Upload folder using huggingface_hub

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
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+confusion_matrix_resnet18.png filter=lfs diff=lfs merge=lfs -text
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+wandb/run-20250718_041408-zryhujpe/run-zryhujpe.wandb filter=lfs diff=lfs merge=lfs -text

.gradio/certificate.pem

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README.md

@@ -1,12 +1,6 @@
 ---
-title: CNN Benchmark
-emoji: ⚡
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 5.38.0
+title: cnn_benchmark
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 5.37.0
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,151 @@
+import gradio as gr
+import torch
+import torch.nn.functional as F
+from torchvision import transforms
+from PIL import Image
+import numpy as np
+from models.custom_cnn import create_custom_cnn
+from models.resnet18 import load_resnet18
+from utils.data_loader import get_cifar10_info
+
+# CIFAR-10 preprocessing
+CIFAR10_MEAN = (0.4914, 0.4822, 0.4465)
+CIFAR10_STD = (0.2023, 0.1994, 0.2010)
+
+def preprocess_image(image):
+    """Preprocess uploaded image for CIFAR-10 model."""
+    transform = transforms.Compose([
+        transforms.Resize((32, 32)),
+        transforms.ToTensor(),
+        transforms.Normalize(CIFAR10_MEAN, CIFAR10_STD)
+    ])
+    
+    if image.mode != 'RGB':
+        image = image.convert('RGB')
+    
+    return transform(image).unsqueeze(0)
+
+def predict_image(image, model_choice="CustomCNN"):
+    """
+    Predict class label for uploaded image.
+    
+    Args:
+        image: PIL Image uploaded by user
+        model_choice: Which model to use for prediction
+        
+    Returns:
+        str: Formatted prediction result
+    """
+    try:
+        # Load model
+        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        
+        if model_choice == "CustomCNN":
+            model = create_custom_cnn()
+            model_path = "best_model_custom.pth"
+        else:
+            model = load_resnet18()
+            model_path = "best_model_resnet18.pth"
+        
+        # Load trained weights
+        try:
+            checkpoint = torch.load(model_path, map_location=device)
+            model.load_state_dict(checkpoint['model_state_dict'])
+        except FileNotFoundError:
+            return f"❌ Model weights not found: {model_path}\nTrain the model first!"
+        
+        model.to(device)
+        model.eval()
+        
+        # Preprocess image
+        input_tensor = preprocess_image(image).to(device)
+        
+        # Make prediction
+        with torch.no_grad():
+            outputs = model(input_tensor)
+            probabilities = F.softmax(outputs, dim=1)
+            confidence, predicted = torch.max(probabilities, 1)
+        
+        # Get class info
+        cifar10_info = get_cifar10_info()
+        class_names = cifar10_info['class_names']
+        predicted_class = class_names[predicted.item()]
+        confidence_score = confidence.item() * 100
+        
+        # Format result with top-3 predictions
+        top3_prob, top3_indices = torch.topk(probabilities, 3)
+        
+        result = f"🎯 **Prediction: {predicted_class}** ({confidence_score:.1f}% confidence)\n\n"
+        result += f"📊 **Top 3 Predictions:**\n"
+        
+        for i in range(3):
+            class_name = class_names[top3_indices[0][i].item()]
+            prob = top3_prob[0][i].item() * 100
+            result += f"{i+1}. {class_name}: {prob:.1f}%\n"
+        
+        result += f"\n🔧 **Model:** {model_choice}\n"
+        result += f"📱 **Device:** {device}"
+        
+        return result
+        
+    except Exception as e:
+        return f"❌ **Error:** {str(e)}\n\nPlease ensure the image is valid and model is trained."
+
+def create_gradio_interface():
+    """Create and launch Gradio interface."""
+    
+    # Custom CSS for better styling
+    css = """
+    .gradio-container {
+        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+    }
+    .output-text {
+        font-family: 'Courier New', monospace;
+        font-size: 14px;
+    }
+    """
+    
+    # Create interface
+    interface = gr.Interface(
+        fn=predict_image,
+        inputs=[
+            gr.Image(type="pil", label="Upload Image", height=300),
+            gr.Dropdown(
+                choices=["CustomCNN", "ResNet18"],
+                value="CustomCNN",
+                label="Select Model"
+            )
+        ],
+        outputs=gr.Textbox(
+            label="Prediction Result",
+            lines=10,
+            elem_classes=["output-text"]
+        ),
+        title="🧠 CIFAR-10 CNN Benchmark",
+        description="""
+        Upload an image to test our trained models! 
+        
+        **Models:**
+        - **CustomCNN**: Lightweight 3M parameter model
+        - **ResNet18**: Standard 11M parameter baseline
+        
+        **Note:** Images will be resized to 32x32 pixels (CIFAR-10 format)
+        
+        **CIFAR-10 Classes:** airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck
+        """,
+        css=css,
+        theme=gr.themes.Soft(),
+        allow_flagging="never"
+    )
+    
+    return interface
+
+if __name__ == "__main__":
+    # Create and launch interface
+    demo = create_gradio_interface()
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=True,
+        show_error=True
+    )

best_model_custom.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4c270dbebadfa9bd400caf6fb6a094e26509122cf5577076a8c9a264542a3ccf
+size 28032771

best_model_customcnn.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a77f4a3d40ddc06d5d194006f7f974c85e306b64287838857c628cfbdde71763
+size 28033128

best_model_resnet18.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:890d6e5f67ced601d8fe8770cba3ad89c2c14270920070ecc5b6a48e408c0114
+size 134315407

eval.py

@@ -0,0 +1,169 @@
+import torch
+import torch.nn as nn
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import time
+from sklearn.metrics import confusion_matrix, classification_report
+from utils.data_loader import get_cifar10_info
+
+def evaluate(model, test_loader, device='cuda'):
+    """
+    Evaluate model on test set with comprehensive metrics.
+    
+    Args:
+        model: Trained PyTorch model
+        test_loader: Test data loader
+        device: Device to run evaluation on
+        
+    Returns:
+        dict: Evaluation results including accuracy, loss, predictions
+    """
+    model.to(device)
+    model.eval()
+    
+    test_loss = 0.0
+    correct = 0
+    total = 0
+    all_predictions = []
+    all_targets = []
+    
+    criterion = nn.CrossEntropyLoss()
+    
+    with torch.no_grad():
+        for inputs, targets in test_loader:
+            inputs, targets = inputs.to(device), targets.to(device)
+            outputs = model(inputs)
+            loss = criterion(outputs, targets)
+            
+            test_loss += loss.item()
+            _, predicted = outputs.max(1)
+            total += targets.size(0)
+            correct += predicted.eq(targets).sum().item()
+            
+            all_predictions.extend(predicted.cpu().numpy())
+            all_targets.extend(targets.cpu().numpy())
+    
+    test_acc = 100.0 * correct / total
+    test_loss /= len(test_loader)
+    
+    return {
+        'test_accuracy': test_acc,
+        'test_loss': test_loss,
+        'predictions': np.array(all_predictions),
+        'targets': np.array(all_targets)
+    }
+
+def plot_confusion_matrix(y_true, y_pred, save_path=None):
+    """
+    Plot confusion matrix heatmap.
+    
+    Args:
+        y_true: True labels
+        y_pred: Predicted labels
+        save_path: Path to save plot (optional)
+    """
+    cifar10_info = get_cifar10_info()
+    class_names = cifar10_info['class_names']
+    
+    cm = confusion_matrix(y_true, y_pred)
+    
+    plt.figure(figsize=(10, 8))
+    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
+                xticklabels=class_names, yticklabels=class_names)
+    plt.title('Confusion Matrix', fontsize=16)
+    plt.ylabel('True Label', fontsize=12)
+    plt.xlabel('Predicted Label', fontsize=12)
+    plt.xticks(rotation=45)
+    plt.yticks(rotation=0)
+    plt.tight_layout()
+    
+    if save_path:
+        plt.savefig(save_path, dpi=300, bbox_inches='tight')
+    
+    plt.show()
+    return cm
+
+def benchmark_model(model, test_loader, device='cuda', num_samples=100):
+    """
+    Benchmark model performance metrics.
+    
+    Args:
+        model: PyTorch model to benchmark
+        test_loader: Test data loader
+        device: Device to run benchmark on
+        num_samples: Number of samples for inference timing
+        
+    Returns:
+        dict: Benchmark results
+    """
+    model.to(device)
+    model.eval()
+    
+    # Parameter count
+    total_params = sum(p.numel() for p in model.parameters())
+    trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+    model_size_mb = total_params * 4 / (1024 * 1024)  # Float32
+    
+    # Inference timing
+    sample_inputs = []
+    sample_count = 0
+    
+    for inputs, _ in test_loader:
+        if sample_count >= num_samples:
+            break
+        batch_size = min(num_samples - sample_count, inputs.size(0))
+        sample_inputs.append(inputs[:batch_size])
+        sample_count += batch_size
+    
+    test_input = torch.cat(sample_inputs, dim=0)[:num_samples].to(device)
+    
+    # Warmup runs
+    with torch.no_grad():
+        for _ in range(10):
+            _ = model(test_input)
+    
+    # Timing runs
+    torch.cuda.synchronize() if device == 'cuda' else None
+    start_time = time.time()
+    
+    with torch.no_grad():
+        for _ in range(10):
+            _ = model(test_input)
+    
+    torch.cuda.synchronize() if device == 'cuda' else None
+    end_time = time.time()
+    
+    avg_inference_time = (end_time - start_time) / 10 / num_samples * 1000  # ms per sample
+    
+    return {
+        'total_params': total_params,
+        'trainable_params': trainable_params,
+        'model_size_mb': model_size_mb,
+        'inference_time_ms': avg_inference_time,
+        'throughput_samples_per_sec': 1000 / avg_inference_time
+    }
+
+def print_evaluation_report(eval_results, benchmark_results, model_name):
+    """Print comprehensive evaluation report."""
+    print(f"\n{'='*50}")
+    print(f"EVALUATION REPORT: {model_name}")
+    print(f"{'='*50}")
+    
+    print(f"📊 Test Performance:")
+    print(f"  Accuracy: {eval_results['test_accuracy']:.2f}%")
+    print(f"  Loss: {eval_results['test_loss']:.4f}")
+    
+    print(f"\n🔧 Model Statistics:")
+    print(f"  Parameters: {benchmark_results['total_params']:,}")
+    print(f"  Model Size: {benchmark_results['model_size_mb']:.2f} MB")
+    print(f"  Inference Time: {benchmark_results['inference_time_ms']:.2f} ms/sample")
+    print(f"  Throughput: {benchmark_results['throughput_samples_per_sec']:.0f} samples/sec")
+    
+    # Classification report
+    cifar10_info = get_cifar10_info()
+    class_names = cifar10_info['class_names']
+    
+    print(f"\n📈 Per-Class Performance:")
+    print(classification_report(eval_results['targets'], eval_results['predictions'], 
+                              target_names=class_names, digits=3))

