620 23.2% 1.0m\n", " 540 0.1896 23.2% 1.0m\n", " 550 0.1829 23.2% 1.0m\n", " 560 0.1763 23.2% 0.9m\n", " 570 0.2276 23.2% 0.9m\n", " 580 0.1653 23.2% 0.9m\n", " 590 0.1524 23.2% 0.9m\n", " 600 0.1824 23.2% 0.8m\n", " 610 0.1903 23.2% 0.8m\n", " 620 0.1606 23.2% 0.8m\n", " 630 0.1338 23.2% 0.7m\n", " 640 0.1431 23.2% 0.7m\n", " 650 0.1485 23.2% 0.7m\n", " 660 0.1624 23.2% 0.6m\n", " 670 0.2051 23.2% 0.6m\n", " 680 0.1739 23.2% 0.6m\n", " 690 0.1799 23.2% 0.5m\n", " 700 0.1633 23.2% 0.5m\n", " 710 0.1396 23.2% 0.5m\n", " 720 0.1535 23.2% 0.4m\n", " 730 0.1647 23.2% 0.4m\n", " 740 0.1384 23.2% 0.4m\n", " 750 0.1726 23.2% 0.4m\n", " 760 0.1645 23.2% 0.3m\n", " 770 0.1455 23.2% 0.3m\n", " 780 0.1752 23.2% 0.3m\n", " 790 0.1808 23.2% 0.2m\n", " 800 0.1699 23.2% 0.2m\n", " 810 0.1662 23.2% 0.2m\n", " 820 0.1892 23.2% 0.1m\n", " 830 0.1632 23.2% 0.1m\n", " 840 0.1821 23.2% 0.1m\n", " 850 0.2102 23.2% 0.0m\n", " 860 0.1622 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 80 COMPLETE in 162s\n", "\n", "โœ… EPOCH 80 SUMMARY\n", " โฑ๏ธ Time: 171s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16646 โ†’ Val=0.16386\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.5% EJ=86.7% Overall=81.3%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 57m\n", "\n", "๐Ÿ”„ Epoch 81/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1867 23.2% 2.6m\n", " 10 0.1815 23.2% 2.7m\n", " 20 0.2033 23.2% 2.7m\n", " 30 0.1737 23.2% 2.6m\n", " 40 0.1712 23.2% 2.6m\n", " 50 0.1817 23.2% 2.6m\n", " 60 0.1820 23.2% 2.5m\n", " 70 0.1389 23.2% 2.5m\n", " 80 0.1422 23.2% 2.5m\n", " 90 0.1733 23.2% 2.4m\n", " 100 0.1714 23.2% 2.4m\n", " 110 0.2039 23.2% 2.4m\n", " 120 0.1535 23.2% 2.4m\n", " 130 0.1715 23.2% 2.3m\n", " 140 0.1465 23.2% 2.3m\n", " 150 0.1833 23.2% 2.3m\n", " 160 0.1446 23.2% 2.2m\n", " 170 0.1506 23.2% 2.2m\n", " 180 0.1858 23.2% 2.2m\n", " 190 0.1941 23.2% 2.1m\n", " 200 0.1753 23.2% 2.1m\n", " 210 0.1823 23.2% 2.1m\n", " 220 0.1700 23.2% 2.0m\n", " 230 0.1744 23.2% 2.0m\n", " 240 0.1403 23.2% 2.0m\n", " 250 0.1276 23.2% 2.0m\n", " 260 0.1814 23.2% 1.9m\n", " 270 0.1889 23.2% 1.9m\n", " 280 0.1803 23.2% 1.9m\n", " 290 0.1066 23.2% 1.8m\n", " 300 0.1767 23.2% 1.8m\n", " 310 0.1639 23.2% 1.8m\n", " 320 0.1099 23.2% 1.7m\n", " 330 0.1657 23.2% 1.7m\n", " 340 0.1893 23.2% 1.7m\n", " 350 0.1560 23.2% 1.6m\n", " 360 0.1700 23.2% 1.6m\n", " 370 0.1627 23.2% 1.6m\n", " 380 0.1800 23.2% 1.5m\n", " 390 0.1887 23.2% 1.5m\n", " 400 0.1272 23.2% 1.5m\n", " 410 0.1626 23.2% 1.4m\n", " 420 0.1548 23.2% 1.4m\n", " 430 0.1623 23.2% 1.4m\n", " 440 0.1599 23.2% 1.3m\n", " 450 0.1435 23.2% 1.3m\n", " 460 0.1784 23.2% 1.3m\n", " 470 0.1875 23.2% 1.3m\n", " 480 0.1565 23.2% 1.2m\n", " 490 0.1513 23.2% 1.2m\n", " 500 0.1556 23.2% 1.2m\n", " 510 0.1269 23.2% 1.1m\n", " 520 0.1744 23.2% 1.1m\n", " 530 0.1582 23.2% 1.1m\n", " 540 0.1674 23.2% 1.0m\n", " 550 0.1435 23.2% 1.0m\n", " 560 0.1681 23.2% 1.0m\n", " 570 0.1710 23.2% 0.9m\n", " 580 0.1358 23.2% 0.9m\n", " 590 0.1564 23.2% 0.9m\n", " 600 0.1558 23.2% 0.8m\n", " 610 0.1586 23.2% 0.8m\n", " 620 0.1895 23.2% 0.8m\n", " 630 0.2110 23.2% 0.7m\n", " 640 0.1856 23.2% 0.7m\n", " 650 0.1719 23.2% 0.7m\n", " 660 0.2094 23.2% 0.6m\n", " 670 0.2307 23.2% 0.6m\n", " 680 0.1966 23.2% 0.6m\n", " 690 0.1620 23.2% 0.6m\n", " 700 0.1458 23.2% 0.5m\n", " 710 0.1335 23.2% 0.5m\n", " 720 0.1537 23.2% 0.5m\n", " 730 0.1650 23.2% 0.4m\n", " 740 0.1728 23.2% 0.4m\n", " 750 0.1659 23.2% 0.4m\n", " 760 0.1765 23.2% 0.3m\n", " 770 0.1775 23.2% 0.3m\n", " 780 0.1868 23.2% 0.3m\n", " 790 0.1982 23.2% 0.2m\n", " 800 0.1486 23.2% 0.2m\n", " 810 0.1655 23.2% 0.2m\n", " 820 0.0965 23.2% 0.1m\n", " 830 0.1582 23.2% 0.1m\n", " 840 0.1433 23.2% 0.1m\n", " 850 0.1293 23.2% 0.0m\n", " 860 0.1564 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 81 COMPLETE in 165s\n", "\n", "โœ… EPOCH 81 SUMMARY\n", " โฑ๏ธ Time: 173s (2.9m)\n", " ๐Ÿ“‰ Loss: Train=0.16645 โ†’ Val=0.16451\n", " ๐Ÿ“Š Acc: EC=81.4% EL=75.5% EJ=86.7% Overall=81.2%\n", " โš ๏ธ No improvement for 20/30\n", " โฑ๏ธ Remaining ETA โ‰ˆ 55m\n", "\n", "๐Ÿ”„ Epoch 82/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1559 23.2% 2.6m\n", " 10 0.2327 23.2% 2.7m\n", " 20 0.1985 23.2% 2.7m\n", " 30 0.1846 23.2% 2.7m\n", " 40 0.1847 23.2% 2.6m\n", " 50 0.1564 23.2% 2.6m\n", " 60 0.1450 23.2% 2.5m\n", " 70 0.1439 23.2% 2.5m\n", " 80 0.1378 23.2% 2.5m\n", " 90 0.1473 23.2% 2.5m\n", " 100 0.1918 23.2% 2.4m\n", " 110 0.2194 23.2% 2.4m\n", " 120 0.1740 23.2% 2.4m\n", " 130 0.1621 23.2% 2.3m\n", " 140 0.1632 23.2% 2.3m\n", " 150 0.1396 23.2% 2.3m\n", " 160 0.1954 23.2% 2.2m\n", " 170 0.1847 23.2% 2.2m\n", " 180 0.1653 23.2% 2.2m\n", " 190 0.1929 23.2% 2.1m\n", " 200 0.1902 23.2% 2.1m\n", " 210 0.1407 23.2% 2.1m\n", " 220 0.1575 23.2% 2.0m\n", " 230 0.1380 23.2% 2.0m\n", " 240 0.1767 23.2% 2.0m\n", " 250 0.1757 23.2% 1.9m\n", " 260 0.1639 23.2% 1.9m\n", " 270 0.1640 23.2% 1.9m\n", " 280 0.1679 23.2% 1.9m\n", " 290 0.1492 23.2% 1.8m\n", " 300 0.1768 23.2% 1.8m\n", " 310 0.1792 23.2% 1.8m\n", " 320 0.2023 23.2% 1.7m\n", " 330 0.1616 23.2% 1.7m\n", " 340 0.1662 23.2% 1.7m\n", " 350 0.1519 23.2% 1.6m\n", " 360 0.2021 23.2% 1.6m\n", " 370 0.1634 23.2% 1.6m\n", " 380 0.1579 23.2% 1.5m\n", " 390 0.1886 23.2% 1.5m\n", " 400 0.1744 23.2% 1.5m\n", " 410 0.1791 23.2% 1.4m\n", " 420 0.1868 23.2% 1.4m\n", " 430 0.1551 23.2% 1.4m\n", " 440 0.1292 23.2% 1.3m\n", " 450 0.1671 23.2% 1.3m\n", " 460 0.1782 23.2% 1.3m\n", " 470 0.1976 23.2% 1.2m\n", " 480 0.1672 23.2% 1.2m\n", " 490 0.1519 23.2% 1.2m\n", " 500 0.1600 23.2% 1.2m\n", " 510 0.1430 23.2% 1.1m\n", " 520 0.1449 23.2% 1.1m\n", " 530 0.1561 23.2% 1.1m\n", " 540 0.1631 23.2% 1.0m\n", " 550 0.1747 23.2% 1.0m\n", " 560 0.1742 23.2% 1.0m\n", " 570 0.1817 23.2% 0.9m\n", " 580 0.1813 23.2% 0.9m\n", " 590 0.1615 23.2% 0.9m\n", " 600 0.1755 23.2% 0.8m\n", " 610 0.1908 23.2% 0.8m\n", " 620 0.1899 23.2% 0.8m\n", " 630 0.1790 23.2% 0.7m\n", " 640 0.1204 23.2% 0.7m\n", " 650 0.1654 23.2% 0.7m\n", " 660 0.1455 23.2% 0.6m\n", " 670 0.1572 23.2% 0.6m\n", " 680 0.1800 23.2% 0.6m\n", " 690 0.1423 23.2% 0.5m\n", " 700 0.1510 23.2% 0.5m\n", " 710 0.1483 23.2% 0.5m\n", " 720 0.1831 23.2% 0.5m\n", " 730 0.1802 23.2% 0.4m\n", " 740 0.1516 23.2% 0.4m\n", " 750 0.1879 23.2% 0.4m\n", " 760 0.1797 23.2% 0.3m\n", " 770 0.1726 23.2% 0.3m\n", " 780 0.2022 23.2% 0.3m\n", " 790 0.1827 23.2% 0.2m\n", " 800 0.1292 23.2% 0.2m\n", " 810 0.1458 23.2% 0.2m\n", " 820 0.1461 23.2% 0.1m\n", " 830 0.1278 23.2% 0.1m\n", " 840 0.1801 23.2% 0.1m\n", " 850 0.1471 23.2% 0.0m\n", " 860 0.1978 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 82 COMPLETE in 164s\n", "\n", "โœ… EPOCH 82 SUMMARY\n", " โฑ๏ธ Time: 172s (2.9m)\n", " ๐Ÿ“‰ Loss: Train=0.16654 โ†’ Val=0.16407\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.8% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 52m\n", "\n", "๐Ÿ”„ Epoch 83/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1728 23.2% 2.8m\n", " 10 0.1666 23.2% 2.7m\n", " 20 0.1575 23.2% 2.7m\n", " 30 0.1267 23.2% 2.6m\n", " 40 0.1826 23.2% 2.6m\n", " 50 0.1527 23.2% 2.6m\n", " 60 0.1916 23.2% 2.5m\n", " 70 0.1591 23.2% 2.5m\n", " 80 0.1425 23.2% 2.5m\n", " 90 0.1285 23.2% 2.4m\n", " 100 0.1519 23.2% 2.4m\n", " 110 0.1392 23.2% 2.4m\n", " 120 0.2215 23.2% 2.3m\n", " 130 0.2009 23.2% 2.3m\n", " 140 0.1454 23.2% 2.3m\n", " 150 0.1226 23.2% 2.2m\n", " 160 0.1675 23.2% 2.2m\n", " 170 0.1669 23.2% 2.2m\n", " 180 0.1324 23.2% 2.1m\n", " 190 0.1925 23.2% 2.1m\n", " 200 0.1696 23.2% 2.1m\n", " 210 0.2047 23.2% 2.0m\n", " 220 0.1946 23.2% 2.0m\n", " 230 0.1476 23.2% 2.0m\n", " 240 0.1424 23.2% 2.0m\n", " 250 0.1837 23.2% 1.9m\n", " 260 0.1817 23.2% 1.9m\n", " 270 0.1717 23.2% 1.9m\n", " 280 0.1644 23.2% 1.8m\n", " 290 0.1302 23.2% 1.8m\n", " 300 0.1844 23.2% 1.8m\n", " 310 0.1432 23.2% 1.7m\n", " 320 0.1508 23.2% 1.7m\n", " 330 0.1744 23.2% 1.7m\n", " 340 0.2077 23.2% 1.6m\n", " 350 0.1764 23.2% 1.6m\n", " 360 0.1954 23.2% 1.6m\n", " 370 0.1818 23.2% 1.5m\n", " 380 0.1582 23.2% 1.5m\n", " 390 0.1597 23.2% 1.5m\n", " 400 0.2034 23.2% 1.4m\n", " 410 0.1640 23.2% 1.4m\n", " 