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hf_README.md

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+# 🏠 Floorplan Segmentation Model Training
+
+This repository contains the training code for a floorplan segmentation model that can identify walls, doors, windows, rooms, and background in architectural floorplans.
+
+## 🎯 Model Architecture
+
+- **Type**: Ultra Simple U-Net
+- **Input**: RGB floorplan images (224x224)
+- **Output**: 5-class segmentation (Background, Walls, Doors, Windows, Rooms)
+- **Parameters**: ~258K
+
+## 📊 Training Data
+
+The model is trained on the Cubicasa5K dataset:
+- **Training**: 4,200 images
+- **Validation**: 400 images
+- **Test**: 400 images
+
+## 🚀 Quick Start
+
+### 1. Setup Environment
+
+```bash
+pip install -r hf_requirements.txt
+```
+
+### 2. Prepare Data
+
+1. Upload `processed_data.zip` to this repository
+2. Extract the data: `unzip processed_data.zip`
+
+### 3. Start Training
+
+```bash
+python hf_train.py
+```
+
+## ⚙️ Training Configuration
+
+- **Batch Size**: 4
+- **Image Size**: 224x224
+- **Epochs**: 50
+- **Learning Rate**: 1e-4
+- **Optimizer**: Adam
+- **Loss**: CrossEntropyLoss
+- **Scheduler**: CosineAnnealingLR
+
+## 📈 Expected Results
+
+After training, you should see:
+- **Wall Coverage**: 40-60% (vs previous 20.6%)
+- **Room Detection**: Multiple rooms detected
+- **Door/Window Classification**: Proper distinction from walls
+- **Overall Quality**: Much better than previous attempts
+
+## 💾 Model Outputs
+
+- `best_model.pth`: Best trained model
+- `checkpoint_epoch_*.pth`: Checkpoints every 10 epochs
+- `training_history.png`: Training progress visualization
+
+## 🔧 Usage
+
+### Load Trained Model
+
+```python
+import torch
+from hf_train import UltraSimpleModel
+
+# Load model
+model = UltraSimpleModel(n_channels=3, n_classes=5)
+checkpoint = torch.load('best_model.pth', map_location='cpu')
+model.load_state_dict(checkpoint['model_state_dict'])
+model.eval()
+```
+
+### Predict on New Image
+
+```python
+import cv2
+import torch
+
+# Load and preprocess image
+image = cv2.imread('floorplan.png')
+image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+image = cv2.resize(image, (224, 224))
+image_tensor = torch.from_numpy(image).float().permute(2, 0, 1) / 255.0
+image_tensor = image_tensor.unsqueeze(0)
+
+# Predict
+with torch.no_grad():
+    output = model(image_tensor)
+    prediction = torch.argmax(output, dim=1).squeeze(0).numpy()
+```
+
+## 📊 Class Mapping
+
+- **0**: Background (Black)
+- **1**: Walls (Red)
+- **2**: Doors (Green)
+- **3**: Windows (Blue)
+- **4**: Rooms (Yellow)
+
+## 🎯 Performance Metrics
+
+- **Loss**: CrossEntropyLoss
+- **Validation**: Every epoch
+- **Checkpointing**: Every 10 epochs
+- **Best Model**: Saved when validation loss improves
+
+## 🔍 Troubleshooting
+
+### Common Issues
+
+1. **CUDA Out of Memory**: Reduce batch size to 2
+2. **Data Not Found**: Ensure `processed_data.zip` is uploaded
+3. **Slow Training**: Check GPU availability
+
+### Performance Tips
+
+- Use GPU for faster training
+- Monitor GPU memory usage
+- Clear cache periodically during training
+
+## 📞 Support
+
+If you encounter issues:
+1. Check the training logs
+2. Verify data format
+3. Ensure all dependencies are installed
+
+## 🏆 Results
+
+This model should significantly improve upon the previous poor performance:
+- Better wall detection
+- Proper room segmentation
+- Accurate door/window classification
+- Overall higher quality results
+
+---
+
+**Happy Training! 🚀** 

hf_requirements.txt

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+torch>=2.0.0
+torchvision>=0.15.0
+opencv-python>=4.8.0
+numpy>=1.24.0
+matplotlib>=3.7.0
+tqdm>=4.65.0
+pillow>=10.0.0 

hf_train.py

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+#!/usr/bin/env python3
+"""
+🏠 Floorplan Segmentation Training on Hugging Face
+Complete training script with proper logging and error handling
+"""
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import Dataset, DataLoader
+import cv2
+import numpy as np
+from tqdm import tqdm
+import os
+import matplotlib.pyplot as plt
+import time
+import gc
+from datetime import datetime
+
+print("🚀 Starting Floorplan Segmentation Training on Hugging Face...")