main.py

@@ -0,0 +1,212 @@
+#!/usr/bin/env python3
+"""
+CIFAR-10 CNN Benchmark - Production Pipeline
+FAANG-level deep learning experiment orchestration
+"""
+
+import torch
+import torch.nn as nn
+from pathlib import Path
+import argparse
+import json
+from datetime import datetime
+
+# Local imports
+from models.custom_cnn import create_custom_cnn
+from models.resnet18 import load_resnet18
+from utils.data_loader import get_cifar10_loaders
+from train import train, create_optimizer, create_scheduler
+from eval import evaluate, benchmark_model, print_evaluation_report, plot_confusion_matrix
+from wandb_utils import WandbLogger, create_hyperparameter_sweep, run_hyperparameter_sweep
+
+def run_single_experiment(model_name: str, config: dict, use_wandb: bool = True):
+    """Run a single training experiment with comprehensive evaluation."""
+    print(f"\n🚀 Starting {model_name} experiment...")
+    
+    # Initialize model
+    if model_name.lower() == 'custom':
+        model = create_custom_cnn()
+        model_display_name = "custom"
+    elif model_name.lower() == 'resnet18':
+        model = load_resnet18()
+        model_display_name = "ResNet18"
+    else:
+        raise ValueError(f"Unknown model: {model_name}")
+    
+    # Load data
+    train_loader, val_loader, test_loader = get_cifar10_loaders(
+        batch_size=config['batch_size'],
+        num_workers=config.get('num_workers', 4)
+    )
+    
+    # Setup training components
+    optimizer = create_optimizer(
+        model, 
+        opt_type=config['optimizer'],
+        lr=config['learning_rate'],
+        weight_decay=config['weight_decay']
+    )
+    scheduler = create_scheduler(
+        optimizer, 
+        scheduler_type=config['scheduler'],
+        num_epochs=config['epochs']
+    )
+    criterion = nn.CrossEntropyLoss()
+    
+    # Train model
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    history = train(
+        model, train_loader, val_loader, optimizer, criterion, scheduler,
+        num_epochs=config['epochs'], device=device,
+        use_wandb=use_wandb, model_name=model_display_name
+    )
+    
+    # Comprehensive evaluation
+    print(f"\n📊 Evaluating {model_display_name}...")
+    
+    # Load best model
+    checkpoint = torch.load(f'best_model_{model_name.lower()}.pth', map_location=device)
+    model.load_state_dict(checkpoint['model_state_dict'])
+    
+    # Test evaluation
+    eval_results = evaluate(model, test_loader, device)
+    benchmark_results = benchmark_model(model, test_loader, device)
+    
+    # Print comprehensive report
+    print_evaluation_report(eval_results, benchmark_results, model_display_name)
+    
+    # Plot confusion matrix
+    plot_confusion_matrix(
+        eval_results['targets'], 
+        eval_results['predictions'],
+        save_path=f'confusion_matrix_{model_name.lower()}.png'
+    )
+    
+    # Save experiment results
+    results = {
+        'model': model_display_name,
+        'config': config,
+        'timestamp': datetime.now().isoformat(),
+        'evaluation': {
+            'test_accuracy': eval_results['test_accuracy'],
+            'test_loss': eval_results['test_loss']
+        },
+        'benchmark': benchmark_results,
+        'training_history': history
+    }
+    
+    with open(f'results_{model_name.lower()}.json', 'w') as f:
+        json.dump(results, f, indent=2)
+    
+    return results
+
+def run_model_comparison(config: dict, use_wandb: bool = True):
+    """Run comparative analysis between CustomCNN and ResNet18."""
+    print("\n🔬 Starting Model Comparison Analysis...")
+    
+    models = ['custom', 'resnet18']
+    all_results = {}
+    
+    for model_name in models:
+        print(f"\n{'='*60}")
+        print(f"Training {model_name.upper()}")
+        print(f"{'='*60}")
+        
+        results = run_single_experiment(model_name, config, use_wandb)
+        all_results[model_name] = results
+    
+    # Generate comparison report
+    print(f"\n{'='*60}")
+    print("📈 MODEL COMPARISON SUMMARY")
+    print(f"{'='*60}")
+    
+    for model_name, results in all_results.items():
+        eval_results = results['evaluation']
+        benchmark_results = results['benchmark']
+        
+        print(f"\n{results['model']}:")
+        print(f"  Test Accuracy: {eval_results['test_accuracy']:.2f}%")
+        print(f"  Parameters: {benchmark_results['total_params']:,}")
+        print(f"  Model Size: {benchmark_results['model_size_mb']:.2f} MB")
+        print(f"  Inference Time: {benchmark_results['inference_time_ms']:.2f} ms")
+        print(f"  Throughput: {benchmark_results['throughput_samples_per_sec']:.0f} samples/sec")
+    
+    # Determine winner
+    custom_acc = all_results['custom']['evaluation']['test_accuracy']
+    resnet_acc = all_results['resnet18']['evaluation']['test_accuracy']
+    
+    if custom_acc > resnet_acc:
+        print(f"\n🏆 Winner: CustomCNN ({custom_acc:.2f}% vs {resnet_acc:.2f}%)")
+    else:
+        print(f"\n🏆 Winner: ResNet18 ({resnet_acc:.2f}% vs {custom_acc:.2f}%)")
+    
+    # Save comparison results
+    with open('model_comparison.json', 'w') as f:
+        json.dump(all_results, f, indent=2)
+    
+    return all_results
+
+def run_hyperparameter_sweep(model_name: str, count: int = 20):
+    """Run automated hyperparameter optimization."""
+    print(f"\n🎯 Starting Hyperparameter Sweep for {model_name}...")
+    
+    sweep_config = create_hyperparameter_sweep()
+    
+    def train_fn():
+        import wandb
+        config = wandb.config
+        
+        # Convert wandb config to dict
+        config_dict = {
+            'learning_rate': config.learning_rate,
+            'batch_size': config.batch_size,
+            'weight_decay': config.weight_decay,
+            'optimizer': config.optimizer,
+            'scheduler': config.scheduler,
+            'epochs': 50  # Changed from 50 to 50
+        }
+        
+        run_single_experiment(model_name, config_dict, use_wandb=True)
+    
+    run_hyperparameter_sweep(train_fn, sweep_config, count=count)
+
+def main():
+    parser = argparse.ArgumentParser(description='CIFAR-10 CNN Benchmark Pipeline')
+    parser.add_argument('--mode', choices=['single', 'compare', 'sweep'], default='compare',
+                       help='Experiment mode')
+    parser.add_argument('--model', choices=['custom', 'resnet18'], default='custom',
+                       help='Model for single experiment')
+    parser.add_argument('--no-wandb', action='store_true', help='Disable W&B logging')
+    parser.add_argument('--epochs', type=int, default=50, help='Number of epochs')  # Changed from 100 to 50
+    parser.add_argument('--lr', type=float, default=0.001, help='Learning rate')
+    parser.add_argument('--batch_size', type=int, default=128, help='Batch size')
+    parser.add_argument('--sweep_count', type=int, default=20, help='Number of sweep runs')
+    
+    args = parser.parse_args()
+    
+    # Default configuration
+    config = {
+        'epochs': args.epochs,
+        'learning_rate': args.lr,
+        'batch_size': args.batch_size,
+        'weight_decay': 1e-4,
+        'optimizer': 'adamw',
+        'scheduler': 'cosine',
+        'num_workers': 4
+    }
+    
+    # Create output directory
+    Path('outputs').mkdir(exist_ok=True)
+    
+    # Run experiment based on mode
+    if args.mode == 'single':
+        run_single_experiment(args.model, config, use_wandb=not args.no_wandb)
+    elif args.mode == 'compare':
+        run_model_comparison(config, use_wandb=not args.no_wandb)
+    elif args.mode == 'sweep':
+        run_hyperparameter_sweep(args.model, count=args.sweep_count)
+    
+    print("\n✅ Experiment completed! Check outputs/ directory for results.")
+
+if __name__ == "__main__":
+    main()