420 0.1683 23.2% 1.4m\n", " 430 0.2173 23.2% 1.4m\n", " 440 0.1558 23.2% 1.3m\n", " 450 0.1501 23.2% 1.3m\n", " 460 0.1798 23.2% 1.3m\n", " 470 0.1463 23.2% 1.2m\n", " 480 0.1692 23.2% 1.2m\n", " 490 0.1935 23.2% 1.2m\n", " 500 0.1455 23.2% 1.1m\n", " 510 0.1735 23.2% 1.1m\n", " 520 0.1460 23.2% 1.1m\n", " 530 0.1906 23.2% 1.0m\n", " 540 0.1497 23.2% 1.0m\n", " 550 0.2111 23.2% 1.0m\n", " 560 0.1573 23.2% 0.9m\n", " 570 0.1792 23.2% 0.9m\n", " 580 0.1763 23.2% 0.9m\n", " 590 0.1382 23.2% 0.9m\n", " 600 0.1543 23.2% 0.8m\n", " 610 0.2190 23.2% 0.8m\n", " 620 0.1591 23.2% 0.8m\n", " 630 0.1921 23.2% 0.7m\n", " 640 0.1530 23.2% 0.7m\n", " 650 0.1670 23.2% 0.7m\n", " 660 0.1109 23.2% 0.6m\n", " 670 0.1450 23.2% 0.6m\n", " 680 0.1521 23.2% 0.6m\n", " 690 0.1351 23.2% 0.5m\n", " 700 0.1735 23.2% 0.5m\n", " 710 0.1586 23.2% 0.5m\n", " 720 0.1748 23.2% 0.4m\n", " 730 0.1509 23.2% 0.4m\n", " 740 0.1318 23.2% 0.4m\n", " 750 0.1252 23.2% 0.4m\n", " 760 0.1864 23.2% 0.3m\n", " 770 0.1830 23.2% 0.3m\n", " 780 0.1465 23.2% 0.3m\n", " 790 0.1452 23.2% 0.2m\n", " 800 0.1217 23.2% 0.2m\n", " 810 0.1684 23.2% 0.2m\n", " 820 0.1817 23.2% 0.1m\n", " 830 0.1781 23.2% 0.1m\n", " 840 0.1946 23.2% 0.1m\n", " 850 0.1587 23.2% 0.0m\n", " 860 0.1595 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 83 COMPLETE in 162s\n", "\n", "โœ… EPOCH 83 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16633 โ†’ Val=0.16375\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.5% EJ=86.7% Overall=81.3%\n", " โญ NEW BEST MODEL SAVED (val_loss=0.16375)\n", " โฑ๏ธ Remaining ETA โ‰ˆ 48m\n", "\n", "๐Ÿ”„ Epoch 84/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.2196 23.2% 2.7m\n", " 10 0.1989 23.2% 2.7m\n", " 20 0.1943 23.2% 2.6m\n", " 30 0.1574 23.2% 2.6m\n", " 40 0.1464 23.2% 2.6m\n", " 50 0.1506 23.2% 2.5m\n", " 60 0.1831 23.2% 2.5m\n", " 70 0.1698 23.2% 2.5m\n", " 80 0.1998 23.2% 2.4m\n", " 90 0.1519 23.2% 2.4m\n", " 100 0.1856 23.2% 2.4m\n", " 110 0.1482 23.2% 2.4m\n", " 120 0.2076 23.2% 2.3m\n", " 130 0.1909 23.2% 2.3m\n", " 140 0.1783 23.2% 2.3m\n", " 150 0.1873 23.2% 2.2m\n", " 160 0.1705 23.2% 2.2m\n", " 170 0.1639 23.2% 2.2m\n", " 180 0.1681 23.2% 2.1m\n", " 190 0.1742 23.2% 2.1m\n", " 200 0.1506 23.2% 2.1m\n", " 210 0.1984 23.2% 2.1m\n", " 220 0.2072 23.2% 2.0m\n", " 230 0.1310 23.2% 2.0m\n", " 240 0.1896 23.2% 2.0m\n", " 250 0.1673 23.2% 1.9m\n", " 260 0.1608 23.2% 1.9m\n", " 270 0.1625 23.2% 1.9m\n", " 280 0.1454 23.2% 1.8m\n", " 290 0.1968 23.2% 1.8m\n", " 300 0.1819 23.2% 1.8m\n", " 310 0.1403 23.2% 1.7m\n", " 320 0.2052 23.2% 1.7m\n", " 330 0.1856 23.2% 1.7m\n", " 340 0.1537 23.2% 1.6m\n", " 350 0.1661 23.2% 1.6m\n", " 360 0.1979 23.2% 1.6m\n", " 370 0.1332 23.2% 1.6m\n", " 380 0.1606 23.2% 1.5m\n", " 390 0.1908 23.2% 1.5m\n", " 400 0.1860 23.2% 1.5m\n", " 410 0.1838 23.2% 1.4m\n", " 420 0.1941 23.2% 1.4m\n", " 430 0.1783 23.2% 1.4m\n", " 440 0.1137 23.2% 1.3m\n", " 450 0.1990 23.2% 1.3m\n", " 460 0.1789 23.2% 1.3m\n", " 470 0.1569 23.2% 1.2m\n", " 480 0.1505 23.2% 1.2m\n", " 490 0.1328 23.2% 1.2m\n", " 500 0.1706 23.2% 1.1m\n", " 510 0.1528 23.2% 1.1m\n", " 520 0.1422 23.2% 1.1m\n", " 530 0.1577 23.2% 1.0m\n", " 540 0.1716 23.2% 1.0m\n", " 550 0.1173 23.2% 1.0m\n", " 560 0.1646 23.2% 1.0m\n", " 570 0.1858 23.2% 0.9m\n", " 580 0.1280 23.2% 0.9m\n", " 590 0.1856 23.2% 0.9m\n", " 600 0.1392 23.2% 0.8m\n", " 610 0.1302 23.2% 0.8m\n", " 620 0.1970 23.2% 0.8m\n", " 630 0.1684 23.2% 0.7m\n", " 640 0.1614 23.2% 0.7m\n", " 650 0.1565 23.2% 0.7m\n", " 660 0.1673 23.2% 0.6m\n", " 670 0.1339 23.2% 0.6m\n", " 680 0.1434 23.2% 0.6m\n", " 690 0.1582 23.2% 0.5m\n", " 700 0.1706 23.2% 0.5m\n", " 710 0.1893 23.2% 0.5m\n", " 720 0.1130 23.2% 0.5m\n", " 730 0.2408 23.2% 0.4m\n", " 740 0.1480 23.2% 0.4m\n", " 750 0.1584 23.2% 0.4m\n", " 760 0.2042 23.2% 0.3m\n", " 770 0.1908 23.2% 0.3m\n", " 780 0.1659 23.2% 0.3m\n", " 790 0.1888 23.2% 0.2m\n", " 800 0.1531 23.2% 0.2m\n", " 810 0.1502 23.2% 0.2m\n", " 820 0.1340 23.2% 0.1m\n", " 830 0.1616 23.2% 0.1m\n", " 840 0.1577 23.2% 0.1m\n", " 850 0.1519 23.2% 0.0m\n", " 860 0.1394 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 84 COMPLETE in 163s\n", "\n", "โœ… EPOCH 84 SUMMARY\n", " โฑ๏ธ Time: 171s (2.9m)\n", " ๐Ÿ“‰ Loss: Train=0.16649 โ†’ Val=0.16390\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.5% EJ=86.7% Overall=81.3%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 46m\n", "\n", "๐Ÿ”„ Epoch 85/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1673 23.2% 2.7m\n", " 10 0.1696 23.2% 2.7m\n", " 20 0.1413 23.2% 2.6m\n", " 30 0.1383 23.2% 2.6m\n", " 40 0.1906 23.2% 2.6m\n", " 50 0.1856 23.2% 2.5m\n", " 60 0.2284 23.2% 2.5m\n", " 70 0.1386 23.2% 2.5m\n", " 80 0.1850 23.2% 2.5m\n", " 90 0.1895 23.2% 2.4m\n", " 100 0.1278 23.2% 2.4m\n", " 110 0.1562 23.2% 2.4m\n", " 120 0.1556 23.2% 2.3m\n", " 130 0.1327 23.2% 2.3m\n", " 140 0.1472 23.2% 2.3m\n", " 150 0.1342 23.2% 2.2m\n", " 160 0.1694 23.2% 2.2m\n", " 170 0.1518 23.2% 2.2m\n", " 180 0.1741 23.2% 2.1m\n", " 190 0.1343 23.2% 2.1m\n", " 200 0.1838 23.2% 2.1m\n", " 210 0.1976 23.2% 2.0m\n", " 220 0.1733 23.2% 2.0m\n", " 230 0.1087 23.2% 2.0m\n", " 240 0.1744 23.2% 2.0m\n", " 250 0.1793 23.2% 1.9m\n", " 260 0.1654 23.2% 1.9m\n", " 270 0.1789 23.2% 1.9m\n", " 280 0.1591 23.2% 1.8m\n", " 290 0.1979 23.2% 1.8m\n", " 300 0.1492 23.2% 1.8m\n", " 310 0.1399 23.2% 1.7m\n", " 320 0.1815 23.2% 1.7m\n", " 330 0.1660 23.2% 1.7m\n", " 340 0.1974 23.2% 1.6m\n", " 350 0.1478 23.2% 1.6m\n", " 360 0.1572 23.2% 1.6m\n", " 370 0.1573 23.2% 1.6m\n", " 380 0.1373 23.2% 1.5m\n", " 390 0.1566 23.2% 1.5m\n", " 400 0.1757 23.2% 1.5m\n", " 410 0.1765 23.2% 1.4m\n", " 420 0.1908 23.2% 1.4m\n", " 430 0.1496 23.2% 1.4m\n", " 440 0.1677 23.2% 1.3m\n", " 450 0.1519 23.2% 1.3m\n", " 460 0.1861 23.2% 1.3m\n", " 470 0.1370 23.2% 1.2m\n", " 480 0.1597 23.2% 1.2m\n", " 490 0.1813 23.2% 1.2m\n", " 500 0.1476 23.2% 1.1m\n", " 510 0.1711 23.2% 1.1m\n", " 520 0.1566 23.2% 1.1m\n", " 530 0.1338 23.2% 1.0m\n", " 540 0.2030 23.2% 1.0m\n", " 550 0.1937 23.2% 1.0m\n", " 560 0.1442 23.2% 1.0m\n", " 570 0.1786 23.2% 0.9m\n", " 580 0.1492 23.2% 0.9m\n", " 590 0.1668 23.2% 0.9m\n", " 600 0.1765 23.2% 0.8m\n", " 610 0.1896 23.2% 0.8m\n", " 620 0.1688 23.2% 0.8m\n", " 630 0.1577 23.2% 0.7m\n", " 640 0.1329 23.2% 0.7m\n", " 650 0.1647 23.2% 0.7m\n", " 660 0.1348 23.2% 0.6m\n", " 670 0.1161 23.2% 0.6m\n", " 680 0.1465 23.2% 0.6m\n", " 690 0.1626 23.2% 0.5m\n", " 700 0.1492 23.2% 0.5m\n", " 710 0.1741 23.2% 0.5m\n", " 720 0.2034 23.2% 0.5m\n", " 730 0.1427 23.2% 0.4m\n", " 740 0.1783 23.2% 0.4m\n", " 750 0.1857 23.2% 0.4m\n", " 760 0.1598 23.2% 0.3m\n", " 770 0.1576 23.2% 0.3m\n", " 780 0.1865 23.2% 0.3m\n", " 790 0.2265 23.2% 0.2m\n", " 800 0.1899 23.2% 0.2m\n", " 810 0.1951 23.2% 0.2m\n", " 820 0.1446 23.2% 0.1m\n", " 830 0.1574 23.2% 0.1m\n", " 840 0.2147 23.2% 0.1m\n", " 850 0.1585 23.2% 0.0m\n", " 860 0.1864 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 85 COMPLETE in 163s\n", "\n", "โœ… EPOCH 85 SUMMARY\n", " โฑ๏ธ Time: 171s (2.9m)\n", " ๐Ÿ“‰ Loss: Train=0.16640 โ†’ Val=0.16390\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.5% EJ=86.7% Overall=81.3%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 43m\n", "\n", "๐Ÿ”„ Epoch 86/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1752 23.2% 2.7m\n", " 10 0.1479 23.2% 2.7m\n", " 20 0.1756 23.2% 2.7m\n", " 30 0.1785 23.2% 2.7m\n", " 40 0.1752 23.2% 2.6m\n", " 50 0.1536 23.2% 2.6m\n", " 60 0.1473 23.2% 2.6m\n", " 70 0.1871 23.2% 2.5m\n", " 80 0.1801 23.2% 2.5m\n", " 90 0.1550 23.2% 2.5m\n", " 100 0.1179 23.2% 2.4m\n", " 110 0.1297 23.2% 2.4m\n", " 120 0.1462 23.2% 2.4m\n", " 130 0.1417 23.2% 2.3m\n", " 140 0.1338 23.2% 2.3m\n", " 150 0.1675 23.2% 2.3m\n", " 160 0.1859 23.2% 2.2m\n", " 170 0.2143 23.2% 2.2m\n", " 180 0.1792 23.2% 2.2m\n", " 190 0.1460 23.2% 2.1m\n", " 200 0.1754 23.2% 2.1m\n", " 210 0.1362 23.2% 2.1m\n", " 220 0.1562 23.2% 2.0m\n", " 230 0.1620 23.2% 2.0m\n", " 240 0.1478 23.2% 2.0m\n", " 250 0.1589 23.2% 1.9m\n", " 260 0.1864 23.2% 1.9m\n", " 270 0.1775 23.2% 1.9m\n", " 280 0.1247 23.2% 1.8m\n", " 290 0.1703 