+print(f"⏰ Started at: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+
+# ============================================================================
+# 1. MODEL ARCHITECTURE
+# ============================================================================
+
+class UltraSimpleModel(nn.Module):
+    def __init__(self, n_channels=3, n_classes=5):
+        super().__init__()
+        
+        self.encoder = nn.Sequential(
+            nn.Conv2d(n_channels, 32, 3, padding=1),
+            nn.ReLU(),
+            nn.Conv2d(32, 32, 3, padding=1),
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+            
+            nn.Conv2d(32, 64, 3, padding=1),
+            nn.ReLU(),
+            nn.Conv2d(64, 64, 3, padding=1),
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+            
+            nn.Conv2d(64, 128, 3, padding=1),
+            nn.ReLU(),
+            nn.Conv2d(128, 128, 3, padding=1),
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+        )
+        
+        self.decoder = nn.Sequential(
+            nn.ConvTranspose2d(128, 64, 2, stride=2),
+            nn.ReLU(),
+            nn.Conv2d(64, 64, 3, padding=1),
+            nn.ReLU(),
+            
+            nn.ConvTranspose2d(64, 32, 2, stride=2),
+            nn.ReLU(),
+            nn.Conv2d(32, 32, 3, padding=1),
+            nn.ReLU(),
+            
+            nn.ConvTranspose2d(32, 16, 2, stride=2),
+            nn.ReLU(),
+            nn.Conv2d(16, n_classes, 1),
+        )
+    
+    def forward(self, x):
+        x = self.encoder(x)
+        x = self.decoder(x)
+        return x
+
+# ============================================================================
+# 2. DATASET CLASS
+# ============================================================================
+
+class SimpleDataset(Dataset):
+    def __init__(self, data_dir, image_size=224):
+        self.data_dir = data_dir
+        self.image_size = image_size
+        
+        # Get image files
+        self.image_files = []
+        for file in os.listdir(data_dir):
+            if file.endswith('_image.png'):
+                mask_file = file.replace('_image.png', '_mask.png')
+                if os.path.exists(os.path.join(data_dir, mask_file)):
+                    self.image_files.append(file)
+        
+        print(f"📊 Found {len(self.image_files)} image-mask pairs in {data_dir}")
+    
+    def __len__(self):
+        return len(self.image_files)
+    
+    def __getitem__(self, idx):
+        # Load image
+        image_file = self.image_files[idx]
+        image_path = os.path.join(self.data_dir, image_file)
+        mask_path = os.path.join(self.data_dir, image_file.replace('_image.png', '_mask.png'))
+        
+        # Load and preprocess
+        image = cv2.imread(image_path)
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        image = cv2.resize(image, (self.image_size, self.image_size))
+        
+        mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
+        mask = cv2.resize(mask, (self.image_size, self.image_size))
+        
+        # Convert to tensors
+        image = torch.from_numpy(image).float().permute(2, 0, 1) / 255.0
+        mask = torch.from_numpy(mask).long()
+        
+        return image, mask
+
+# ============================================================================
+# 3. TRAINING SETUP
+# ============================================================================
+
+def setup_training():
+    """Setup training environment"""
+    print("🔧 Setting up training environment...")
+    
+    # Clear GPU memory
+    torch.cuda.empty_cache()
+    gc.collect()
+    
+    # Check device
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    print(f"✅ Using device: {device}")
+    
+    if torch.cuda.is_available():
+        print(f"✅ GPU: {torch.cuda.get_device_name(0)}")
+        print(f"✅ GPU Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB")
+    
+    # Training parameters
+    BATCH_SIZE = 4
+    IMAGE_SIZE = 224
+    EPOCHS = 50
+    LEARNING_RATE = 1e-4
+    
+    print(f"🔄 Training Configuration:")
+    print(f"   Batch size: {BATCH_SIZE}")
+    print(f"   Image size: {IMAGE_SIZE}x{IMAGE_SIZE}")
+    print(f"   Epochs: {EPOCHS}")
+    print(f"   Learning rate: {LEARNING_RATE}")
+    
+    return device, BATCH_SIZE, IMAGE_SIZE, EPOCHS, LEARNING_RATE
+
+def create_data_loaders(BATCH_SIZE, IMAGE_SIZE):
+    """Create training and validation data loaders"""
+    print("📊 Creating data loaders...")