model_comparison.json

@@ -0,0 +1,574 @@
+{
+  "custom": {
+    "model": "custom",
+    "config": {
+      "epochs": 50,
+      "learning_rate": 0.001,
+      "batch_size": 128,
+      "weight_decay": 0.0001,
+      "optimizer": "adamw",
+      "scheduler": "cosine",
+      "num_workers": 4
+    },
+    "timestamp": "2025-07-18T04:13:55.384588",
+    "evaluation": {
+      "test_accuracy": 90.64,
+      "test_loss": 0.40045145286034933
+    },
+    "benchmark": {
+      "total_params": 2332106,
+      "trainable_params": 2332106,
+      "model_size_mb": 8.896278381347656,
+      "inference_time_ms": 0.03669309616088868,
+      "throughput_samples_per_sec": 27253.083130823507
+    },
+    "training_history": {
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+        9.866357858642198e-07,
+        0.0
+      ]
+    }
+  },
+  "resnet18": {
+    "model": "ResNet18",
+    "config": {
+      "epochs": 50,
+      "learning_rate": 0.001,
+      "batch_size": 128,
+      "weight_decay": 0.0001,
+      "optimizer": "adamw",
+      "scheduler": "cosine",
+      "num_workers": 4
+    },
+    "timestamp": "2025-07-18T15:21:09.396093",
+    "evaluation": {
+      "test_accuracy": 84.86,
+      "test_loss": 0.562998549093174
+    },
+    "benchmark": {
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+      "trainable_params": 11181642,
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+      "inference_time_ms": 0.07653045654296875,
+      "throughput_samples_per_sec": 13066.69325092214
+    },
+    "training_history": {
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+        0.0
+      ]
+    }
+  }
+}

models/custom_cnn.py

@@ -0,0 +1,84 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class CustomCNN(nn.Module):
+    """
+    Custom CNN optimized for CIFAR-10 with ~3-4M parameters.
+    Designed to compete with ResNet18 while being lightweight.
+    """
+    
+    def __init__(self, num_classes=10, dropout_rate=0.4):
+        super(CustomCNN, self).__init__()
+        
+        # Feature extractor with efficient blocks
+        self.features = nn.Sequential(
+            # Block 1: 32x32 -> 32x32
+            self._conv_block(3, 64, stride=1),
+            self._conv_block(64, 64, stride=1),
+            
+            # Block 2: 32x32 -> 16x16
+            self._conv_block(64, 128, stride=2),
+            self._conv_block(128, 128, stride=1),
+            
+            # Block 3: 16x16 -> 8x8
+            self._conv_block(128, 256, stride=2),
+            self._conv_block(256, 256, stride=1),
+            
+            # Block 4: 8x8 -> 4x4 (deep feature extraction)
+            self._conv_block(256, 512, stride=2),
+        )
+        
+        # Global pooling and classifier
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.dropout = nn.Dropout(dropout_rate)
+        self.classifier = nn.Linear(512, num_classes)
+        
+        # Initialize weights
+        self._initialize_weights()
+    
+    def _conv_block(self, in_channels, out_channels, stride=1):
+        """Efficient conv block with BatchNorm and ReLU."""
+        return nn.Sequential(
+            nn.Conv2d(in_channels, out_channels, kernel_size=3, 
+                     stride=stride, padding=1, bias=False),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True)
+        )
+    
+    def _initialize_weights(self):
+        """Initialize weights using He initialization for ReLU."""
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                nn.init.constant_(m.bias, 0)
+    
+    def forward(self, x):
+        x = self.features(x)
+        x = self.avgpool(x)
+        x = torch.flatten(x, 1)
+        x = self.dropout(x)
+        x = self.classifier(x)
+        return x
+    
+    def get_model_info(self):
+        """Return model architecture info."""
+        total_params = sum(p.numel() for p in self.parameters())
+        trainable_params = sum(p.numel() for p in self.parameters() if p.requires_grad)
+        
+        return {
+            'total_params': total_params,
+            'trainable_params': trainable_params,
+            'model_size_mb': total_params * 4 / (1024 * 1024),  # Assuming float32
+            'architecture': '7-layer CNN with BatchNorm and Dropout'
+        }
+
+# Factory function for easy instantiation
+def create_custom_cnn(num_classes=10, dropout_rate=0.4):
+    """Create and return a CustomCNN instance."""
+    return CustomCNN(num_classes=num_classes, dropout_rate=dropout_rate)

models/resnet18.py

@@ -0,0 +1,56 @@
+import torch
+import torch.nn as nn
+from torchvision import models
+
+def load_resnet18(num_classes=10, pretrained=False):
+    """
+    Load ResNet18 modified for CIFAR-10 classification.
+    
+    Args:
+        num_classes: Number of output classes (default: 10 for CIFAR-10)
+        pretrained: Whether to use ImageNet pretrained weights (default: False for fair comparison)
+        
+    Returns:
+        Modified ResNet18 model
+    """
+    # Load ResNet18 without pretrained weights for fair comparison
+    weights = models.ResNet18_Weights.IMAGENET1K_V1 if pretrained else None
+    model = models.resnet18(weights=weights)
+    
+    # Replace final layer for CIFAR-10 (10 classes)
+    model.fc = nn.Linear(model.fc.in_features, num_classes)
+    
+    # Initialize the new classifier layer properly
+    nn.init.normal_(model.fc.weight, 0, 0.01)
+    nn.init.constant_(model.fc.bias, 0)
+    
+    return model
+
+def get_resnet18_info():
+    """Return ResNet18 model information."""
+    model = load_resnet18()
+    total_params = sum(p.numel() for p in model.parameters())
+    trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+    
+    return {
+        'total_params': total_params,
+        'trainable_params': trainable_params,
+        'model_size_mb': total_params * 4 / (1024 * 1024),
+        'architecture': 'ResNet18 with modified classifier',
+        'original_fc_features': 512,
+        'modified_fc_classes': 10
+    }
+
+def freeze_backbone(model, freeze=True):
+    """
+    Freeze/unfreeze ResNet18 backbone for transfer learning experiments.
+    
+    Args:
+        model: ResNet18 model
+        freeze: Whether to freeze backbone parameters
+    """
+    for name, param in model.named_parameters():
+        if 'fc' not in name:  # Don't freeze the final classifier
+            param.requires_grad = not freeze
+    
+    return model