23.2% 1.8m\n", " 300 0.1890 23.2% 1.8m\n", " 310 0.1521 23.2% 1.8m\n", " 320 0.1631 23.2% 1.7m\n", " 330 0.1553 23.2% 1.7m\n", " 340 0.1626 23.2% 1.7m\n", " 350 0.1562 23.2% 1.6m\n", " 360 0.1629 23.2% 1.6m\n", " 370 0.1585 23.2% 1.6m\n", " 380 0.1694 23.2% 1.5m\n", " 390 0.1785 23.2% 1.5m\n", " 400 0.1855 23.2% 1.5m\n", " 410 0.1611 23.2% 1.4m\n", " 420 0.1631 23.2% 1.4m\n", " 430 0.1733 23.2% 1.4m\n", " 440 0.1627 23.2% 1.3m\n", " 450 0.1766 23.2% 1.3m\n", " 460 0.1595 23.2% 1.3m\n", " 470 0.1596 23.2% 1.2m\n", " 480 0.1436 23.2% 1.2m\n", " 490 0.1836 23.2% 1.2m\n", " 500 0.1516 23.2% 1.1m\n", " 510 0.1416 23.2% 1.1m\n", " 520 0.1567 23.2% 1.1m\n", " 530 0.1485 23.2% 1.1m\n", " 540 0.1872 23.2% 1.0m\n", " 550 0.1776 23.2% 1.0m\n", " 560 0.1767 23.2% 1.0m\n", " 570 0.1842 23.2% 0.9m\n", " 580 0.2200 23.2% 0.9m\n", " 590 0.1986 23.2% 0.9m\n", " 600 0.1805 23.2% 0.8m\n", " 610 0.1784 23.2% 0.8m\n", " 620 0.1929 23.2% 0.8m\n", " 630 0.1515 23.2% 0.7m\n", " 640 0.1492 23.2% 0.7m\n", " 650 0.1648 23.2% 0.7m\n", " 660 0.1916 23.2% 0.6m\n", " 670 0.1918 23.2% 0.6m\n", " 680 0.1559 23.2% 0.6m\n", " 690 0.1443 23.2% 0.5m\n", " 700 0.1969 23.2% 0.5m\n", " 710 0.1865 23.2% 0.5m\n", " 720 0.1443 23.2% 0.5m\n", " 730 0.1594 23.2% 0.4m\n", " 740 0.1124 23.2% 0.4m\n", " 750 0.1738 23.2% 0.4m\n", " 760 0.1750 23.2% 0.3m\n", " 770 0.1534 23.2% 0.3m\n", " 780 0.1793 23.2% 0.3m\n", " 790 0.1580 23.2% 0.2m\n", " 800 0.1931 23.2% 0.2m\n", " 810 0.1522 23.2% 0.2m\n", " 820 0.1354 23.2% 0.1m\n", " 830 0.1657 23.2% 0.1m\n", " 840 0.1469 23.2% 0.1m\n", " 850 0.1820 23.2% 0.0m\n", " 860 0.2048 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 86 COMPLETE in 164s\n", "\n", "โœ… EPOCH 86 SUMMARY\n", " โฑ๏ธ Time: 172s (2.9m)\n", " ๐Ÿ“‰ Loss: Train=0.16645 โ†’ Val=0.16387\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.5% EJ=86.7% Overall=81.3%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 40m\n", "\n", "๐Ÿ”„ Epoch 87/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1703 23.2% 2.7m\n", " 10 0.1521 23.2% 2.7m\n", " 20 0.1768 23.2% 2.7m\n", " 30 0.1739 23.2% 2.7m\n", " 40 0.1348 23.2% 2.6m\n", " 50 0.1530 23.2% 2.6m\n", " 60 0.1605 23.2% 2.6m\n", " 70 0.2008 23.2% 2.5m\n", " 80 0.1540 23.2% 2.5m\n", " 90 0.1730 23.2% 2.5m\n", " 100 0.1680 23.2% 2.4m\n", " 110 0.1436 23.2% 2.4m\n", " 120 0.1582 23.2% 2.4m\n", " 130 0.1830 23.2% 2.3m\n", " 140 0.1606 23.2% 2.3m\n", " 150 0.1338 23.2% 2.3m\n", " 160 0.1850 23.2% 2.2m\n", " 170 0.1950 23.2% 2.2m\n", " 180 0.1441 23.2% 2.2m\n", " 190 0.1392 23.2% 2.1m\n", " 200 0.2093 23.2% 2.1m\n", " 210 0.1873 23.2% 2.1m\n", " 220 0.1637 23.2% 2.0m\n", " 230 0.1609 23.2% 2.0m\n", " 240 0.1648 23.2% 2.0m\n", " 250 0.1418 23.2% 1.9m\n", " 260 0.1560 23.2% 1.9m\n", " 270 0.1414 23.2% 1.9m\n", " 280 0.1904 23.2% 1.8m\n", " 290 0.1609 23.2% 1.8m\n", " 300 0.1679 23.2% 1.8m\n", " 310 0.1291 23.2% 1.8m\n", " 320 0.1432 23.2% 1.7m\n", " 330 0.1543 23.2% 1.7m\n", " 340 0.1802 23.2% 1.7m\n", " 350 0.1557 23.2% 1.6m\n", " 360 0.1540 23.2% 1.6m\n", " 370 0.1521 23.2% 1.6m\n", " 380 0.1157 23.2% 1.5m\n", " 390 0.1699 23.2% 1.5m\n", " 400 0.1638 23.2% 1.5m\n", " 410 0.1587 23.2% 1.4m\n", " 420 0.1708 23.2% 1.4m\n", " 430 0.1910 23.2% 1.4m\n", " 440 0.1449 23.2% 1.3m\n", " 450 0.2045 23.2% 1.3m\n", " 460 0.1468 23.2% 1.3m\n", " 470 0.1925 23.2% 1.2m\n", " 480 0.1371 23.2% 1.2m\n", " 490 0.1730 23.2% 1.2m\n", " 500 0.1301 23.2% 1.2m\n", " 510 0.1354 23.2% 1.1m\n", " 520 0.1977 23.2% 1.1m\n", " 530 0.1615 23.2% 1.1m\n", " 540 0.1602 23.2% 1.0m\n", " 550 0.2030 23.2% 1.0m\n", " 560 0.1764 23.2% 1.0m\n", " 570 0.1476 23.2% 0.9m\n", " 580 0.1984 23.2% 0.9m\n", " 590 0.1868 23.2% 0.9m\n", " 600 0.1300 23.2% 0.8m\n", " 610 0.1410 23.2% 0.8m\n", " 620 0.1621 23.2% 0.8m\n", " 630 0.1418 23.2% 0.7m\n", " 640 0.1690 23.2% 0.7m\n", " 650 0.2315 23.2% 0.7m\n", " 660 0.1836 23.2% 0.6m\n", " 670 0.1427 23.2% 0.6m\n", " 680 0.1996 23.2% 0.6m\n", " 690 0.1454 23.2% 0.5m\n", " 700 0.1329 23.2% 0.5m\n", " 710 0.1811 23.2% 0.5m\n", " 720 0.1289 23.2% 0.5m\n", " 730 0.1799 23.2% 0.4m\n", " 740 0.1399 23.2% 0.4m\n", " 750 0.1834 23.2% 0.4m\n", " 760 0.1738 23.2% 0.3m\n", " 770 0.1585 23.2% 0.3m\n", " 780 0.1229 23.2% 0.3m\n", " 790 0.1784 23.2% 0.2m\n", " 800 0.1602 23.2% 0.2m\n", " 810 0.1566 23.2% 0.2m\n", " 820 0.1672 23.2% 0.1m\n", " 830 0.1447 23.2% 0.1m\n", " 840 0.1848 23.2% 0.1m\n", " 850 0.1496 23.2% 0.0m\n", " 860 0.1428 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 87 COMPLETE in 164s\n", "\n", "โœ… EPOCH 87 SUMMARY\n", " โฑ๏ธ Time: 172s (2.9m)\n", " ๐Ÿ“‰ Loss: Train=0.16653 โ†’ Val=0.16399\n", " ๐Ÿ“Š Acc: EC=81.5% EL=75.5% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 37m\n", "\n", "๐Ÿ”„ Epoch 88/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1544 23.2% 2.6m\n", " 10 0.1453 23.2% 2.7m\n", " 20 0.1213 23.2% 2.6m\n", " 30 0.1431 23.2% 2.6m\n", " 40 0.1955 23.2% 2.6m\n", " 50 0.1517 23.2% 2.5m\n", " 60 0.1542 23.2% 2.5m\n", " 70 0.1471 23.2% 2.5m\n", " 80 0.1829 23.2% 2.5m\n", " 90 0.1514 23.2% 2.4m\n", " 100 0.1366 23.2% 2.4m\n", " 110 0.1729 23.2% 2.4m\n", " 120 0.1312 23.2% 2.3m\n", " 130 0.1585 23.2% 2.3m\n", " 140 0.2054 23.2% 2.3m\n", " 150 0.1457 23.2% 2.2m\n", " 160 0.1342 23.2% 2.2m\n", " 170 0.1759 23.2% 2.2m\n", " 180 0.1454 23.2% 2.2m\n", " 190 0.2016 23.2% 2.1m\n", " 200 0.2042 23.2% 2.1m\n", " 210 0.1907 23.2% 2.1m\n", " 220 0.1784 23.2% 2.0m\n", " 230 0.1312 23.2% 2.0m\n", " 240 0.1474 23.2% 2.0m\n", " 250 0.1959 23.2% 1.9m\n", " 260 0.1371 23.2% 1.9m\n", " 270 0.1488 23.2% 1.9m\n", " 280 0.1865 23.2% 1.8m\n", " 290 0.1246 23.2% 1.8m\n", " 300 0.1801 23.2% 1.8m\n", " 310 0.1828 23.2% 1.7m\n", " 320 0.1632 23.2% 1.7m\n", " 330 0.1749 23.2% 1.7m\n", " 340 0.1676 23.2% 1.7m\n", " 350 0.1558 23.2% 1.6m\n", " 360 0.1607 23.2% 1.6m\n", " 370 0.1902 23.2% 1.6m\n", " 380 0.1307 23.2% 1.5m\n", " 390 0.1373 23.2% 1.5m\n", " 400 0.2051 23.2% 1.5m\n", " 410 0.1494 23.2% 1.4m\n", " 420 0.1339 23.2% 1.4m\n", " 430 0.1838 23.2% 1.4m\n", " 440 0.1611 23.2% 1.3m\n", " 450 0.1524 23.2% 1.3m\n", " 460 0.1721 23.2% 1.3m\n", " 470 0.1462 23.2% 1.2m\n", " 480 0.1800 23.2% 1.2m\n", " 490 0.1628 23.2% 1.2m\n", " 500 0.1566 23.2% 1.1m\n", " 510 0.1719 23.2% 1.1m\n", " 520 0.1837 23.2% 1.1m\n", " 530 0.2195 23.2% 1.1m\n", " 540 0.1796 23.2% 1.0m\n", " 550 0.1903 23.2% 1.0m\n", " 560 0.1493 23.2% 1.0m\n", " 570 0.1900 23.2% 0.9m\n", " 580 0.1985 23.2% 0.9m\n", " 590 0.1465 23.2% 0.9m\n", " 600 0.1748 23.2% 0.8m\n", " 610 0.1522 23.2% 0.8m\n", " 620 0.1711 23.2% 0.8m\n", " 630 0.1501 23.2% 0.7m\n", " 640 0.1654 23.2% 0.7m\n", " 650 0.1681 23.2% 0.7m\n", " 660 0.1623 23.2% 0.6m\n", " 670 0.2033 23.2% 0.6m\n", " 680 0.1691 23.2% 0.6m\n", " 690 0.1440 23.2% 0.5m\n", " 700 0.1656 23.2% 0.5m\n", " 710 0.1529 23.2% 0.5m\n", " 720 0.1778 23.2% 0.5m\n", " 730 0.1479 23.2% 0.4m\n", " 740 0.1430 23.2% 0.4m\n", " 750 0.1822 23.2% 0.4m\n", " 760 0.1601 23.2% 0.3m\n", " 770 0.1762 23.2% 0.3m\n", " 780 0.1607 23.2% 0.3m\n", " 790 0.1504 23.2% 0.2m\n", " 800 0.1763 23.2% 0.2m\n", " 810 0.1196 23.2% 0.2m\n", " 820 0.1805 23.2% 0.1m\n", " 830 0.1379 23.2% 0.1m\n", " 840 0.1972 23.2% 0.1m\n", " 850 0.1954 23.2% 0.0m\n", " 860 0.1727 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 88 COMPLETE in 163s\n", "\n", "โœ… EPOCH 88 SUMMARY\n", " โฑ๏ธ Time: 171s (2.9m)\n", " ๐Ÿ“‰ Loss: Train=0.16646 โ†’ Val=0.16370\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.5% EJ=86.7% Overall=81.3%\n", " โญ NEW BEST MODEL SAVED (val_loss=0.16370)\n", " โฑ๏ธ Remaining ETA โ‰ˆ 34m\n", "\n", "๐Ÿ”„ Epoch 89/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1834 23.2% 2.8m\n", " 10 0.1171 23.2% 2.8m\n", " 20 0.1369 23.2% 2.7m\n", " 30 0.1613 23.2% 2.7m\n", " 40 0.1763 23.2% 2.6m\n", " 50 0.1230 23.2% 2.6m\n", " 60 0.1414 23.2% 2.5m\n", " 70 0.1559 23.2% 2.5m\n", " 80 0.1581 23.2% 2.5m\n", " 90 0.1476 23.2% 2.5m\n", " 100 0.1711 23.2% 2.4m\n", " 110 0.1903 23.2% 2.4m\n", " 120 0.1857 23.2% 2.4m\n", " 130 0.1541 23.2% 2.3m\n", " 140 0.2083 23.2% 2.3m\n", " 150 0.1628 23.2% 2.2m\n", " 160 0.1817 23.2% 2.2m\n", " 170 0.1629 23.2% 2.2m\n", " 180 