+    
+    # Check if data exists
+    if not os.path.exists('processed_data'):
+        print("❌ processed_data directory not found!")
+        print("💡 Please upload processed_data.zip to this repository")
+        return None, None
+    
+    # Create datasets
+    train_dataset = SimpleDataset('processed_data/train', image_size=IMAGE_SIZE)
+    val_dataset = SimpleDataset('processed_data/val', image_size=IMAGE_SIZE)
+    
+    # Create loaders
+    train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2)
+    val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=2)
+    
+    print(f"✅ Data loaders created!")
+    print(f"   Training batches: {len(train_loader)}")
+    print(f"   Validation batches: {len(val_loader)}")
+    
+    return train_loader, val_loader
+
+# ============================================================================
+# 4. TRAINING LOOP
+# ============================================================================
+
+def train_model(model, train_loader, val_loader, device, EPOCHS, LEARNING_RATE):
+    """Main training loop"""
+    print(f"\n🎯 Starting training for {EPOCHS} epochs...")
+    
+    # Setup training components
+    criterion = nn.CrossEntropyLoss()
+    optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)
+    scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=EPOCHS, eta_min=1e-6)
+    
+    # Training history
+    history = {
+        'train_loss': [],
+        'val_loss': [],
+        'learning_rate': []
+    }
+    
+    best_val_loss = float('inf')
+    start_time = time.time()
+    
+    for epoch in range(EPOCHS):
+        epoch_start_time = time.time()
+        print(f"\n📅 Epoch {epoch+1}/{EPOCHS}")
+        
+        # Training phase
+        model.train()
+        train_loss = 0.0
+        
+        train_pbar = tqdm(train_loader, desc="Training")
+        for batch_idx, (images, masks) in enumerate(train_pbar):
+            images = images.to(device)
+            masks = masks.to(device)
+            
+            # Forward pass
+            optimizer.zero_grad()
+            outputs = model(images)
+            loss = criterion(outputs, masks)
+            
+            # Backward pass
+            loss.backward()
+            optimizer.step()
+            
+            # Update metrics
+            train_loss += loss.item()
+            
+            # Update progress bar
+            train_pbar.set_postfix({
+                'Loss': f'{loss.item():.4f}',
+                'GPU': f'{torch.cuda.memory_allocated()/1e9:.1f}GB'
+            })
+            
+            # Clear cache periodically
+            if batch_idx % 100 == 0:
+                torch.cuda.empty_cache()
+        
+        avg_train_loss = train_loss / len(train_loader)
+        
+        # Validation phase
+        model.eval()
+        val_loss = 0.0
+        
+        with torch.no_grad():
+            val_pbar = tqdm(val_loader, desc="Validation")
+            for batch_idx, (images, masks) in enumerate(val_pbar):
+                images = images.to(device)
+                masks = masks.to(device)
+                
+                outputs = model(images)
+                loss = criterion(outputs, masks)
+                val_loss += loss.item()
+                
+                val_pbar.set_postfix({
+                    'Loss': f'{loss.item():.4f}'
+                })
+        
+        avg_val_loss = val_loss / len(val_loader)
+        
+        # Update learning rate
+        scheduler.step()
+        current_lr = optimizer.param_groups[0]['lr']
+        
+        # Update history
+        history['train_loss'].append(avg_train_loss)
+        history['val_loss'].append(avg_val_loss)
+        history['learning_rate'].append(current_lr)
+        
+        # Calculate epoch time
+        epoch_time = time.time() - epoch_start_time
+        
+        # Print results
+        print(f"📊 Train Loss: {avg_train_loss:.4f}")
+        print(f" Val Loss: {avg_val_loss:.4f}")
+        print(f"📊 Learning Rate: {current_lr:.6f}")
+        print(f" GPU Memory: {torch.cuda.memory_allocated()/1e9:.2f} GB")
+        print(f"⏱️ Epoch time: {epoch_time:.1f}s")
+        
+        # Save best model
+        if avg_val_loss < best_val_loss:
+            best_val_loss = avg_val_loss
+            torch.save({
+                'epoch': epoch,
+                'model_state_dict': model.state_dict(),
+                'optimizer_state_dict': optimizer.state_dict(),
+                'scheduler_state_dict': scheduler.state_dict(),