requirements.txt

@@ -0,0 +1,24 @@
+# Core ML/DL Dependencies
+torch>=2.0.0
+torchvision>=0.15.0
+numpy>=1.21.0
+scikit-learn>=1.0.0
+
+# Experiment Tracking & Visualization
+wandb>=0.15.0
+matplotlib>=3.5.0
+seaborn>=0.11.0
+
+# UI & Demo
+gradio>=4.0.0
+
+# Development & Code Quality
+pytest>=7.0.0
+black>=22.0.0
+flake8>=4.0.0
+mypy>=0.950
+
+# Optional: For advanced features
+tensorboard>=2.10.0  # Alternative to W&B
+pillow>=9.0.0       # Image processing
+tqdm>=4.64.0        # Progress bars

results_custom.json

@@ -0,0 +1,286 @@
+{
+  "model": "custom",
+  "config": {
+    "epochs": 50,
+    "learning_rate": 0.001,
+    "batch_size": 128,
+    "weight_decay": 0.0001,
+    "optimizer": "adamw",
+    "scheduler": "cosine",
+    "num_workers": 4
+  },
+  "timestamp": "2025-07-18T04:13:55.384588",
+  "evaluation": {
+    "test_accuracy": 90.64,
+    "test_loss": 0.40045145286034933
+  },
+  "benchmark": {
+    "total_params": 2332106,
+    "trainable_params": 2332106,
+    "model_size_mb": 8.896278381347656,
+    "inference_time_ms": 0.03669309616088868,
+    "throughput_samples_per_sec": 27253.083130823507
+  },
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+}

results_resnet18.json

@@ -0,0 +1,286 @@
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+      0.0007408768370508576,
+      0.0007128896457825362,
+      0.0006840622763423389,
+      0.0006545084971874735,
+      0.0006243449435824271,
+      0.0005936906572928622,
+      0.000562666616782152,
+      0.0005313952597646566,
+      0.0004999999999999998,
+      0.00046860474023534314,
+      0.00043733338321784774,
+      0.00040630934270713756,
+      0.00037565505641757246,
+      0.00034549150281252633,
+      0.00031593772365766105,
+      0.00028711035421746355,
+      0.0002591231629491422,
+      0.00023208660251050145,
+      0.00020610737385376337,
+      0.00018128800512565502,
+      0.00015772644703565555,
+      0.00013551568628929425,
+      0.00011474337861210535,
+      9.549150281252626e-05,
+      7.783603724899252e-05,
+      6.184665997806817e-05,
+      4.7586473766990294e-05,
+      3.5111757055874305e-05,
+      2.4471741852423218e-05,
+      1.5708419435684507e-05,
+      8.856374635655634e-06,
+      3.942649342761115e-06,
+      9.866357858642198e-07,
+      0.0
+    ]
+  }
+}

train.py

@@ -0,0 +1,234 @@
+import torch
+import torch.nn as nn
+import time
+from collections import defaultdict
+from wandb_utils import WandbLogger
+import argparse
+
+def train(model, train_loader, val_loader, optimizer, criterion, scheduler, 
+          num_epochs, device='cuda', use_wandb=True, model_name="CustomCNN"):
+    """
+    Production-grade training pipeline with optional W&B integration.
+    
+    Args:
+        model: PyTorch model to train
+        train_loader: Training data loader
+        val_loader: Validation data loader
+        optimizer: Optimizer (AdamW/SGD)
+        criterion: Loss function (CrossEntropyLoss)
+        scheduler: Learning rate scheduler
+        num_epochs: Number of training epochs
+        device: Device to train on
+        use_wandb: Whether to use W&B logging
+        model_name: Name of the model for logging
+        
+    Returns:
+        dict: Training history with losses and accuracies
+    """
+    model.to(device)
+    best_val_acc = 0.0
+    history = defaultdict(list)
+    
+    # Initialize W&B if enabled
+    logger = None
+    if use_wandb:
+        logger = WandbLogger()
+        config = {
+            'epochs': num_epochs,
+            'device': str(device),
+            'optimizer': optimizer.__class__.__name__,
+            'scheduler': scheduler.__class__.__name__,
+            'learning_rate': optimizer.param_groups[0]['lr']
+        }
+        logger.init_experiment(config, model, model_name)
+    
+    print(f"🚀 Training {model_name} on {device} for {num_epochs} epochs...")
+    print("-" * 60)
+    
+    for epoch in range(num_epochs):
+        epoch_start = time.time()
+        
+        # Training phase
+        model.train()
+        train_loss, train_acc = _train_epoch(model, train_loader, optimizer, criterion, device)
+        
+        # Validation phase
+        model.eval()
+        val_loss, val_acc, val_preds, val_targets = _validate_epoch(model, val_loader, criterion, device)
+        
+        # Update scheduler
+        scheduler.step()
+        
+        # Log metrics
+        metrics = {
+            'train_loss': train_loss,
+            'train_acc': train_acc,
+            'val_loss': val_loss,
+            'val_acc': val_acc,
+            'lr': optimizer.param_groups[0]['lr']
+        }
+        
+        for key, value in metrics.items():
+            history[key].append(value)
+        
+        # W&B logging
+        if logger:
+            logger.log_metrics({
+                'epoch': epoch,
+                'train_loss': train_loss,
+                'train_accuracy': train_acc,
+                'val_loss': val_loss,
+                'val_accuracy': val_acc,
+                'learning_rate': optimizer.param_groups[0]['lr']
+            }, step=epoch)
+            
+            # Log confusion matrix every 20 epochs
+            if (epoch + 1) % 20 == 0:
+                logger.log_confusion_matrix(val_targets, val_preds, epoch)
+        
+        # Save best model
+        is_best = val_acc > best_val_acc
+        if is_best:
+            best_val_acc = val_acc
+            checkpoint = {
+                'epoch': epoch,
+                'model_state_dict': model.state_dict(),
+                'optimizer_state_dict': optimizer.state_dict(),
+                'val_acc': val_acc,
+                'val_loss': val_loss
+            }
+            torch.save(checkpoint, f'best_model_{model_name.lower()}.pth')
+            
+            # Only log best model checkpoint to W&B
+            if logger:
+                logger.log_model_checkpoint(model, optimizer, epoch, 
+                                          {'val_accuracy': val_acc, 'val_loss': val_loss}, 
+                                          is_best=True)
+        
+        # Print progress
+        epoch_time = time.time() - epoch_start
+        print(f"Epoch {epoch+1:3d}/{num_epochs} | "
+              f"Train: {train_loss:.4f}/{train_acc:.2f}% | "
+              f"Val: {val_loss:.4f}/{val_acc:.2f}% | "
+              f"LR: {optimizer.param_groups[0]['lr']:.2e} | "
+              f"Time: {epoch_time:.1f}s")
+    
+    if logger:
+        logger.finish()
+    
+    print(f"\n🎯 Best validation accuracy: {best_val_acc:.2f}%")
+    return dict(history)
+
+def _train_epoch(model, train_loader, optimizer, criterion, device):
+    """Single training epoch with metrics."""
+    running_loss = 0.0
+    correct = 0
+    total = 0
+    
+    for batch_idx, (inputs, targets) in enumerate(train_loader):
+        inputs, targets = inputs.to(device), targets.to(device)
+        
+        optimizer.zero_grad()
+        outputs = model(inputs)
+        loss = criterion(outputs, targets)
+        loss.backward()
+        optimizer.step()
+        
+        running_loss += loss.item()
+        _, predicted = outputs.max(1)
+        total += targets.size(0)
+        correct += predicted.eq(targets).sum().item()
+    
+    return running_loss / len(train_loader), 100.0 * correct / total
+
+def _validate_epoch(model, val_loader, criterion, device):
+    """Single validation epoch with predictions."""
+    running_loss = 0.0
+    correct = 0
+    total = 0
+    all_preds = []
+    all_targets = []
+    
+    with torch.no_grad():
+        for inputs, targets in val_loader:
+            inputs, targets = inputs.to(device), targets.to(device)
+            outputs = model(inputs)
+            loss = criterion(outputs, targets)
+            
+            running_loss += loss.item()
+            _, predicted = outputs.max(1)
+            total += targets.size(0)
+            correct += predicted.eq(targets).sum().item()
+            
+            all_preds.extend(predicted.cpu().numpy())
+            all_targets.extend(targets.cpu().numpy())
+    
+    return (running_loss / len(val_loader), 100.0 * correct / total, 
+            all_preds, all_targets)
+
+def create_optimizer(model, opt_type='adamw', lr=0.001, weight_decay=1e-4):
+    """Create optimizer with best practices."""
+    if opt_type.lower() == 'adamw':
+        return torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)
+    elif opt_type.lower() == 'sgd':
+        return torch.optim.SGD(model.parameters(), lr=lr, momentum=0.9, weight_decay=weight_decay)
+    else:
+        raise ValueError(f"Unsupported optimizer: {opt_type}")
+
+def create_scheduler(optimizer, scheduler_type='cosine', num_epochs=50):  # Changed default from 100 to 50
+    """Create learning rate scheduler."""
+    if scheduler_type.lower() == 'cosine':
+        return torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=num_epochs)
+    elif scheduler_type.lower() == 'step':
+        return torch.optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1)
+    else:
+        raise ValueError(f"Unsupported scheduler: {scheduler_type}")
+
+# CLI interface for hyperparameter sweeps
+def main():
+    parser = argparse.ArgumentParser(description='Train CIFAR-10 models with W&B')
+    parser.add_argument('--model', choices=['custom', 'resnet18'], default='custom')
+    parser.add_argument('--epochs', type=int, default=50)  # Changed from 100 to 50
+    parser.add_argument('--lr', type=float, default=0.001)
+    parser.add_argument('--batch_size', type=int, default=128)
+    parser.add_argument('--no-wandb', action='store_true', help='Disable W&B logging')
+    parser.add_argument('--sweep', action='store_true', help='Run hyperparameter sweep')
+    
+    args = parser.parse_args()
+    
+    # Import models
+    if args.model == 'custom':
+        from models.custom_cnn import create_custom_cnn
+        model = create_custom_cnn()
+        model_name = "CustomCNN"
+    else:
+        from models.resnet18 import load_resnet18
+        model = load_resnet18()
+        model_name = "ResNet18"
+    
+    # Load data
+    from utils.data_loader import get_cifar10_loaders
+    train_loader, val_loader, test_loader = get_cifar10_loaders(batch_size=args.batch_size)
+    
+    # Create optimizer and scheduler
+    optimizer = create_optimizer(model, lr=args.lr)
+    scheduler = create_scheduler(optimizer, num_epochs=args.epochs)
+    criterion = nn.CrossEntropyLoss()
+    
+    # Train model
+    if args.sweep:
+        from wandb_utils import create_hyperparameter_sweep, run_hyperparameter_sweep
+        sweep_config = create_hyperparameter_sweep()
+        
+        def train_fn():
+            # W&B will set hyperparameters
+            train(model, train_loader, val_loader, optimizer, criterion, 
+                  scheduler, args.epochs, model_name=model_name)
+        
+        run_hyperparameter_sweep(train_fn, sweep_config)
+    else:
+        train(model, train_loader, val_loader, optimizer, criterion, scheduler, 
+              args.epochs, use_wandb=not args.no_wandb, model_name=model_name)
+
+if __name__ == "__main__":
+    main()