0.1748 23.2% 2.2m\n", " 190 0.1442 23.2% 2.1m\n", " 200 0.1880 23.2% 2.1m\n", " 210 0.1461 23.2% 2.1m\n", " 220 0.1732 23.2% 2.0m\n", " 230 0.1505 23.2% 2.0m\n", " 240 0.1728 23.2% 2.0m\n", " 250 0.1475 23.2% 1.9m\n", " 260 0.1724 23.2% 1.9m\n", " 270 0.1613 23.2% 1.9m\n", " 280 0.2107 23.2% 1.8m\n", " 290 0.1632 23.2% 1.8m\n", " 300 0.1709 23.2% 1.8m\n", " 310 0.1513 23.2% 1.7m\n", " 320 0.1901 23.2% 1.7m\n", " 330 0.1709 23.2% 1.7m\n", " 340 0.1764 23.2% 1.6m\n", " 350 0.2090 23.2% 1.6m\n", " 360 0.2090 23.2% 1.6m\n", " 370 0.1598 23.2% 1.5m\n", " 380 0.1658 23.2% 1.5m\n", " 390 0.1496 23.2% 1.5m\n", " 400 0.2036 23.2% 1.5m\n", " 410 0.1897 23.2% 1.4m\n", " 420 0.1558 23.2% 1.4m\n", " 430 0.1696 23.2% 1.4m\n", " 440 0.1900 23.2% 1.3m\n", " 450 0.1459 23.2% 1.3m\n", " 460 0.1600 23.2% 1.3m\n", " 470 0.1852 23.2% 1.2m\n", " 480 0.1681 23.2% 1.2m\n", " 490 0.1869 23.2% 1.2m\n", " 500 0.1465 23.2% 1.1m\n", " 510 0.1694 23.2% 1.1m\n", " 520 0.2163 23.2% 1.1m\n", " 530 0.1687 23.2% 1.0m\n", " 540 0.1404 23.2% 1.0m\n", " 550 0.1536 23.2% 1.0m\n", " 560 0.1329 23.2% 1.0m\n", " 570 0.1426 23.2% 0.9m\n", " 580 0.1868 23.2% 0.9m\n", " 590 0.1891 23.2% 0.9m\n", " 600 0.1739 23.2% 0.8m\n", " 610 0.1921 23.2% 0.8m\n", " 620 0.2020 23.2% 0.8m\n", " 630 0.1770 23.2% 0.7m\n", " 640 0.1126 23.2% 0.7m\n", " 650 0.1693 23.2% 0.7m\n", " 660 0.1554 23.2% 0.6m\n", " 670 0.2023 23.2% 0.6m\n", " 680 0.1390 23.2% 0.6m\n", " 690 0.1759 23.2% 0.5m\n", " 700 0.1561 23.2% 0.5m\n", " 710 0.1651 23.2% 0.5m\n", " 720 0.1657 23.2% 0.4m\n", " 730 0.1946 23.2% 0.4m\n", " 740 0.1295 23.2% 0.4m\n", " 750 0.1974 23.2% 0.4m\n", " 760 0.1460 23.2% 0.3m\n", " 770 0.1341 23.2% 0.3m\n", " 780 0.1804 23.2% 0.3m\n", " 790 0.1626 23.2% 0.2m\n", " 800 0.1673 23.2% 0.2m\n", " 810 0.1836 23.2% 0.2m\n", " 820 0.1163 23.2% 0.1m\n", " 830 0.1952 23.2% 0.1m\n", " 840 0.1726 23.2% 0.1m\n", " 850 0.1467 23.2% 0.0m\n", " 860 0.2200 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 89 COMPLETE in 163s\n", "\n", "โœ… EPOCH 89 SUMMARY\n", " โฑ๏ธ Time: 171s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16645 โ†’ Val=0.16424\n", " ๐Ÿ“Š Acc: EC=81.5% EL=75.5% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 31m\n", "\n", "๐Ÿ”„ Epoch 90/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1484 23.2% 2.6m\n", " 10 0.1744 23.2% 2.7m\n", " 20 0.1624 23.2% 2.7m\n", " 30 0.1702 23.2% 2.6m\n", " 40 0.1564 23.2% 2.6m\n", " 50 0.1836 23.2% 2.6m\n", " 60 0.1910 23.2% 2.5m\n", " 70 0.1694 23.2% 2.5m\n", " 80 0.1919 23.2% 2.5m\n", " 90 0.1636 23.2% 2.4m\n", " 100 0.1490 23.2% 2.4m\n", " 110 0.1568 23.2% 2.4m\n", " 120 0.1609 23.2% 2.3m\n", " 130 0.1911 23.2% 2.3m\n", " 140 0.1757 23.2% 2.3m\n", " 150 0.2042 23.2% 2.2m\n", " 160 0.1358 23.2% 2.2m\n", " 170 0.1810 23.2% 2.2m\n", " 180 0.1839 23.2% 2.2m\n", " 190 0.1180 23.2% 2.1m\n", " 200 0.1796 23.2% 2.1m\n", " 210 0.1443 23.2% 2.1m\n", " 220 0.1526 23.2% 2.0m\n", " 230 0.1807 23.2% 2.0m\n", " 240 0.1336 23.2% 2.0m\n", " 250 0.1760 23.2% 1.9m\n", " 260 0.1494 23.2% 1.9m\n", " 270 0.1736 23.2% 1.9m\n", " 280 0.1422 23.2% 1.8m\n", " 290 0.1644 23.2% 1.8m\n", " 300 0.1737 23.2% 1.8m\n", " 310 0.1783 23.2% 1.7m\n", " 320 0.1320 23.2% 1.7m\n", " 330 0.1277 23.2% 1.7m\n", " 340 0.1569 23.2% 1.6m\n", " 350 0.2258 23.2% 1.6m\n", " 360 0.1257 23.2% 1.6m\n", " 370 0.1760 23.2% 1.6m\n", " 380 0.1673 23.2% 1.5m\n", " 390 0.1771 23.2% 1.5m\n", " 400 0.1647 23.2% 1.5m\n", " 410 0.1699 23.2% 1.4m\n", " 420 0.1839 23.2% 1.4m\n", " 430 0.1643 23.2% 1.4m\n", " 440 0.1931 23.2% 1.3m\n", " 450 0.1667 23.2% 1.3m\n", " 460 0.2075 23.2% 1.3m\n", " 470 0.1510 23.2% 1.2m\n", " 480 0.1579 23.2% 1.2m\n", " 490 0.1495 23.2% 1.2m\n", " 500 0.1452 23.2% 1.1m\n", " 510 0.1546 23.2% 1.1m\n", " 520 0.2026 23.2% 1.1m\n", " 530 0.1812 23.2% 1.0m\n", " 540 0.1586 23.2% 1.0m\n", " 550 0.1488 23.2% 1.0m\n", " 560 0.1794 23.2% 1.0m\n", " 570 0.1878 23.2% 0.9m\n", " 580 0.1940 23.2% 0.9m\n", " 590 0.1374 23.2% 0.9m\n", " 600 0.1805 23.2% 0.8m\n", " 610 0.1545 23.2% 0.8m\n", " 620 0.1457 23.2% 0.8m\n", " 630 0.1685 23.2% 0.7m\n", " 640 0.1230 23.2% 0.7m\n", " 650 0.1693 23.2% 0.7m\n", " 660 0.1542 23.2% 0.6m\n", " 670 0.1477 23.2% 0.6m\n", " 680 0.1508 23.2% 0.6m\n", " 690 0.1601 23.2% 0.5m\n", " 700 0.1837 23.2% 0.5m\n", " 710 0.1743 23.2% 0.5m\n", " 720 0.1974 23.2% 0.5m\n", " 730 0.1489 23.2% 0.4m\n", " 740 0.1581 23.2% 0.4m\n", " 750 0.1955 23.2% 0.4m\n", " 760 0.1489 23.2% 0.3m\n", " 770 0.1660 23.2% 0.3m\n", " 780 0.1765 23.2% 0.3m\n", " 790 0.1523 23.2% 0.2m\n", " 800 0.1554 23.2% 0.2m\n", " 810 0.1705 23.2% 0.2m\n", " 820 0.1689 23.2% 0.1m\n", " 830 0.1546 23.2% 0.1m\n", " 840 0.1302 23.2% 0.1m\n", " 850 0.1714 23.2% 0.0m\n", " 860 0.1857 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 90 COMPLETE in 163s\n", "\n", "โœ… EPOCH 90 SUMMARY\n", " โฑ๏ธ Time: 171s (2.9m)\n", " ๐Ÿ“‰ Loss: Train=0.16634 โ†’ Val=0.16382\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.5% EJ=86.7% Overall=81.3%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 29m\n", "\n", "๐Ÿ”„ Epoch 91/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1624 23.2% 2.7m\n", " 10 0.1689 23.2% 2.7m\n", " 20 0.1982 23.2% 2.7m\n", " 30 0.1617 23.2% 2.6m\n", " 40 0.1275 23.2% 2.6m\n", " 50 0.1477 23.2% 2.5m\n", " 60 0.1822 23.2% 2.5m\n", " 70 0.1446 23.2% 2.5m\n", " 80 0.1724 23.2% 2.4m\n", " 90 0.2023 23.2% 2.4m\n", " 100 0.1926 23.2% 2.4m\n", " 110 0.1617 23.2% 2.4m\n", " 120 0.1724 23.2% 2.3m\n", " 130 0.1770 23.2% 2.3m\n", " 140 0.1098 23.2% 2.3m\n", " 150 0.1899 23.2% 2.2m\n", " 160 0.1825 23.2% 2.2m\n", " 170 0.1641 23.2% 2.2m\n", " 180 0.1904 23.2% 2.1m\n", " 190 0.2047 23.2% 2.1m\n", " 200 0.1817 23.2% 2.1m\n", " 210 0.1928 23.2% 2.1m\n", " 220 0.1389 23.2% 2.0m\n", " 230 0.1743 23.2% 2.0m\n", " 240 0.1652 23.2% 2.0m\n", " 250 0.1290 23.2% 1.9m\n", " 260 0.1839 23.2% 1.9m\n", " 270 0.1933 23.2% 1.9m\n", " 280 0.1576 23.2% 1.8m\n", " 290 0.1607 23.2% 1.8m\n", " 300 0.1741 23.2% 1.8m\n", " 310 0.1621 23.2% 1.7m\n", " 320 0.1740 23.2% 1.7m\n", " 330 0.1549 23.2% 1.7m\n", " 340 0.2375 23.2% 1.6m\n", " 350 0.1914 23.2% 1.6m\n", " 360 0.1736 23.2% 1.6m\n", " 370 0.1484 23.2% 1.5m\n", " 380 0.1195 23.2% 1.5m\n", " 390 0.1607 23.2% 1.5m\n", " 400 0.1730 23.2% 1.5m\n", " 410 0.1677 23.2% 1.4m\n", " 420 0.1418 23.2% 1.4m\n", " 430 0.1444 23.2% 1.4m\n", " 440 0.1757 23.2% 1.3m\n", " 450 0.1882 23.2% 1.3m\n", " 460 0.1655 23.2% 1.3m\n", " 470 0.2327 23.2% 1.2m\n", " 480 0.1648 23.2% 1.2m\n", " 490 0.1732 23.2% 1.2m\n", " 500 0.1689 23.2% 1.1m\n", " 510 0.1598 23.2% 1.1m\n", " 520 0.1344 23.2% 1.1m\n", " 530 0.1642 23.2% 1.0m\n", " 540 0.1675 23.2% 1.0m\n", " 550 0.1852 23.2% 1.0m\n", " 560 0.1691 23.2% 1.0m\n", " 570 0.1788 23.2% 0.9m\n", " 580 0.1628 23.2% 0.9m\n", " 590 0.1295 23.2% 0.9m\n", " 600 0.1509 23.2% 0.8m\n", " 610 0.1700 23.2% 0.8m\n", " 620 0.1482 23.2% 0.8m\n", " 630 0.1549 23.2% 0.7m\n", " 640 0.1775 23.2% 0.7m\n", " 650 0.1676 23.2% 0.7m\n", " 660 0.1548 23.2% 0.6m\n", " 670 0.1674 23.2% 0.6m\n", " 680 0.1476 23.2% 0.6m\n", " 690 0.1693 23.2% 0.5m\n", " 700 0.1521 23.2% 0.5m\n", " 710 0.1259 23.2% 0.5m\n", " 720 0.1593 23.2% 0.4m\n", " 730 0.1841 23.2% 0.4m\n", " 740 0.1956 23.2% 0.4m\n", " 750 0.1522 23.2% 0.4m\n", " 760 0.1685 23.2% 0.3m\n", " 770 0.1622 23.2% 0.3m\n", " 780 0.2277 23.2% 0.3m\n", " 790 0.1002 23.2% 0.2m\n", " 800 0.1703 23.2% 0.2m\n", " 810 0.1654 23.2% 0.2m\n", " 820 0.1453 23.2% 0.1m\n", " 830 0.1740 23.2% 0.1m\n", " 840 0.1627 23.2% 0.1m\n", " 850 0.1272 23.2% 0.0m\n", " 860 0.1358 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 91 COMPLETE in 163s\n", "\n", "โœ… EPOCH 91 SUMMARY\n", " โฑ๏ธ Time: 171s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16643 โ†’ Val=0.16393\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.5% EJ=86.7% Overall=81.3%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 26m\n", "\n", "๐Ÿ”„ Epoch 92/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1582 23.2% 2.6m\n", " 10 0.1798 23.2% 2.6m\n", " 20 0.1859 23.2% 2.6m\n", " 30 0.1803 23.2% 2.6m\n", " 40 0.1780 23.2% 2.6m\n", " 50 0.1305 23.2% 2.6m\n", " 60 0.2080 