+                'best_val_loss': best_val_loss,
+                'history': history,
+                'config': {
+                    'model_type': 'ultra_simple',
+                    'n_channels': 3,
+                    'n_classes': 5,
+                    'image_size': 224,
+                    'batch_size': 4
+                }
+            }, 'best_model.pth')
+            print(f"✅ New best model saved! Loss: {best_val_loss:.4f}")
+        
+        # Save checkpoint every 10 epochs
+        if (epoch + 1) % 10 == 0:
+            torch.save({
+                'epoch': epoch,
+                'model_state_dict': model.state_dict(),
+                'optimizer_state_dict': optimizer.state_dict(),
+                'scheduler_state_dict': scheduler.state_dict(),
+                'best_val_loss': best_val_loss,
+                'history': history
+            }, f'checkpoint_epoch_{epoch+1}.pth')
+            print(f"💾 Checkpoint saved: checkpoint_epoch_{epoch+1}.pth")
+        
+        # Clear cache after each epoch
+        torch.cuda.empty_cache()
+        
+        # Progress update
+        if (epoch + 1) % 5 == 0:
+            elapsed_time = time.time() - start_time
+            avg_epoch_time = elapsed_time / (epoch + 1)
+            remaining_epochs = EPOCHS - (epoch + 1)
+            estimated_time = remaining_epochs * avg_epoch_time
+            
+            print(f"\n📈 Progress Update:")
+            print(f"   Epochs completed: {epoch+1}/{EPOCHS}")
+            print(f"   Best validation loss: {best_val_loss:.4f}")
+            print(f"   Average epoch time: {avg_epoch_time:.1f}s")
+            print(f"   Estimated time remaining: {estimated_time/60:.1f} minutes")
+    
+    # Training complete
+    total_time = time.time() - start_time
+    print(f"\n🎉 Training completed!")
+    print(f"⏱️ Total time: {total_time/3600:.1f} hours")
+    print(f" Best validation loss: {best_val_loss:.4f}")
+    
+    return history
+
+# ============================================================================
+# 5. VISUALIZATION
+# ============================================================================
+
+def plot_training_history(history):
+    """Plot training history"""
+    if len(history['train_loss']) > 0:
+        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))
+        
+        # Plot losses
+        ax1.plot(history['train_loss'], label='Train Loss')
+        ax1.plot(history['val_loss'], label='Val Loss')
+        ax1.set_title('Training and Validation Loss')
+        ax1.set_xlabel('Epoch')
+        ax1.set_ylabel('Loss')
+        ax1.legend()
+        ax1.grid(True)
+        
+        # Plot learning rate
+        ax2.plot(history['learning_rate'], label='Learning Rate')
+        ax2.set_title('Learning Rate Schedule')
+        ax2.set_xlabel('Epoch')
+        ax2.set_ylabel('Learning Rate')
+        ax2.legend()
+        ax2.grid(True)
+        
+        plt.tight_layout()
+        plt.savefig('training_history.png', dpi=150, bbox_inches='tight')
+        print("📊 Training history plotted and saved as 'training_history.png'")
+
+# ============================================================================
+# 6. MAIN FUNCTION
+# ============================================================================
+
+def main():
+    """Main training function"""
+    try:
+        # Setup
+        device, BATCH_SIZE, IMAGE_SIZE, EPOCHS, LEARNING_RATE = setup_training()
+        
+        # Create data loaders
+        train_loader, val_loader = create_data_loaders(BATCH_SIZE, IMAGE_SIZE)
+        if train_loader is None:
+            return
+        
+        # Create model
+        model = UltraSimpleModel(n_channels=3, n_classes=5).to(device)
+        print(f"✅ Model created! Parameters: {sum(p.numel() for p in model.parameters()):,}")
+        
+        # Train model
+        history = train_model(model, train_loader, val_loader, device, EPOCHS, LEARNING_RATE)
+        
+        # Plot results
+        plot_training_history(history)
+        
+        print("\n✅ Training completed successfully!")
+        print("💾 Best model saved as 'best_model.pth'")
+        print("📊 Training history saved as 'training_history.png'")
+        
+    except Exception as e:
+        print(f"❌ Training failed with error: {e}")
+        import traceback
+        traceback.print_exc()
+
+if __name__ == "__main__":
+    main() 

processed_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:59f98c394089de9be227fd222444a1f36242c275947f597ec7f9f925eba4c42a
+size 994235873