utils/data_loader.py

@@ -0,0 +1,156 @@
+import torch
+from torch.utils.data import DataLoader, Dataset, random_split
+from torchvision import transforms
+import os
+import pandas as pd
+from PIL import Image
+
+class KaggleCIFAR10Dataset(Dataset):
+    """Custom dataset for CIFAR-10 in train/test + CSV format."""
+
+    def __init__(self, data_dir, is_train=True, transform=None):
+        self.data_dir = data_dir
+        self.is_train = is_train
+        self.transform = transform
+
+        # Class name mapping (same for train and test)
+        self.class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
+                            'dog', 'frog', 'horse', 'ship', 'truck']
+        self.label_to_idx = {name: idx for idx, name in enumerate(self.class_names)}
+
+        # Common root for labels and image folders
+        labels_root = os.path.join(data_dir, 'raw')
+
+        if is_train:
+            labels_path = os.path.join(labels_root, 'trainLabels.csv')
+            self.image_dir = os.path.join(labels_root, 'train')
+        else:
+            labels_path = os.path.join(labels_root, 'testLabels.csv')
+            self.image_dir = os.path.join(labels_root, 'test')
+
+        self.labels_df = pd.read_csv(labels_path)
+
+    def __len__(self):
+        return len(self.labels_df)
+
+    def __getitem__(self, idx):
+        row = self.labels_df.iloc[idx]
+        image_id = row['id']
+        label_name = row['label']
+
+        image_path = os.path.join(self.image_dir, f"{image_id}.png")
+        image = Image.open(image_path).convert('RGB')
+        label = self.label_to_idx[label_name]
+
+        if self.transform:
+            image = self.transform(image)
+
+        return image, label
+
+
+def get_cifar10_loaders(data_dir='./data', batch_size=64, num_workers=4, val_split=0.1):
+    """
+    Load CIFAR-10 from custom directory layout with augmentation and splits.
+
+    Args:
+        data_dir: Directory containing 'raw/train', 'raw/test', and both CSV files
+        batch_size: Batch size for loaders
+        num_workers: Worker processes for DataLoader
+        val_split: Fraction of training data used for validation
+
+    Returns:
+        (train_loader, val_loader, test_loader)
+    """
+    CIFAR10_MEAN = (0.4914, 0.4822, 0.4465)
+    CIFAR10_STD = (0.2023, 0.1994, 0.2010)
+
+    train_transform = transforms.Compose([
+        transforms.RandomCrop(32, padding=4),
+        transforms.RandomHorizontalFlip(),
+        transforms.ToTensor(),
+        transforms.Normalize(CIFAR10_MEAN, CIFAR10_STD)
+    ])
+
+    eval_transform = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize(CIFAR10_MEAN, CIFAR10_STD)
+    ])
+
+    # Full training dataset
+    full_train_dataset = KaggleCIFAR10Dataset(
+        data_dir=data_dir,
+        is_train=True,
+        transform=train_transform
+    )
+
+    # Split into train and validation
+    train_size = int((1 - val_split) * len(full_train_dataset))
+    val_size = len(full_train_dataset) - train_size
+
+    train_subset, val_subset = random_split(
+        full_train_dataset,
+        [train_size, val_size],
+        generator=torch.Generator().manual_seed(42)
+    )
+
+    # Use eval transforms for validation set
+    val_dataset = KaggleCIFAR10Dataset(
+        data_dir=data_dir,
+        is_train=True,
+        transform=eval_transform
+    )
+    val_subset.dataset = val_dataset
+
+    # Load labeled test dataset
+    test_dataset = KaggleCIFAR10Dataset(
+        data_dir=data_dir,
+        is_train=False,
+        transform=eval_transform
+    )
+
+    # DataLoaders
+    train_loader = DataLoader(
+        train_subset,
+        batch_size=batch_size,
+        shuffle=True,
+        num_workers=num_workers,
+        pin_memory=torch.cuda.is_available(),
+        persistent_workers=True if num_workers > 0 else False
+    )
+
+    val_loader = DataLoader(
+        val_subset,
+        batch_size=batch_size,
+        shuffle=False,
+        num_workers=num_workers,
+        pin_memory=torch.cuda.is_available(),
+        persistent_workers=True if num_workers > 0 else False
+    )
+
+    test_loader = DataLoader(
+        test_dataset,
+        batch_size=batch_size,
+        shuffle=False,
+        num_workers=num_workers,
+        pin_memory=torch.cuda.is_available(),
+        persistent_workers=True if num_workers > 0 else False
+    )
+
+    print(f"📊 Dataset loaded successfully!")
+    print(f"   Training samples: {len(train_subset)}")
+    print(f"   Validation samples: {len(val_subset)}")
+    print(f"   Test samples: {len(test_dataset)}")
+
+    return train_loader, val_loader, test_loader
+
+
+def get_cifar10_info():
+    """Return CIFAR-10 dataset information."""
+    return {
+        'num_classes': 10,
+        'class_names': ['airplane', 'automobile', 'bird', 'cat', 'deer',
+                        'dog', 'frog', 'horse', 'ship', 'truck'],
+        'input_shape': (3, 32, 32),
+        'mean': (0.4914, 0.4822, 0.4465),
+        'std': (0.2023, 0.1994, 0.2010)
+    }