23.2% 2.5m\n", " 70 0.1541 23.2% 2.5m\n", " 80 0.1937 23.2% 2.5m\n", " 90 0.1630 23.2% 2.4m\n", " 100 0.1729 23.2% 2.4m\n", " 110 0.1681 23.2% 2.4m\n", " 120 0.1542 23.2% 2.3m\n", " 130 0.1763 23.2% 2.3m\n", " 140 0.1306 23.2% 2.3m\n", " 150 0.1607 23.2% 2.2m\n", " 160 0.2150 23.2% 2.2m\n", " 170 0.1650 23.2% 2.2m\n", " 180 0.1599 23.2% 2.1m\n", " 190 0.1770 23.2% 2.1m\n", " 200 0.0967 23.2% 2.1m\n", " 210 0.1726 23.2% 2.1m\n", " 220 0.1709 23.2% 2.0m\n", " 230 0.1405 23.2% 2.0m\n", " 240 0.2076 23.2% 2.0m\n", " 250 0.2011 23.2% 1.9m\n", " 260 0.1507 23.2% 1.9m\n", " 270 0.1788 23.2% 1.9m\n", " 280 0.1593 23.2% 1.8m\n", " 290 0.1656 23.2% 1.8m\n", " 300 0.1539 23.2% 1.8m\n", " 310 0.1708 23.2% 1.7m\n", " 320 0.1245 23.2% 1.7m\n", " 330 0.1584 23.2% 1.7m\n", " 340 0.1386 23.2% 1.6m\n", " 350 0.1713 23.2% 1.6m\n", " 360 0.1513 23.2% 1.6m\n", " 370 0.1767 23.2% 1.5m\n", " 380 0.1875 23.2% 1.5m\n", " 390 0.1949 23.2% 1.5m\n", " 400 0.1667 23.2% 1.5m\n", " 410 0.1572 23.2% 1.4m\n", " 420 0.1750 23.2% 1.4m\n", " 430 0.1964 23.2% 1.4m\n", " 440 0.1799 23.2% 1.3m\n", " 450 0.2112 23.2% 1.3m\n", " 460 0.1778 23.2% 1.3m\n", " 470 0.2012 23.2% 1.2m\n", " 480 0.1732 23.2% 1.2m\n", " 490 0.1646 23.2% 1.2m\n", " 500 0.1993 23.2% 1.1m\n", " 510 0.1492 23.2% 1.1m\n", " 520 0.1926 23.2% 1.1m\n", " 530 0.1698 23.2% 1.0m\n", " 540 0.1470 23.2% 1.0m\n", " 550 0.1642 23.2% 1.0m\n", " 560 0.1747 23.2% 0.9m\n", " 570 0.1645 23.2% 0.9m\n", " 580 0.1821 23.2% 0.9m\n", " 590 0.1349 23.2% 0.9m\n", " 600 0.1545 23.2% 0.8m\n", " 610 0.1338 23.2% 0.8m\n", " 620 0.1720 23.2% 0.8m\n", " 630 0.1865 23.2% 0.7m\n", " 640 0.1387 23.2% 0.7m\n", " 650 0.1559 23.2% 0.7m\n", " 660 0.1711 23.2% 0.6m\n", " 670 0.1177 23.2% 0.6m\n", " 680 0.1842 23.2% 0.6m\n", " 690 0.1472 23.2% 0.5m\n", " 700 0.1900 23.2% 0.5m\n", " 710 0.1893 23.2% 0.5m\n", " 720 0.1365 23.2% 0.4m\n", " 730 0.1603 23.2% 0.4m\n", " 740 0.1692 23.2% 0.4m\n", " 750 0.2126 23.2% 0.4m\n", " 760 0.1801 23.2% 0.3m\n", " 770 0.1733 23.2% 0.3m\n", " 780 0.1534 23.2% 0.3m\n", " 790 0.1247 23.2% 0.2m\n", " 800 0.1417 23.2% 0.2m\n", " 810 0.1823 23.2% 0.2m\n", " 820 0.1403 23.2% 0.1m\n", " 830 0.1583 23.2% 0.1m\n", " 840 0.1629 23.2% 0.1m\n", " 850 0.1409 23.2% 0.0m\n", " 860 0.1573 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 92 COMPLETE in 162s\n", "\n", "โœ… EPOCH 92 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16639 โ†’ Val=0.16397\n", " ๐Ÿ“Š Acc: EC=81.5% EL=75.5% EJ=86.7% Overall=81.3%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 23m\n", "\n", "๐Ÿ”„ Epoch 93/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1432 23.2% 2.7m\n", " 10 0.2085 23.2% 2.7m\n", " 20 0.1870 23.2% 2.6m\n", " 30 0.2004 23.2% 2.6m\n", " 40 0.1500 23.2% 2.6m\n", " 50 0.1561 23.2% 2.6m\n", " 60 0.1011 23.2% 2.5m\n", " 70 0.1519 23.2% 2.5m\n", " 80 0.1618 23.2% 2.5m\n", " 90 0.1910 23.2% 2.4m\n", " 100 0.2227 23.2% 2.4m\n", " 110 0.1424 23.2% 2.4m\n", " 120 0.1418 23.2% 2.3m\n", " 130 0.1342 23.2% 2.3m\n", " 140 0.1495 23.2% 2.3m\n", " 150 0.1726 23.2% 2.2m\n", " 160 0.1554 23.2% 2.2m\n", " 170 0.1747 23.2% 2.2m\n", " 180 0.1288 23.2% 2.2m\n", " 190 0.1854 23.2% 2.1m\n", " 200 0.1852 23.2% 2.1m\n", " 210 0.1457 23.2% 2.1m\n", " 220 0.1471 23.2% 2.0m\n", " 230 0.1635 23.2% 2.0m\n", " 240 0.1405 23.2% 2.0m\n", " 250 0.1778 23.2% 1.9m\n", " 260 0.1652 23.2% 1.9m\n", " 270 0.1728 23.2% 1.9m\n", " 280 0.1459 23.2% 1.8m\n", " 290 0.1801 23.2% 1.8m\n", " 300 0.1985 23.2% 1.8m\n", " 310 0.1641 23.2% 1.7m\n", " 320 0.1504 23.2% 1.7m\n", " 330 0.1730 23.2% 1.7m\n", " 340 0.1674 23.2% 1.6m\n", " 350 0.1475 23.2% 1.6m\n", " 360 0.1241 23.2% 1.6m\n", " 370 0.1375 23.2% 1.5m\n", " 380 0.1922 23.2% 1.5m\n", " 390 0.1709 23.2% 1.5m\n", " 400 0.1846 23.2% 1.5m\n", " 410 0.1252 23.2% 1.4m\n", " 420 0.1814 23.2% 1.4m\n", " 430 0.1802 23.2% 1.4m\n", " 440 0.1786 23.2% 1.3m\n", " 450 0.1484 23.2% 1.3m\n", " 460 0.1842 23.2% 1.3m\n", " 470 0.2081 23.2% 1.2m\n", " 480 0.1575 23.2% 1.2m\n", " 490 0.1740 23.2% 1.2m\n", " 500 0.1536 23.2% 1.1m\n", " 510 0.1953 23.2% 1.1m\n", " 520 0.1216 23.2% 1.1m\n", " 530 0.1934 23.2% 1.0m\n", " 540 0.1883 23.2% 1.0m\n", " 550 0.1905 23.2% 1.0m\n", " 560 0.1532 23.2% 1.0m\n", " 570 0.1538 23.2% 0.9m\n", " 580 0.1146 23.2% 0.9m\n", " 590 0.1390 23.2% 0.9m\n", " 600 0.1382 23.2% 0.8m\n", " 610 0.1731 23.2% 0.8m\n", " 620 0.1830 23.2% 0.8m\n", " 630 0.1892 23.2% 0.7m\n", " 640 0.1862 23.2% 0.7m\n", " 650 0.2000 23.2% 0.7m\n", " 660 0.2005 23.2% 0.6m\n", " 670 0.1828 23.2% 0.6m\n", " 680 0.1939 23.2% 0.6m\n", " 690 0.1553 23.2% 0.5m\n", " 700 0.1816 23.2% 0.5m\n", " 710 0.1883 23.2% 0.5m\n", " 720 0.1787 23.2% 0.4m\n", " 730 0.1803 23.2% 0.4m\n", " 740 0.1841 23.2% 0.4m\n", " 750 0.1143 23.2% 0.4m\n", " 760 0.1854 23.2% 0.3m\n", " 770 0.1590 23.2% 0.3m\n", " 780 0.1947 23.2% 0.3m\n", " 790 0.1366 23.2% 0.2m\n", " 800 0.1617 23.2% 0.2m\n", " 810 0.1845 23.2% 0.2m\n", " 820 0.1405 23.2% 0.1m\n", " 830 0.1957 23.2% 0.1m\n", " 840 0.1693 23.2% 0.1m\n", " 850 0.1803 23.2% 0.0m\n", " 860 0.1759 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 93 COMPLETE in 163s\n", "\n", "โœ… EPOCH 93 SUMMARY\n", " โฑ๏ธ Time: 171s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16643 โ†’ Val=0.16386\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.5% EJ=86.7% Overall=81.3%\n", " โš ๏ธ No improvement for 5/30\n", " โฑ๏ธ Remaining ETA โ‰ˆ 20m\n", "\n", "๐Ÿ”„ Epoch 94/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1271 23.2% 2.7m\n", " 10 0.1561 23.2% 2.7m\n", " 20 0.1518 23.2% 2.6m\n", " 30 0.1786 23.2% 2.6m\n", " 40 0.1765 23.2% 2.6m\n", " 50 0.1521 23.2% 2.5m\n", " 60 0.2019 23.2% 2.5m\n", " 70 0.1572 23.2% 2.5m\n", " 80 0.1315 23.2% 2.5m\n", " 90 0.1633 23.2% 2.4m\n", " 100 0.1552 23.2% 2.4m\n", " 110 0.1720 23.2% 2.4m\n", " 120 0.1982 23.2% 2.3m\n", " 130 0.1759 23.2% 2.3m\n", " 140 0.1416 23.2% 2.3m\n", " 150 0.1521 23.2% 2.2m\n", " 160 0.1742 23.2% 2.2m\n", " 170 0.1495 23.2% 2.2m\n", " 180 0.1729 23.2% 2.1m\n", " 190 0.2122 23.2% 2.1m\n", " 200 0.1784 23.2% 2.1m\n", " 210 0.1983 23.2% 2.0m\n", " 220 0.1683 23.2% 2.0m\n", " 230 0.1511 23.2% 2.0m\n", " 240 0.1664 23.2% 2.0m\n", " 250 0.1323 23.2% 1.9m\n", " 260 0.1859 23.2% 1.9m\n", " 270 0.1233 23.2% 1.9m\n", " 280 0.1440 23.2% 1.8m\n", " 290 0.1832 23.2% 1.8m\n", " 300 0.1623 23.2% 1.8m\n", " 310 0.1819 23.2% 1.7m\n", " 320 0.1775 23.2% 1.7m\n", " 330 0.1524 23.2% 1.7m\n", " 340 0.1851 23.2% 1.6m\n", " 350 0.1573 23.2% 1.6m\n", " 360 0.2056 23.2% 1.6m\n", " 370 0.1487 23.2% 1.6m\n", " 380 0.1903 23.2% 1.5m\n", " 390 0.1682 23.2% 1.5m\n", " 400 0.1594 23.2% 1.5m\n", " 410 0.1736 23.2% 1.4m\n", " 420 0.1379 23.2% 1.4m\n", " 430 0.1284 23.2% 1.4m\n", " 440 0.1627 23.2% 1.3m\n", " 450 0.1611 23.2% 1.3m\n", " 460 0.1521 23.2% 1.3m\n", " 470 0.1598 23.2% 1.2m\n", " 480 0.1900 23.2% 1.2m\n", " 490 0.1730 23.2% 1.2m\n", " 500 0.1358 23.2% 1.1m\n", " 510 0.2020 23.2% 1.1m\n", " 520 0.2029 23.2% 1.1m\n", " 530 0.1685 23.2% 1.0m\n", " 540 0.1588 23.2% 1.0m\n", " 550 0.1401 23.2% 1.0m\n", " 560 0.1637 23.2% 1.0m\n", " 570 0.1293 23.2% 0.9m\n", " 580 0.2091 23.2% 0.9m\n", " 590 0.1831 23.2% 0.9m\n", " 600 0.1800 23.2% 0.8m\n", " 610 0.2062 23.2% 0.8m\n", " 620 0.1656 23.2% 0.8m\n", " 630 0.1336 23.2% 0.7m\n", " 640 0.1581 23.2% 0.7m\n", " 650 0.1816 23.2% 0.7m\n", " 660 0.1663 23.2% 0.6m\n", " 670 0.1911 23.2% 0.6m\n", " 680 0.1945 23.2% 0.6m\n", " 690 0.1774 23.2% 0.5m\n", " 700 0.1689 23.2% 0.5m\n", " 710 0.2115 23.2% 0.5m\n", " 720 0.1439 23.2% 0.4m\n", " 730 0.1500 23.2% 0.4m\n", " 740 0.1703 23.2% 0.4m\n", " 750 0.1355 23.2% 0.4m\n", " 760 0.1738 23.2% 0.3m\n", " 770 0.1859 23.2% 0.3m\n", " 780 0.1782 23.2% 0.3m\n", " 790 0.1372 23.2% 0.2m\n", " 800 0.1704 23.2% 0.2m\n", " 810 0.1754 23.2% 0.2m\n", " 820 0.1996 23.2% 0.1m\n", " 830 0.1722 23.2% 0.1m\n", " 840 0.1713 23.2% 0.1m\n", " 850 0.1587 23.2% 0.0m\n", " 860 0.1688 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 94 COMPLETE in 163s\n", "\n", "โœ… EPOCH 94 SUMMARY\n", " โฑ๏ธ Time: 171s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16642 โ†’ Val=0.16403\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.5% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 17m\n", "\n", "๐Ÿ”„ Epoch 95/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1635 23.2% 2.7m\n", " 10 0.1516 23.2% 2.6m\n", " 20 0.1513 23.2% 2.7m\n", " 30 0.1838 23.2% 2.6m\n", " 40 0.1996 23.2% 2.6m\n", " 50 0.1959 23.2% 2.6m\n", " 60 0.2000 23.2% 2.5m\n", " 70 0.1558 23.2% 2.5m\n", " 80 0.1952 23.2% 2.5m\n", " 90 0.1972 23.2% 2.4m\n", " 100 0.1964 23.2% 2.4m\n", " 110 0.1584 23.2% 2.4m\n", " 120 0.1979 23.2% 2.3m\n", " 130 0.1485 23.2% 2.3m\n", " 140 0.1834 23.2% 2.3m\n", " 150 0.1809 23.2% 2.2m\n", " 160 0.1365 23.2% 2.2m\n", " 170 0.2236 23.2% 2.2m\n", " 180 0.1695 23.2% 2.1m\n", " 190 0.1895 23.2% 2.1m\n", " 200 0.1481 23.2% 2.1m\n", " 210 0.1749 23.2% 2.1m\n", " 220 0.1937 23.2% 2.0m\n", " 230 0.1590 23.2% 2.0m\n", " 240 0.1902 23.2% 2.0m\n", " 250 0.1448 23.2% 1.9m\n", " 260 0.1692 23.2% 1.9m\n", " 270 0.1263 23.2% 1.9m\n", " 280 0.1450 23.2% 1.8m\n", " 290 0.1597 23.2% 1.8m\n", " 300 0.1957 23.2% 1.8m\n", " 310 0.1765 23.2% 1.7m\n", " 320 0.2218 23.2% 1.7m\n", " 330 0.1665 23.2% 1.7m\n", " 340 0.1617 23.2% 1.6m\n", " 350 0.1437 23.2% 1.6m\n", " 360 0.1690 23.2% 1.6m\n", " 370 0.1760 23.2% 1.6m\n", " 380 0.1491 23.2% 1.5m\n", " 390 0.1415 23.2% 1.5m\n", " 400 0.1841 23.2% 1.5m\n", " 410 0.1824 23.2% 1.4m\n", " 420 0.1403 23.2% 1.4m\n", " 430 0.1825 23.2% 1.4m\n", " 440 0.1213 23.2% 1.3m\n", " 450 0.1508 23.2% 1.3m\n", " 460 0.1567 23.2% 1.3m\n", " 470 0.1325 23.2% 1.2m\n", " 480 0.1805 23.2% 1.2m\n", " 490 0.1282 23.2% 1.2m\n", " 500 0.1940 23.2% 1.1m\n", " 510 0.1706 23.2% 1.1m\n", " 520 0.1539 23.2% 1.1m\n", " 530 0.1924 23.2% 1.0m\n", " 540 0.1596 23.2% 1.0m\n", " 550 0.1378 23.2% 1.0m\n", " 560 0.1674 23.2% 1.0m\n", " 570 0.1386 23.2% 0.9m\n", " 580 0.1411 23.2% 0.9m\n", " 590 0.1269 23.2% 0.9m\n", " 600 0.2017 23.2% 0.8m\n", " 610 0.1846 23.2% 0.8m\n", " 620 0.1646 23.2% 0.8m\n", " 630 0.1371 23.2% 0.7m\n", " 640 0.1270 23.2% 0.7m\n", " 650 0.1447 23.2% 0.7m\n", " 660 0.1459 23.2% 0.6m\n", " 670 0.1771 23.2% 0.6m\n", " 680 0.1958 23.2% 0.6m\n", " 690 0.1706 23.2% 0.5m\n", " 700 0.1783 23.2% 0.5m\n", " 710 0.1602 23.2% 0.5m\n", " 720 0.1428 23.2% 0.5m\n", " 730 0.1272 23.2% 0.4m\n", " 740 0.1866 23.2% 0.4m\n", " 750 0.2257 23.2% 0.4m\n", " 760 0.1987 23.2% 0.3m\n", " 770 0.1421 23.2% 0.3m\n", " 780 0.1392 23.2% 0.3m\n", " 790 0.1416 23.2% 0.2m\n", " 800 0.1762 23.2% 0.2m\n", " 810 0.1520 23.2% 0.2m\n", " 820 0.2048 23.2% 0.1m\n", " 830 0.1829 23.2% 0.1m\n", " 840 0.1423 23.2% 0.1m\n", " 850 0.1807 23.2% 0.0m\n", " 860 0.2003 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 95 COMPLETE in 163s\n", "\n", "โœ… EPOCH 95 SUMMARY\n", " โฑ๏ธ Time: 171s (2.9m)\n", " ๐Ÿ“‰ Loss: Train=0.16650 โ†’ Val=0.16388\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.4% EJ=86.7% Overall=81.3%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 14m\n", "\n", "๐Ÿ”„ Epoch 96/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1438 23.2% 2.7m\n", " 10 0.1455 23.2% 2.8m\n", " 20 0.1749 23.2% 2.7m\n", " 30 0.2083 23.2% 2.7m\n", " 40 0.1603 23.2% 2.6m\n", " 50 0.1399 23.2% 2.6m\n", " 60 0.1778 23.2% 2.6m\n", " 70 0.1538 23.2% 2.5m\n", " 80 0.2118 23.2% 2.5m\n", " 90 0.1396 23.2% 2.5m\n", " 100 0.1585 23.2% 2.4m\n", " 110 0.1674 23.2% 2.4m\n", " 120 0.1620 23.2% 2.4m\n", " 130 0.1670 23.2% 2.3m\n", " 140 0.1774 23.2% 2.3m\n", " 150 0.1605 23.2% 2.2m\n", " 160 0.2029 23.2% 2.2m\n", " 170 0.1221 23.2% 2.2m\n", " 180 0.1487 23.2% 2.1m\n", " 190 0.1869 23.2% 2.1m\n", " 200 0.1600 23.2% 2.1m\n", " 210 0.1617 23.2% 2.1m\n", " 220 0.1745 23.2% 2.0m\n", " 230 0.2005 23.2% 2.0m\n", " 240 0.1590 23.2% 2.0m\n", " 250 0.1661 23.2% 1.9m\n", " 260 0.1884 23.2% 1.9m\n", " 270 0.1086 23.2% 1.9m\n", " 280 0.1462 23.2% 1.8m\n", " 290 0.1428 23.2% 1.8m\n", " 300 0.1638 23.2% 1.8m\n", " 310 0.1937 23.2% 1.7m\n", " 320 0.1969 23.2% 1.7m\n", " 330 0.1573 23.2% 1.7m\n", " 340 0.1646 23.2% 1.6m\n", " 350 0.1612 23.2% 1.6m\n", " 360 0.2004 23.2% 1.6m\n", " 370 0.1217 23.2% 1.5m\n", " 380 0.1873 23.2% 1.5m\n", " 390 0.1811 23.2% 1.5m\n", " 400 0.1457 23.2% 1.5m\n", " 410 0.1847 23.2% 1.4m\n", " 420 0.1700 23.2% 1.4m\n", " 430 0.1528 23.2% 1.4m\n", " 440 0.1389 23.2% 1.3m\n", " 450 0.1592 23.2% 1.3m\n", " 460 0.1681 23.2% 1.3m\n", " 470 0.1424 23.2% 1.2m\n", " 480 0.2193 23.2% 1.2m\n", " 490 0.1334 23.2% 1.2m\n", " 500 0.1585 23.2% 1.1m\n", " 510 0.1607 23.2% 1.1m\n", " 520 0.1534 23.2% 1.1m\n", " 530 0.1605 23.2% 1.0m\n", " 540 0.2289 23.2% 1.0m\n", " 550 0.1843 23.2% 1.0m\n", " 560 0.1935 23.2% 0.9m\n", " 570 0.1798 23.2% 0.9m\n", " 580 0.1924 23.2% 0.9m\n", " 590 0.1685 23.2% 0.9m\n", " 600 0.1480 23.2% 0.8m\n", " 610 0.1555 23.2% 0.8m\n", " 620 0.1798 23.2% 0.8m\n", " 630 0.1884 23.2% 0.7m\n", " 640 0.1738 23.2% 0.7m\n", " 650 0.1587 23.2% 0.7m\n", " 660 0.1844 23.2% 0.6m\n", " 670 0.1955 23.2% 0.6m\n", " 680 0.1683 23.2% 0.6m\n", " 690 0.1457 23.2% 0.5m\n", " 700 0.1498 23.2% 0.5m\n", " 710 0.1455 23.2% 0.5m\n", " 720 0.1448 23.2% 0.4m\n", " 730 0.1848 23.2% 0.4m\n", " 740 0.1875 23.2% 0.4m\n", " 750 0.1932 23.2% 0.4m\n", " 760 0.1657 23.2% 0.3m\n", " 770 0.2021 23.2% 0.3m\n", " 780 0.1802 23.2% 0.3m\n", " 790 0.1460 23.2% 0.2m\n", " 800 0.1683 23.2% 0.2m\n", " 810 0.1632 23.2% 0.2m\n", " 820 0.1377 23.2% 0.1m\n", " 830 0.1811 23.2% 0.1m\n", " 840 0.1794 23.2% 0.1m\n", " 850 0.1801 23.2% 0.0m\n", " 860 0.1628 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 96 COMPLETE in 162s\n", "\n", "โœ… EPOCH 96 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16646 โ†’ Val=0.16379\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.5% EJ=86.7% Overall=81.3%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 11m\n", "\n", "๐Ÿ”„ Epoch 97/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1647 23.2% 2.7m\n", " 10 0.1905 23.2% 2.6m\n", " 20 0.2009 23.2% 2.6m\n", " 30 0.1909 23.2% 2.6m\n", " 40 0.1880 23.2% 2.6m\n", " 50 0.1614 23.2% 2.5m\n", " 60 0.1261 23.2% 2.5m\n", " 70 0.1235 23.2% 2.5m\n", " 80 0.1391 23.2% 2.4m\n", " 90 0.1507 23.2% 2.4m\n", " 100 0.1659 23.2% 2.4m\n", " 110 0.1889 23.2% 2.4m\n", " 120 0.1652 23.2% 2.3m\n", " 130 0.1825 23.2% 2.3m\n", " 140 0.1372 23.2% 2.3m\n", " 150 0.1249 23.2% 2.2m\n", " 160 0.1861 23.2% 2.2m\n", " 170 0.2181 23.2% 2.2m\n", " 180 0.1380 23.2% 2.1m\n", " 190 0.1772 23.2% 2.1m\n", " 200 0.1646 23.2% 2.1m\n", " 210 0.1834 23.2% 2.0m\n", " 220 0.1730 23.2% 2.0m\n", " 230 0.1534 23.2% 2.0m\n", " 240 0.1204 23.2% 2.0m\n", " 250 0.1681 23.2% 1.9m\n", " 260 0.1724 23.2% 1.9m\n", " 270 0.1896 23.2% 1.9m\n", " 280 0.2123 23.2% 1.8m\n", " 290 0.1613 23.2% 1.8m\n", " 300 0.1795 23.2% 1.8m\n", " 310 0.1674 23.2% 1.7m\n", " 320 0.1456 23.2% 1.7m\n", " 330 0.2152 23.2% 1.7m\n", " 340 0.1621 23.2% 1.6m\n", " 350 0.1442 23.2% 1.6m\n", " 360 0.1346 23.2% 1.6m\n", " 370 0.1959 23.2% 1.5m\n", " 380 0.1573 23.2% 1.5m\n", " 390 0.2487 23.2% 1.5m\n", " 400 0.1892 23.2% 1.4m\n", " 410 0.1747 23.2% 1.4m\n", " 420 0.1614 23.2% 1.4m\n", " 430 0.2166 23.2% 1.4m\n", " 440 0.1686 23.2% 1.3m\n", " 450 0.1519 23.2% 1.3m\n", " 460 0.1021 23.2% 1.3m\n", " 470 0.1364 23.2% 1.2m\n", " 480 0.1410 23.2% 1.2m\n", " 490 0.1271 23.2% 1.2m\n", " 500 0.1420 23.2% 1.1m\n", " 510 0.1684 23.2% 1.1m\n", " 520 0.1987 23.2% 1.1m\n", " 530 0.1727 23.2% 1.0m\n", " 540 0.1820 23.2% 1.0m\n", " 550 0.1655 23.2% 1.0m\n", " 560 0.1759 23.2% 0.9m\n", " 570 0.1975 23.2% 0.9m\n", " 580 0.1829 23.2% 0.9m\n", " 590 0.1898 23.2% 0.9m\n", " 600 0.1891 23.2% 0.8m\n", " 610 0.1620 23.2% 0.8m\n", " 620 0.1686 23.2% 0.8m\n", " 630 0.2096 23.2% 0.7m\n", " 640 0.1533 23.2% 0.7m\n", " 650 0.1964 23.2% 0.7m\n", " 660 0.1882 23.2% 0.6m\n", " 670 0.2046 23.2% 0.6m\n", " 680 0.1454 23.2% 0.6m\n", " 690 0.1718 23.2% 0.5m\n", " 700 0.1329 23.2% 0.5m\n", " 710 0.1530 23.2% 0.5m\n", " 720 0.1843 23.2% 0.4m\n", " 730 0.1684 23.2% 0.4m\n", " 740 0.1963 23.2% 0.4m\n", " 750 0.1658 23.2% 0.4m\n", " 760 0.1495 23.2% 0.3m\n", " 770 0.1484 23.2% 0.3m\n", " 780 0.1316 23.2% 0.3m\n", " 790 0.1516 23.2% 0.2m\n", " 800 0.1753 23.2% 0.2m\n", " 810 0.1489 23.2% 0.2m\n", " 820 0.1598 23.2% 0.1m\n", " 830 