utils/visualizations.py

@@ -0,0 +1,154 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+
+def plot_training_curves(train_acc, val_acc, train_loss, val_loss, model_name, save_dir='plots'):
+    """
+    Plot and save training/validation curves.
+    
+    Args:
+        train_acc: List of training accuracies
+        val_acc: List of validation accuracies
+        train_loss: List of training losses
+        val_loss: List of validation losses
+        model_name: Name of the model for plot titles and filenames
+        save_dir: Directory to save plots
+    """
+    os.makedirs(save_dir, exist_ok=True)
+    
+    epochs = range(1, len(train_acc) + 1)
+    
+    # Set up the plotting style
+    plt.style.use('seaborn-v0_8')
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))
+    
+    # Plot accuracy curves
+    ax1.plot(epochs, train_acc, 'b-', label='Training Accuracy', linewidth=2)
+    ax1.plot(epochs, val_acc, 'r-', label='Validation Accuracy', linewidth=2)
+    ax1.set_title(f'{model_name} - Accuracy Curves', fontsize=14, fontweight='bold')
+    ax1.set_xlabel('Epochs', fontsize=12)
+    ax1.set_ylabel('Accuracy (%)', fontsize=12)
+    ax1.legend(fontsize=11)
+    ax1.grid(True, alpha=0.3)
+    ax1.set_ylim([0, 100])
+    
+    # Add best accuracy annotations
+    best_train_acc = max(train_acc)
+    best_val_acc = max(val_acc)
+    best_train_epoch = train_acc.index(best_train_acc) + 1
+    best_val_epoch = val_acc.index(best_val_acc) + 1
+    
+    ax1.annotate(f'Best: {best_val_acc:.2f}%', 
+                xy=(best_val_epoch, best_val_acc), 
+                xytext=(10, 10), textcoords='offset points',
+                bbox=dict(boxstyle='round,pad=0.3', facecolor='red', alpha=0.7),
+                arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0'))
+    
+    # Plot loss curves
+    ax2.plot(epochs, train_loss, 'b-', label='Training Loss', linewidth=2)
+    ax2.plot(epochs, val_loss, 'r-', label='Validation Loss', linewidth=2)
+    ax2.set_title(f'{model_name} - Loss Curves', fontsize=14, fontweight='bold')
+    ax2.set_xlabel('Epochs', fontsize=12)
+    ax2.set_ylabel('Loss', fontsize=12)
+    ax2.legend(fontsize=11)
+    ax2.grid(True, alpha=0.3)
+    
+    # Add minimum loss annotation
+    min_val_loss = min(val_loss)
+    min_loss_epoch = val_loss.index(min_val_loss) + 1
+    ax2.annotate(f'Min: {min_val_loss:.4f}', 
+                xy=(min_loss_epoch, min_val_loss), 
+                xytext=(10, 10), textcoords='offset points',
+                bbox=dict(boxstyle='round,pad=0.3', facecolor='red', alpha=0.7),
+                arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0'))
+    
+    plt.tight_layout()
+    
+    # Save plot
+    save_path = os.path.join(save_dir, f'{model_name.lower()}_training_curves.png')
+    plt.savefig(save_path, dpi=300, bbox_inches='tight')
+    plt.show()
+    
+    print(f"📊 Training curves saved to: {save_path}")
+    return fig
+
+def plot_model_comparison(models_history, save_dir='plots'):
+    """
+    Compare training curves of multiple models.
+    
+    Args:
+        models_history: Dict with model names as keys and history dicts as values
+        save_dir: Directory to save plots
+    """
+    os.makedirs(save_dir, exist_ok=True)
+    
+    plt.style.use('seaborn-v0_8')
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))
+    
+    colors = ['blue', 'red', 'green', 'orange', 'purple']
+    
+    for i, (model_name, history) in enumerate(models_history.items()):
+        epochs = range(1, len(history['val_acc']) + 1)
+        color = colors[i % len(colors)]
+        
+        # Plot validation accuracy
+        ax1.plot(epochs, history['val_acc'], color=color, 
+                label=f'{model_name}', linewidth=2, marker='o', markersize=3)
+        
+        # Plot validation loss
+        ax2.plot(epochs, history['val_loss'], color=color, 
+                label=f'{model_name}', linewidth=2, marker='o', markersize=3)
+    
+    # Configure accuracy plot
+    ax1.set_title('Model Comparison - Validation Accuracy', fontsize=14, fontweight='bold')
+    ax1.set_xlabel('Epochs', fontsize=12)
+    ax1.set_ylabel('Validation Accuracy (%)', fontsize=12)
+    ax1.legend(fontsize=11)
+    ax1.grid(True, alpha=0.3)
+    ax1.set_ylim([0, 100])
+    
+    # Configure loss plot
+    ax2.set_title('Model Comparison - Validation Loss', fontsize=14, fontweight='bold')
+    ax2.set_xlabel('Epochs', fontsize=12)
+    ax2.set_ylabel('Validation Loss', fontsize=12)
+    ax2.legend(fontsize=11)
+    ax2.grid(True, alpha=0.3)
+    
+    plt.tight_layout()
+    
+    # Save comparison plot
+    save_path = os.path.join(save_dir, 'model_comparison.png')
+    plt.savefig(save_path, dpi=300, bbox_inches='tight')
+    plt.show()
+    
+    print(f"📊 Model comparison saved to: {save_path}")
+    return fig
+
+def plot_learning_rate_schedule(lr_history, model_name, save_dir='plots'):
+    """
+    Plot learning rate schedule over epochs.
+    
+    Args:
+        lr_history: List of learning rates per epoch
+        model_name: Name of the model
+        save_dir: Directory to save plots
+    """
+    os.makedirs(save_dir, exist_ok=True)
+    
+    epochs = range(1, len(lr_history) + 1)
+    
+    plt.figure(figsize=(10, 6))
+    plt.plot(epochs, lr_history, 'g-', linewidth=2)
+    plt.title(f'{model_name} - Learning Rate Schedule', fontsize=14, fontweight='bold')
+    plt.xlabel('Epochs', fontsize=12)
+    plt.ylabel('Learning Rate', fontsize=12)
+    plt.grid(True, alpha=0.3)
+    plt.yscale('log')
+    
+    # Save plot
+    save_path = os.path.join(save_dir, f'{model_name.lower()}_lr_schedule.png')
+    plt.savefig(save_path, dpi=300, bbox_inches='tight')
+    plt.show()
+    
+    print(f"📊 LR schedule saved to: {save_path}")
+    return plt.gcf()

wandb/run-20250717_042842-06r19hoq/files/config.yaml

@@ -0,0 +1,83 @@
+_wandb:
+    value:
+        cli_version: 0.21.0
+        e:
+            wwsyhap7oz1qssew84oaomf0lpswl335:
+                codePath: main.py
+                codePathLocal: main.py
+                cpu_count: 4
+                cpu_count_logical: 8
+                cudaVersion: "12.7"
+                disk:
+                    /:
+                        total: "255230791680"
+                        used: "157923209216"
+                email: shreshthkapai@gmail.com
+                executable: C:\Users\Legion\Miniconda3\envs\ML\python.exe
+                git:
+                    remote: https://github.com/shreshthkapai/cnn_benchmark.git
+                gpu: NVIDIA GeForce GTX 1650
+                gpu_count: 1
+                gpu_nvidia:
+                    - architecture: Turing
+                      cudaCores: 1024
+                      memoryTotal: "4294967296"
+                      name: NVIDIA GeForce GTX 1650
+                      uuid: GPU-fbcd7647-fb67-66f5-b8c7-1a4198b7e4fa
+                host: DESKTOP-EIHJJJL
+                memory:
+                    total: "8506298368"
+                os: Windows-11-10.0.26100-SP0
+                program: C:\Users\Legion\desktop\cnn_benchmark\main.py
+                python: CPython 3.13.5
+                root: C:\Users\Legion\desktop\cnn_benchmark
+                startedAt: "2025-07-16T22:58:42.840536Z"
+                writerId: wwsyhap7oz1qssew84oaomf0lpswl335
+        m: []
+        python_version: 3.13.5
+        t:
+            "1":
+                - 1
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+            "2":
+                - 1
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+            "3":
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+            "4": 3.13.5
+            "5": 0.21.0
+            "8":
+                - 3
+            "12": 0.21.0
+            "13": windows-amd64
+architecture:
+    value: CustomCNN
+device:
+    value: cpu
+epochs:
+    value: 100
+learning_rate:
+    value: 0.001
+model_name:
+    value: CustomCNN
+model_size_mb:
+    value: 8.896278381347656
+optimizer:
+    value: AdamW
+scheduler:
+    value: CosineAnnealingLR
+total_params:
+    value: 2332106
+trainable_params:
+    value: 2332106

wandb/run-20250717_042842-06r19hoq/files/media/graph/graph_0_summary_132b1dd32ead095f5a49.graph.json