0.1787 23.2% 0.1m\n", " 840 0.1328 23.2% 0.1m\n", " 850 0.1288 23.2% 0.0m\n", " 860 0.1638 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 97 COMPLETE in 162s\n", "\n", "โœ… EPOCH 97 SUMMARY\n", " โฑ๏ธ Time: 170s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16648 โ†’ Val=0.16402\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.5% EJ=86.7% Overall=81.2%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 9m\n", "\n", "๐Ÿ”„ Epoch 98/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1779 23.2% 2.7m\n", " 10 0.1657 23.2% 2.7m\n", " 20 0.1455 23.2% 2.7m\n", " 30 0.1772 23.2% 2.6m\n", " 40 0.1553 23.2% 2.6m\n", " 50 0.2412 23.2% 2.6m\n", " 60 0.1309 23.2% 2.5m\n", " 70 0.2068 23.2% 2.5m\n", " 80 0.1673 23.2% 2.5m\n", " 90 0.1614 23.2% 2.4m\n", " 100 0.1553 23.2% 2.4m\n", " 110 0.1685 23.2% 2.4m\n", " 120 0.1585 23.2% 2.3m\n", " 130 0.1824 23.2% 2.3m\n", " 140 0.1356 23.2% 2.3m\n", " 150 0.1533 23.2% 2.3m\n", " 160 0.1642 23.2% 2.2m\n", " 170 0.1463 23.2% 2.2m\n", " 180 0.1536 23.2% 2.2m\n", " 190 0.1737 23.2% 2.1m\n", " 200 0.1489 23.2% 2.1m\n", " 210 0.2103 23.2% 2.1m\n", " 220 0.1454 23.2% 2.0m\n", " 230 0.1809 23.2% 2.0m\n", " 240 0.2231 23.2% 2.0m\n", " 250 0.2044 23.2% 1.9m\n", " 260 0.1612 23.2% 1.9m\n", " 270 0.1627 23.2% 1.9m\n", " 280 0.1996 23.2% 1.8m\n", " 290 0.1794 23.2% 1.8m\n", " 300 0.1611 23.2% 1.8m\n", " 310 0.1655 23.2% 1.7m\n", " 320 0.1713 23.2% 1.7m\n", " 330 0.1700 23.2% 1.7m\n", " 340 0.1797 23.2% 1.6m\n", " 350 0.1886 23.2% 1.6m\n", " 360 0.1797 23.2% 1.6m\n", " 370 0.1366 23.2% 1.5m\n", " 380 0.1149 23.2% 1.5m\n", " 390 0.1819 23.2% 1.5m\n", " 400 0.1397 23.2% 1.5m\n", " 410 0.1863 23.2% 1.4m\n", " 420 0.1635 23.2% 1.4m\n", " 430 0.1533 23.2% 1.4m\n", " 440 0.1663 23.2% 1.3m\n", " 450 0.1695 23.2% 1.3m\n", " 460 0.1863 23.2% 1.3m\n", " 470 0.1573 23.2% 1.2m\n", " 480 0.1397 23.2% 1.2m\n", " 490 0.1731 23.2% 1.2m\n", " 500 0.1874 23.2% 1.1m\n", " 510 0.1463 23.2% 1.1m\n", " 520 0.1682 23.2% 1.1m\n", " 530 0.2214 23.2% 1.0m\n", " 540 0.1866 23.2% 1.0m\n", " 550 0.1634 23.2% 1.0m\n", " 560 0.1945 23.2% 1.0m\n", " 570 0.1781 23.2% 0.9m\n", " 580 0.1758 23.2% 0.9m\n", " 590 0.1734 23.2% 0.9m\n", " 600 0.1825 23.2% 0.8m\n", " 610 0.1805 23.2% 0.8m\n", " 620 0.1415 23.2% 0.8m\n", " 630 0.1418 23.2% 0.7m\n", " 640 0.1548 23.2% 0.7m\n", " 650 0.1823 23.2% 0.7m\n", " 660 0.1523 23.2% 0.6m\n", " 670 0.1756 23.2% 0.6m\n", " 680 0.1460 23.2% 0.6m\n", " 690 0.1849 23.2% 0.5m\n", " 700 0.1636 23.2% 0.5m\n", " 710 0.1959 23.2% 0.5m\n", " 720 0.1691 23.2% 0.4m\n", " 730 0.2137 23.2% 0.4m\n", " 740 0.1711 23.2% 0.4m\n", " 750 0.1636 23.2% 0.4m\n", " 760 0.1710 23.2% 0.3m\n", " 770 0.1810 23.2% 0.3m\n", " 780 0.1812 23.2% 0.3m\n", " 790 0.1612 23.2% 0.2m\n", " 800 0.2138 23.2% 0.2m\n", " 810 0.1857 23.2% 0.2m\n", " 820 0.1932 23.2% 0.1m\n", " 830 0.1617 23.2% 0.1m\n", " 840 0.1774 23.2% 0.1m\n", " 850 0.1579 23.2% 0.0m\n", " 860 0.1719 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 98 COMPLETE in 163s\n", "\n", "โœ… EPOCH 98 SUMMARY\n", " โฑ๏ธ Time: 171s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16634 โ†’ Val=0.16388\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.5% EJ=86.7% Overall=81.3%\n", " โš ๏ธ No improvement for 10/30\n", " โฑ๏ธ Remaining ETA โ‰ˆ 6m\n", "\n", "๐Ÿ”„ Epoch 99/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1941 23.2% 2.7m\n", " 10 0.1761 23.2% 2.7m\n", " 20 0.1436 23.2% 2.7m\n", " 30 0.1630 23.2% 2.7m\n", " 40 0.1470 23.2% 2.6m\n", " 50 0.1399 23.2% 2.6m\n", " 60 0.1535 23.2% 2.5m\n", " 70 0.1766 23.2% 2.5m\n", " 80 0.2055 23.2% 2.5m\n", " 90 0.1620 23.2% 2.4m\n", " 100 0.0960 23.2% 2.4m\n", " 110 0.1716 23.2% 2.4m\n", " 120 0.1680 23.2% 2.3m\n", " 130 0.1396 23.2% 2.3m\n", " 140 0.1484 23.2% 2.3m\n", " 150 0.1880 23.2% 2.2m\n", " 160 0.1627 23.2% 2.2m\n", " 170 0.1820 23.2% 2.2m\n", " 180 0.1500 23.2% 2.1m\n", " 190 0.2018 23.2% 2.1m\n", " 200 0.1708 23.2% 2.1m\n", " 210 0.1480 23.2% 2.1m\n", " 220 0.2187 23.2% 2.0m\n", " 230 0.1584 23.2% 2.0m\n", " 240 0.1480 23.2% 2.0m\n", " 250 0.1351 23.2% 1.9m\n", " 260 0.1668 23.2% 1.9m\n", " 270 0.1794 23.2% 1.9m\n", " 280 0.1613 23.2% 1.8m\n", " 290 0.1671 23.2% 1.8m\n", " 300 0.1912 23.2% 1.8m\n", " 310 0.1516 23.2% 1.7m\n", " 320 0.1488 23.2% 1.7m\n", " 330 0.1657 23.2% 1.7m\n", " 340 0.1249 23.2% 1.6m\n", " 350 0.1597 23.2% 1.6m\n", " 360 0.1488 23.2% 1.6m\n", " 370 0.1813 23.2% 1.6m\n", " 380 0.1324 23.2% 1.5m\n", " 390 0.1921 23.2% 1.5m\n", " 400 0.1664 23.2% 1.5m\n", " 410 0.1443 23.2% 1.4m\n", " 420 0.1895 23.2% 1.4m\n", " 430 0.1867 23.2% 1.4m\n", " 440 0.2018 23.2% 1.3m\n", " 450 0.1425 23.2% 1.3m\n", " 460 0.1556 23.2% 1.3m\n", " 470 0.1265 23.2% 1.2m\n", " 480 0.1904 23.2% 1.2m\n", " 490 0.1497 23.2% 1.2m\n", " 500 0.1387 23.2% 1.1m\n", " 510 0.2107 23.2% 1.1m\n", " 520 0.1441 23.2% 1.1m\n", " 530 0.1644 23.2% 1.0m\n", " 540 0.1835 23.2% 1.0m\n", " 550 0.1201 23.2% 1.0m\n", " 560 0.1688 23.2% 1.0m\n", " 570 0.1652 23.2% 0.9m\n", " 580 0.1776 23.2% 0.9m\n", " 590 0.1430 23.2% 0.9m\n", " 600 0.1955 23.2% 0.8m\n", " 610 0.1810 23.2% 0.8m\n", " 620 0.1491 23.2% 0.8m\n", " 630 0.1145 23.2% 0.7m\n", " 640 0.1922 23.2% 0.7m\n", " 650 0.2135 23.2% 0.7m\n", " 660 0.1491 23.2% 0.6m\n", " 670 0.1709 23.2% 0.6m\n", " 680 0.1698 23.2% 0.6m\n", " 690 0.1414 23.2% 0.5m\n", " 700 0.1722 23.2% 0.5m\n", " 710 0.1610 23.2% 0.5m\n", " 720 0.1344 23.2% 0.4m\n", " 730 0.1824 23.2% 0.4m\n", " 740 0.1557 23.2% 0.4m\n", " 750 0.1567 23.2% 0.4m\n", " 760 0.1394 23.2% 0.3m\n", " 770 0.1994 23.2% 0.3m\n", " 780 0.1793 23.2% 0.3m\n", " 790 0.1712 23.2% 0.2m\n", " 800 0.1583 23.2% 0.2m\n", " 810 0.1396 23.2% 0.2m\n", " 820 0.1456 23.2% 0.1m\n", " 830 0.1237 23.2% 0.1m\n", " 840 0.1819 23.2% 0.1m\n", " 850 0.1864 23.2% 0.0m\n", " 860 0.1641 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 99 COMPLETE in 163s\n", "\n", "โœ… EPOCH 99 SUMMARY\n", " โฑ๏ธ Time: 171s (2.8m)\n", " ๐Ÿ“‰ Loss: Train=0.16643 โ†’ Val=0.16373\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.5% EJ=86.7% Overall=81.3%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 3m\n", "\n", "๐Ÿ”„ Epoch 100/100\n", " Batch Loss GPU ETA\n", " ----------------------------------------\n", " 0 0.1864 23.2% 2.7m\n", " 10 0.1964 23.2% 2.7m\n", " 20 0.1488 23.2% 2.7m\n", " 30 0.1378 23.2% 2.6m\n", " 40 0.1516 23.2% 2.6m\n", " 50 0.1569 23.2% 2.6m\n", " 60 0.1726 23.2% 2.5m\n", " 70 0.1556 23.2% 2.5m\n", " 80 0.1906 23.2% 2.5m\n", " 90 0.1568 23.2% 2.4m\n", " 100 0.1593 23.2% 2.4m\n", " 110 0.1425 23.2% 2.4m\n", " 120 0.1597 23.2% 2.4m\n", " 130 0.1718 23.2% 2.3m\n", " 140 0.1701 23.2% 2.3m\n", " 150 0.1744 23.2% 2.3m\n", " 160 0.1710 23.2% 2.2m\n", " 170 0.1492 23.2% 2.2m\n", " 180 0.1485 23.2% 2.2m\n", " 190 0.1578 23.2% 2.1m\n", " 200 0.1245 23.2% 2.1m\n", " 210 0.1356 23.2% 2.1m\n", " 220 0.1461 23.2% 2.0m\n", " 230 0.2050 23.2% 2.0m\n", " 240 0.1628 23.2% 2.0m\n", " 250 0.1375 23.2% 1.9m\n", " 260 0.2023 23.2% 1.9m\n", " 270 0.1679 23.2% 1.9m\n", " 280 0.1630 23.2% 1.8m\n", " 290 0.1582 23.2% 1.8m\n", " 300 0.1727 23.2% 1.8m\n", " 310 0.2252 23.2% 1.8m\n", " 320 0.1409 23.2% 1.7m\n", " 330 0.1716 23.2% 1.7m\n", " 340 0.1645 23.2% 1.7m\n", " 350 0.1837 23.2% 1.6m\n", " 360 0.1705 23.2% 1.6m\n", " 370 0.1386 23.2% 1.6m\n", " 380 0.1629 23.2% 1.5m\n", " 390 0.1754 23.2% 1.5m\n", " 400 0.1479 23.2% 1.5m\n", " 410 0.1306 23.2% 1.4m\n", " 420 0.1792 23.2% 1.4m\n", " 430 0.1969 23.2% 1.4m\n", " 440 0.1549 23.2% 1.3m\n", " 450 0.1668 23.2% 1.3m\n", " 460 0.1810 23.2% 1.3m\n", " 470 0.1804 23.2% 1.2m\n", " 480 0.1374 23.2% 1.2m\n", " 490 0.1800 23.2% 1.2m\n", " 500 0.1226 23.2% 1.2m\n", " 510 0.1769 23.2% 1.1m\n", " 520 0.1580 23.2% 1.1m\n", " 530 0.1520 23.2% 1.1m\n", " 540 0.1351 23.2% 1.0m\n", " 550 0.1477 23.2% 1.0m\n", " 560 0.1933 23.2% 1.0m\n", " 570 0.1746 23.2% 0.9m\n", " 580 0.2185 23.2% 0.9m\n", " 590 0.1226 23.2% 0.9m\n", " 600 0.1981 23.2% 0.8m\n", " 610 0.1304 23.2% 0.8m\n", " 620 0.1474 23.2% 0.8m\n", " 630 0.1835 23.2% 0.7m\n", " 640 0.1468 23.2% 0.7m\n", " 650 0.1605 23.2% 0.7m\n", " 660 0.1678 23.2% 0.6m\n", " 670 0.1721 23.2% 0.6m\n", " 680 0.2143 