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wandb/run-20250717_042842-06r19hoq/files/output.log

@@ -0,0 +1,103 @@
+wandb: logging graph, to disable use `wandb.watch(log_graph=False)`
+🚀 Training CustomCNN on cpu for 100 epochs...
+------------------------------------------------------------
+Epoch   1/100 | Train: 1.4263/47.28% | Val: 1.2500/55.26% | LR: 1.00e-03 | Time: 688.8s
+Epoch   2/100 | Train: 0.9965/64.49% | Val: 1.0144/64.68% | LR: 9.99e-04 | Time: 554.9s
+Epoch   3/100 | Train: 0.8147/71.34% | Val: 0.8488/71.12% | LR: 9.98e-04 | Time: 493.9s
+Epoch   4/100 | Train: 0.6970/75.81% | Val: 0.7762/73.08% | LR: 9.96e-04 | Time: 442.6s
+Epoch   5/100 | Train: 0.6207/78.49% | Val: 0.6689/77.54% | LR: 9.94e-04 | Time: 425.4s
+Epoch   6/100 | Train: 0.5577/80.68% | Val: 0.6680/77.22% | LR: 9.91e-04 | Time: 445.9s
+Epoch   7/100 | Train: 0.5138/82.33% | Val: 0.6256/79.04% | LR: 9.88e-04 | Time: 444.6s
+Epoch   8/100 | Train: 0.4757/83.72% | Val: 0.6287/79.50% | LR: 9.84e-04 | Time: 443.6s
+Epoch   9/100 | Train: 0.4378/84.72% | Val: 0.4669/83.58% | LR: 9.80e-04 | Time: 446.4s
+Epoch  10/100 | Train: 0.4089/86.04% | Val: 0.5060/82.66% | LR: 9.76e-04 | Time: 444.0s
+Epoch  11/100 | Train: 0.3825/86.71% | Val: 0.4139/85.20% | LR: 9.70e-04 | Time: 444.0s
+Epoch  12/100 | Train: 0.3571/87.85% | Val: 0.4525/84.94% | LR: 9.65e-04 | Time: 442.7s
+Epoch  13/100 | Train: 0.3396/88.30% | Val: 0.4341/85.46% | LR: 9.59e-04 | Time: 443.9s
+Epoch  14/100 | Train: 0.3161/89.11% | Val: 0.4940/84.54% | LR: 9.52e-04 | Time: 443.9s
+Epoch  15/100 | Train: 0.3031/89.43% | Val: 0.3860/87.34% | LR: 9.46e-04 | Time: 444.4s
+Epoch  16/100 | Train: 0.2818/90.13% | Val: 0.4627/85.04% | LR: 9.38e-04 | Time: 444.2s
+Epoch  17/100 | Train: 0.2702/90.67% | Val: 0.3989/87.02% | LR: 9.30e-04 | Time: 441.9s
+Epoch  18/100 | Train: 0.2493/91.35% | Val: 0.3635/87.86% | LR: 9.22e-04 | Time: 445.1s
+Epoch  19/100 | Train: 0.2414/91.60% | Val: 0.4147/87.10% | LR: 9.14e-04 | Time: 444.3s
+Epoch  20/100 | Train: 0.2253/92.17% | Val: 0.3490/88.34% | LR: 9.05e-04 | Time: 448.1s
+Epoch  21/100 | Train: 0.2160/92.56% | Val: 0.4976/84.96% | LR: 8.95e-04 | Time: 446.3s
+Epoch  22/100 | Train: 0.2005/92.99% | Val: 0.3767/88.18% | LR: 8.85e-04 | Time: 445.7s
+Epoch  23/100 | Train: 0.1937/93.28% | Val: 0.3992/87.66% | LR: 8.75e-04 | Time: 426.4s
+Epoch  24/100 | Train: 0.1825/93.52% | Val: 0.3954/88.22% | LR: 8.64e-04 | Time: 446.3s
+Epoch  25/100 | Train: 0.1690/94.03% | Val: 0.3595/89.06% | LR: 8.54e-04 | Time: 445.6s
+Epoch  26/100 | Train: 0.1590/94.44% | Val: 0.3949/88.24% | LR: 8.42e-04 | Time: 446.7s
+Epoch  27/100 | Train: 0.1583/94.37% | Val: 0.3753/88.96% | LR: 8.31e-04 | Time: 445.8s
+Epoch  28/100 | Train: 0.1425/95.03% | Val: 0.3435/89.68% | LR: 8.19e-04 | Time: 445.8s
+Epoch  29/100 | Train: 0.1382/95.12% | Val: 0.4103/88.12% | LR: 8.06e-04 | Time: 446.9s
+Epoch  30/100 | Train: 0.1358/95.16% | Val: 0.3981/88.36% | LR: 7.94e-04 | Time: 444.6s
+Epoch  31/100 | Train: 0.1203/95.72% | Val: 0.3894/88.74% | LR: 7.81e-04 | Time: 445.1s
+Epoch  32/100 | Train: 0.1173/95.90% | Val: 0.3978/89.02% | LR: 7.68e-04 | Time: 444.5s
+Epoch  33/100 | Train: 0.1121/96.00% | Val: 0.4003/88.84% | LR: 7.55e-04 | Time: 445.3s
+Epoch  34/100 | Train: 0.1015/96.43% | Val: 0.3812/89.66% | LR: 7.41e-04 | Time: 446.5s
+Epoch  35/100 | Train: 0.0982/96.53% | Val: 0.3925/89.56% | LR: 7.27e-04 | Time: 447.3s
+Epoch  36/100 | Train: 0.0908/96.82% | Val: 0.3888/89.32% | LR: 7.13e-04 | Time: 445.4s
+Epoch  37/100 | Train: 0.0928/96.70% | Val: 0.3925/90.04% | LR: 6.99e-04 | Time: 433.2s
+Epoch  38/100 | Train: 0.0833/97.05% | Val: 0.3851/89.82% | LR: 6.84e-04 | Time: 442.6s
+Epoch  39/100 | Train: 0.0799/97.23% | Val: 0.4012/90.10% | LR: 6.69e-04 | Time: 439.2s
+Epoch  40/100 | Train: 0.0789/97.12% | Val: 0.3925/89.76% | LR: 6.55e-04 | Time: 414.8s
+Epoch  41/100 | Train: 0.0707/97.46% | Val: 0.3593/90.86% | LR: 6.39e-04 | Time: 426.1s
+Epoch  42/100 | Train: 0.0702/97.56% | Val: 0.3883/89.76% | LR: 6.24e-04 | Time: 439.5s
+Epoch  43/100 | Train: 0.0646/97.77% | Val: 0.4134/89.82% | LR: 6.09e-04 | Time: 446.1s
+Epoch  44/100 | Train: 0.0642/97.75% | Val: 0.3782/90.18% | LR: 5.94e-04 | Time: 445.1s
+Epoch  45/100 | Train: 0.0610/97.93% | Val: 0.3908/89.98% | LR: 5.78e-04 | Time: 423.6s
+Epoch  46/100 | Train: 0.0576/97.94% | Val: 0.4269/89.86% | LR: 5.63e-04 | Time: 443.9s
+Epoch  47/100 | Train: 0.0510/98.27% | Val: 0.4067/90.46% | LR: 5.47e-04 | Time: 443.9s
+Epoch  48/100 | Train: 0.0476/98.34% | Val: 0.3866/90.96% | LR: 5.31e-04 | Time: 444.3s
+Epoch  49/100 | Train: 0.0445/98.49% | Val: 0.4426/90.34% | LR: 5.16e-04 | Time: 445.6s
+Epoch  50/100 | Train: 0.0439/98.44% | Val: 0.3987/90.38% | LR: 5.00e-04 | Time: 444.7s
+Epoch  51/100 | Train: 0.0396/98.73% | Val: 0.4131/90.62% | LR: 4.84e-04 | Time: 443.5s
+Epoch  52/100 | Train: 0.0397/98.64% | Val: 0.4082/90.96% | LR: 4.69e-04 | Time: 442.0s
+Epoch  53/100 | Train: 0.0366/98.74% | Val: 0.4078/90.94% | LR: 4.53e-04 | Time: 442.1s
+Epoch  54/100 | Train: 0.0385/98.68% | Val: 0.4498/90.38% | LR: 4.37e-04 | Time: 441.6s
+Epoch  55/100 | Train: 0.0342/98.84% | Val: 0.4282/90.56% | LR: 4.22e-04 | Time: 441.1s
+Epoch  56/100 | Train: 0.0326/98.90% | Val: 0.4451/90.56% | LR: 4.06e-04 | Time: 437.2s
+Epoch  57/100 | Train: 0.0279/99.06% | Val: 0.4428/90.86% | LR: 3.91e-04 | Time: 424.6s
+Epoch  58/100 | Train: 0.0271/99.10% | Val: 0.4333/90.58% | LR: 3.76e-04 | Time: 427.0s
+Epoch  59/100 | Train: 0.0277/99.03% | Val: 0.4483/90.60% | LR: 3.61e-04 | Time: 442.3s
+Epoch  60/100 | Train: 0.0265/99.07% | Val: 0.4467/90.60% | LR: 3.45e-04 | Time: 443.3s
+Epoch  61/100 | Train: 0.0237/99.21% | Val: 0.4419/91.16% | LR: 3.31e-04 | Time: 443.0s
+Epoch  62/100 | Train: 0.0234/99.21% | Val: 0.4439/90.46% | LR: 3.16e-04 | Time: 464.5s
+Epoch  63/100 | Train: 0.0198/99.37% | Val: 0.4616/90.80% | LR: 3.01e-04 | Time: 474.4s
+Epoch  64/100 | Train: 0.0177/99.42% | Val: 0.4360/90.92% | LR: 2.87e-04 | Time: 444.6s
+Epoch  65/100 | Train: 0.0178/99.44% | Val: 0.4398/91.00% | LR: 2.73e-04 | Time: 444.0s
+Epoch  66/100 | Train: 0.0188/99.40% | Val: 0.4573/91.22% | LR: 2.59e-04 | Time: 445.7s
+Epoch  67/100 | Train: 0.0164/99.45% | Val: 0.4541/91.30% | LR: 2.45e-04 | Time: 437.4s
+Epoch  68/100 | Train: 0.0165/99.49% | Val: 0.4793/90.92% | LR: 2.32e-04 | Time: 435.2s
+Epoch  69/100 | Train: 0.0165/99.46% | Val: 0.4639/91.12% | LR: 2.19e-04 | Time: 444.4s
+Epoch  70/100 | Train: 0.0138/99.58% | Val: 0.4675/90.68% | LR: 2.06e-04 | Time: 444.3s
+Epoch  71/100 | Train: 0.0135/99.54% | Val: 0.4640/91.08% | LR: 1.94e-04 | Time: 444.8s
+Epoch  72/100 | Train: 0.0117/99.63% | Val: 0.4833/91.30% | LR: 1.81e-04 | Time: 469.8s
+Epoch  73/100 | Train: 0.0111/99.65% | Val: 0.4804/90.96% | LR: 1.69e-04 | Time: 452.3s
+Epoch  74/100 | Train: 0.0114/99.65% | Val: 0.4615/91.60% | LR: 1.58e-04 | Time: 426.1s
+Epoch  75/100 | Train: 0.0097/99.72% | Val: 0.4715/91.26% | LR: 1.46e-04 | Time: 424.5s
+Epoch  76/100 | Train: 0.0105/99.68% | Val: 0.4694/91.62% | LR: 1.36e-04 | Time: 425.6s
+Epoch  77/100 | Train: 0.0083/99.75% | Val: 0.4695/91.24% | LR: 1.25e-04 | Time: 427.2s
+Epoch  78/100 | Train: 0.0080/99.76% | Val: 0.4773/91.18% | LR: 1.15e-04 | Time: 427.5s
+Epoch  79/100 | Train: 0.0074/99.78% | Val: 0.4529/91.66% | LR: 1.05e-04 | Time: 424.3s
+Epoch  80/100 | Train: 0.0076/99.78% | Val: 0.4566/91.30% | LR: 9.55e-05 | Time: 425.2s
+Epoch  81/100 | Train: 0.0068/99.80% | Val: 0.4476/91.64% | LR: 8.65e-05 | Time: 424.2s
+Epoch  82/100 | Train: 0.0067/99.81% | Val: 0.4565/91.52% | LR: 7.78e-05 | Time: 424.5s
+Epoch  83/100 | Train: 0.0057/99.86% | Val: 0.4623/91.62% | LR: 6.96e-05 | Time: 425.2s
+Epoch  84/100 | Train: 0.0060/99.85% | Val: 0.4523/91.44% | LR: 6.18e-05 | Time: 423.6s
+Epoch  85/100 | Train: 0.0058/99.84% | Val: 0.4586/91.40% | LR: 5.45e-05 | Time: 425.0s
+Epoch  86/100 | Train: 0.0052/99.86% | Val: 0.4588/91.48% | LR: 4.76e-05 | Time: 425.5s
+Epoch  87/100 | Train: 0.0047/99.87% | Val: 0.4550/91.32% | LR: 4.11e-05 | Time: 423.3s
+Epoch  88/100 | Train: 0.0051/99.86% | Val: 0.4538/91.34% | LR: 3.51e-05 | Time: 423.2s
+Epoch  89/100 | Train: 0.0052/99.84% | Val: 0.4567/91.56% | LR: 2.96e-05 | Time: 426.4s
+Epoch  90/100 | Train: 0.0049/99.85% | Val: 0.4543/91.42% | LR: 2.45e-05 | Time: 424.0s
+Epoch  91/100 | Train: 0.0042/99.91% | Val: 0.4672/91.54% | LR: 1.99e-05 | Time: 423.7s
+Epoch  92/100 | Train: 0.0043/99.87% | Val: 0.4621/91.62% | LR: 1.57e-05 | Time: 424.6s
+Epoch  93/100 | Train: 0.0038/99.92% | Val: 0.4639/91.60% | LR: 1.20e-05 | Time: 424.1s
+Epoch  94/100 | Train: 0.0039/99.91% | Val: 0.4578/91.40% | LR: 8.86e-06 | Time: 424.6s
+Epoch  95/100 | Train: 0.0042/99.90% | Val: 0.4663/91.46% | LR: 6.16e-06 | Time: 425.7s
+Epoch  96/100 | Train: 0.0039/99.89% | Val: 0.4600/91.44% | LR: 3.94e-06 | Time: 422.5s
+Epoch  97/100 | Train: 0.0042/99.88% | Val: 0.4623/91.62% | LR: 2.22e-06 | Time: 423.3s
+Epoch  98/100 | Train: 0.0040/99.90% | Val: 0.4649/91.54% | LR: 9.87e-07 | Time: 423.9s
+Epoch  99/100 | Train: 0.0043/99.90% | Val: 0.4625/91.60% | LR: 2.47e-07 | Time: 422.6s
+Epoch 100/100 | Train: 0.0039/99.89% | Val: 0.4649/91.52% | LR: 0.00e+00 | Time: 423.9s

wandb/run-20250717_042842-06r19hoq/files/requirements.txt

@@ -0,0 +1,119 @@
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+alembic==1.16.2
+altair==5.5.0
+annotated-types==0.7.0
+anyio==4.9.0
+attrs==25.3.0
+audioop-lts==0.2.1
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+Bottleneck==1.4.2
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+colorama==0.4.6
+colorlog==6.9.0
+contourpy==1.3.1
+cycler==0.11.0
+fastapi==0.116.1
+ffmpy==0.6.0
+filelock==3.18.0
+fonttools==4.55.3
+fsspec==2025.5.1
+gitdb==4.0.12
+GitPython==3.1.44
+gradio==5.37.0
+gradio_client==1.10.4
+greenlet==3.2.3
+groovy==0.1.2
+h11==0.16.0
+httpcore==1.0.9
+httpx==0.28.1
+huggingface-hub==0.33.4
+idna==3.10
+imbalanced-learn==0.13.0
+imblearn==0.0
+Jinja2==3.1.6
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wandb/run-20250717_042842-06r19hoq/logs/debug-internal.log

@@ -0,0 +1,13 @@
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