23.2% 0.6m\n", " 690 0.1345 23.2% 0.5m\n", " 700 0.1555 23.2% 0.5m\n", " 710 0.1372 23.2% 0.5m\n", " 720 0.1445 23.2% 0.5m\n", " 730 0.1967 23.2% 0.4m\n", " 740 0.1439 23.2% 0.4m\n", " 750 0.1417 23.2% 0.4m\n", " 760 0.1757 23.2% 0.3m\n", " 770 0.1839 23.2% 0.3m\n", " 780 0.1469 23.2% 0.3m\n", " 790 0.1524 23.2% 0.2m\n", " 800 0.1305 23.2% 0.2m\n", " 810 0.1927 23.2% 0.2m\n", " 820 0.1818 23.2% 0.1m\n", " 830 0.1653 23.2% 0.1m\n", " 840 0.1829 23.2% 0.1m\n", " 850 0.1795 23.2% 0.0m\n", " 860 0.1750 23.2% 0.0m\n", " ----------------------------------------\n", " โœ… Epoch 100 COMPLETE in 164s\n", "\n", "โœ… EPOCH 100 SUMMARY\n", " โฑ๏ธ Time: 173s (2.9m)\n", " ๐Ÿ“‰ Loss: Train=0.16634 โ†’ Val=0.16393\n", " ๐Ÿ“Š Acc: EC=81.6% EL=75.5% EJ=86.7% Overall=81.3%\n", " โฑ๏ธ Remaining ETA โ‰ˆ 0m\n" ] } ], "source": [ "import gc\n", "import os\n", "import time\n", "import torch\n", "import matplotlib.pyplot as plt\n", "\n", "# ================= VRAM RESET =================\n", "torch.cuda.empty_cache()\n", "gc.collect()\n", "os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n", "\n", "# AMP scaler (mixed precision)\n", "scaler = torch.amp.GradScaler(\"cuda\")\n", "\n", "print(\"=\" * 80)\n", "print(\"๐Ÿš€ STAGE 1 TRAINING STARTED - LIVE MONITORING MODE\")\n", "print(\"=\" * 80)\n", "\n", "train_losses, val_losses, val_accuracies = [], [], []\n", "best_val_loss, patience_counter = float('inf'), 0\n", "\n", "# GPU memory monitoring\n", "if device.type == 'cuda':\n", " initial_mem = torch.cuda.memory_allocated() / 1e9\n", " total_vram = torch.cuda.get_device_properties(0).total_memory / 1e9\n", " print(f\"๐Ÿ“Š Initial GPU memory: {initial_mem:.2f} GB / {total_vram:.1f} GB\")\n", "else:\n", " initial_mem, total_vram = 0, 0\n", "\n", "print(f\"๐Ÿ“ˆ Train batches: {len(train_loader)} | Val batches: {len(val_loader)}\")\n", "print()\n", "\n", "# =====================================================\n", "# TRAINING FUNCTION\n", "# =====================================================\n", "def train_one_epoch_live(model, loader, optimizer, device, epoch):\n", " model.train()\n", "\n", " total_loss = 0.0\n", " start_epoch = time.time()\n", "\n", " print(f\" {'Batch':<6}{'Loss':<10}{'GPU':<10}{'ETA'}\")\n", " print(\" \" + \"-\" * 40)\n", "\n", " for batch_idx, (imgs, labels) in enumerate(loader):\n", "\n", " imgs = imgs.to(device, non_blocking=True)\n", " labels = labels.to(device, non_blocking=True)\n", "\n", " optimizer.zero_grad(set_to_none=True)\n", "\n", " # ===== Mixed Precision Forward =====\n", " with torch.amp.autocast(\"cuda\"):\n", " preds = model(imgs)\n", " loss = criterion(preds, labels)\n", "\n", " # ===== Backprop =====\n", " scaler.scale(loss).backward()\n", " scaler.step(optimizer)\n", " scaler.update()\n", "\n", " total_loss += loss.item()\n", "\n", " # ===== progress printing =====\n", " if batch_idx % 10 == 0:\n", " elapsed = time.time() - start_epoch\n", " batches_per_sec = (batch_idx + 1) / elapsed\n", " eta = (len(loader) - batch_idx) / batches_per_sec\n", "\n", " gpu_mem = torch.cuda.memory_allocated() / 1e9 if device.type == \"cuda\" else 0\n", " gpu_percent = (gpu_mem / total_vram * 100) if total_vram else 0\n", "\n", " print(\n", " f\" {batch_idx:<6}{loss.item():<10.4f}\"\n", " f\"{gpu_percent:>6.1f}% \"\n", " f\"{eta/60:.1f}m\"\n", " )\n", "\n", " avg_loss = total_loss / len(loader)\n", "\n", " print(\" \" + \"-\" * 40)\n", " print(f\" โœ… Epoch {epoch} COMPLETE in {time.time()-start_epoch:.0f}s\")\n", "\n", " return avg_loss\n", "\n", "\n", "# =====================================================\n", "# MAIN TRAINING LOOP\n", "# =====================================================\n", "for epoch in range(1, NUM_EPOCHS_STAGE1 + 1):\n", "\n", " # ===== Unfreeze backbone =====\n", " if epoch == 5:\n", " print(\"๐Ÿ”“ Unfreezing backbone for fine-tuning\")\n", "\n", " for param in model.parameters():\n", " param.requires_grad = True\n", "\n", " optimizer = torch.optim.AdamW(\n", " model.parameters(),\n", " lr=3e-5,\n", " weight_decay=1e-4\n", " )\n", "\n", " print(f\"\\n๐Ÿ”„ Epoch {epoch}/{NUM_EPOCHS_STAGE1}\")\n", "\n", " epoch_start = time.time()\n", "\n", " # ===== TRAIN =====\n", " train_loss = train_one_epoch_live(model, train_loader, optimizer, device, epoch)\n", " train_losses.append(train_loss)\n", "\n", " # ===== VALIDATE =====\n", " val_loss, val_acc = validate(model, val_loader, device)\n", "\n", " val_losses.append(val_loss)\n", " val_accuracies.append(val_acc)\n", "\n", " epoch_time = time.time() - epoch_start\n", "\n", " print(f\"\\nโœ… EPOCH {epoch} SUMMARY\")\n", " print(f\" โฑ๏ธ Time: {epoch_time:.0f}s ({epoch_time/60:.1f}m)\")\n", " print(f\" ๐Ÿ“‰ Loss: Train={train_loss:.5f} โ†’ Val={val_loss:.5f}\")\n", " print(\n", " f\" ๐Ÿ“Š Acc: EC={val_acc['EC_acc']:4.1f}% \"\n", " f\"EL={val_acc['EL_acc']:4.1f}% \"\n", " f\"EJ={val_acc['EJ_acc']:4.1f}% \"\n", " f\"Overall={val_acc['overall_acc']:4.1f}%\"\n", " )\n", "\n", " # ===== Scheduler step AFTER epoch =====\n", " if scheduler:\n", " scheduler.step()\n", "\n", " # ===== Save best model =====\n", " if val_loss < best_val_loss:\n", "\n", " best_val_loss = val_loss\n", " patience_counter = 0\n", "\n", " torch.save(\n", " {\n", " \"epoch\": epoch,\n", " \"model_state_dict\": model.state_dict(),\n", " \"optimizer_state_dict\": optimizer.state_dict(),\n", " \"scheduler_state_dict\": scheduler.state_dict() if scheduler else None,\n", " \"train_loss\": train_loss,\n", " \"val_loss\": val_loss,\n", " \"val_acc\": val_acc,\n", " },\n", " \"models/checkpoints/stage1_best.pt\",\n", " )\n", "\n", " print(f\" โญ NEW BEST MODEL SAVED (val_loss={val_loss:.5f})\")\n", "\n", " else:\n", " patience_counter += 1\n", " if patience_counter % 5 == 0:\n", " print(f\" โš ๏ธ No improvement for {patience_counter}/{PATIENCE}\")\n", "\n", " if patience_counter >= PATIENCE:\n", " print(f\"\\n๐Ÿ EARLY STOPPING at epoch {epoch}\")\n", " break\n", "\n", " remaining_epochs = NUM_EPOCHS_STAGE1 - epoch\n", " eta_total = epoch_time * remaining_epochs / 60\n", "\n", " print(f\" โฑ๏ธ Remaining ETA โ‰ˆ {eta_total:.0f}m\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.7 Plot Stage 1 Training Curves\n", "\n", "Visualize the training and validation loss over epochs to check for\n", "convergence and overfitting." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Training curves saved to results/figures/stage1_training.png\n" ] } ], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", "\n", "# Loss curves\n", "axes[0].plot(train_losses, label='Train Loss', alpha=0.8)\n", "axes[0].plot(val_losses, label='Val Loss', alpha=0.8)\n", "axes[0].set_xlabel('Epoch')\n", "axes[0].set_ylabel('MSE Loss')\n", "axes[0].set_title('Stage 1: Loss Curves')\n", "axes[0].legend()\n", "axes[0].set_yscale('log')\n", "axes[0].grid(True, alpha=0.3)\n", "\n", "# Accuracy curves\n", "epochs_range = range(1, len(val_accuracies) + 1)\n", "axes[1].plot(epochs_range, [a['EC_acc'] for a in val_accuracies], label='EC', alpha=0.8)\n", "axes[1].plot(epochs_range, [a['EL_acc'] for a in val_accuracies], label='EL', alpha=0.8)\n", "axes[1].plot(epochs_range, [a['EJ_acc'] for a in val_accuracies], label='EJ', alpha=0.8)\n", "axes[1].plot(epochs_range, [a['overall_acc'] for a in val_accuracies], label='Overall', \n", " linewidth=2, color='black', linestyle='--')\n", "axes[1].set_xlabel('Epoch')\n", "axes[1].set_ylabel('Accuracy (%)')\n", "axes[1].set_title('Stage 1: Validation Accuracy')\n", "axes[1].legend()\n", "axes[1].grid(True, alpha=0.3)\n", "\n", "plt.tight_layout()\n", "plt.savefig('results/figures/stage1_training.png', dpi=150)\n", "plt.show()\n", "print(\"Training curves saved to results/figures/stage1_training.png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## โœ… Stage 1 Complete\n", "\n", "The pre-trained model checkpoint is saved at `models/checkpoints/stage1_best.pt`.\n", "\n", "**Next**: Open **Notebook 3** to fine-tune on the dispersive readout dataset (Stage 2)." ] } ], "metadata": { "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": "3.13.2" } }, "nbformat": 4, "nbformat_minor": 5 }