diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..93dad401b2399440bd5975aaad20f04befc51bc0 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,93 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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diff --git a/datasets/bebidas_gas/dataset/Agua Tonica Schweppes 310 Ml Lata Pack 6 -ID- 6028.jpg b/datasets/bebidas_gas/dataset/Agua Tonica Schweppes 310 Ml Lata Pack 6 -ID- 6028.jpg
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diff --git a/model_curriculum4/prototypical_model_best_bebidas_gas.pth b/model_curriculum4/prototypical_model_best_bebidas_gas.pth
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+size 4403894
diff --git a/model_curriculum4/prototypical_model_best_detergentes.pth b/model_curriculum4/prototypical_model_best_detergentes.pth
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--- /dev/null
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+version https://git-lfs.github.com/spec/v1
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+size 4397674
diff --git a/redes_prototipicas_tvt5.py b/redes_prototipicas_tvt5.py
new file mode 100644
index 0000000000000000000000000000000000000000..7928a26220b99168ac8684718647242df308a2cd
--- /dev/null
+++ b/redes_prototipicas_tvt5.py
@@ -0,0 +1,594 @@
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision.transforms as transforms
+from PIL import Image
+import os
+import numpy as np
+from pathlib import Path
+from torch.utils.data import Dataset, DataLoader
+import random
+from collections import defaultdict
+import json
+import math
+import torch
+
+def setup_device():
+    """Configura y verifica el dispositivo GPU"""
+    if torch.cuda.is_available():
+        device = torch.device('cuda')
+        print(f"✅ GPU disponible: {torch.cuda.get_device_name(0)}")
+        torch.backends.cudnn.benchmark = True
+        torch.cuda.empty_cache()
+    else:
+        device = torch.device('cpu')
+        print("⚠️  GPU no disponible, usando CPU")
+    return device
+
+class ProductDataset(Dataset):
+    """Dataset mejorado con curriculum learning para redes prototípicas"""
+    def __init__(self, data_path, transform=None, mode='train', 
+                 min_n_way=6, max_n_way=None, current_epoch=0, total_epochs=100):
+        self.data_path = Path(data_path)
+        self.transform = transform
+        self.mode = mode
+        self.min_n_way = min_n_way
+        self.current_epoch = current_epoch
+        self.total_epochs = total_epochs
+        self.load_data()
+        
+        # Configurar max_n_way basado en clases disponibles
+        self.max_n_way = max_n_way if max_n_way else len(self.classes)
+        
+    def load_data(self):
+        """Carga las imágenes y organiza por clases"""
+        self.class_to_images = defaultdict(list)
+        valid_extensions = {'.jpg', '.jpeg', '.png', '.bmp', '.tiff'}
+        
+        for img_path in self.data_path.glob('*'):
+            if img_path.suffix.lower() in valid_extensions:
+                class_parts = img_path.stem.split('_')[:-1]
+                class_name = '_'.join(class_parts) if class_parts else img_path.stem
+                self.class_to_images[class_name].append(str(img_path))
+                
+        self.classes = list(self.class_to_images.keys())
+        print(f"Clases encontradas en {self.mode}: {len(self.classes)}")
+        
+    def get_current_n_way(self):
+        """Calcula n_way basado en el progreso del entrenamiento"""
+        if self.mode != 'train':
+            # Para validación/test, usar un n_way fijo más alto
+            return min(len(self.classes), max(10, self.min_n_way * 2))
+        
+        # Curriculum learning: aumentar n_way gradualmente
+        progress = self.current_epoch / max(1, self.total_epochs)
+        
+        # Función de crecimiento suave (sigmoide modificada)
+        # Empieza con min_n_way y crece hasta max_n_way
+        range_n_way = self.max_n_way - self.min_n_way
+        
+        # Usar una función de crecimiento más gradual
+        # Los primeros 20% epochs: crecimiento lento
+        # 20%-80%: crecimiento rápido
+        # Últimos 20%: estabilización
+        if progress < 0.2:
+            growth = progress * 2.5  # 0 a 0.5
+        elif progress < 0.8:
+            growth = 0.5 + (progress - 0.2) * (0.4 / 0.6)  # 0.5 a 0.9
+        else:
+            growth = 0.9 + (progress - 0.8) * 0.5  # 0.9 a 1.0
+        
+        current_n_way = int(self.min_n_way + range_n_way * growth)
+        return min(current_n_way, len(self.classes))
+    
+    def __len__(self):
+        # OPTIMIZADO para dataset pequeño: menos episodios por época
+        return 600 if self.mode == 'train' else 150  # Reducido de 1500/300
+    
+    def __getitem__(self, idx):
+        """Genera un episodio con n_way adaptativo"""
+        n_way = self.get_current_n_way()
+        
+        # Para episodios con muchas clases, usar menos shots por clase
+        if n_way > 12:
+            max_n_support = 1
+            max_n_query = 1
+        else:
+            max_n_support = 2
+            max_n_query = 2
+        
+        selected_classes = random.sample(self.classes, n_way)
+        
+        support_images = []
+        support_labels = []
+        query_images = []
+        query_labels = []
+        
+        # NUEVO: Contar support y query por clase para cálculo correcto
+        support_count_per_class = []
+        query_count_per_class = []
+        
+        for i, cls in enumerate(selected_classes):
+            available_images = self.class_to_images[cls]
+            
+            # Contador para esta clase específica
+            class_support_count = 0
+            class_query_count = 0
+            
+            if len(available_images) >= 2:
+                n_support = min(max_n_support, len(available_images) // 2)
+                n_query = min(max_n_query, len(available_images) - n_support)
+                selected_images = random.sample(available_images, n_support + n_query)
+                support_imgs = selected_images[:n_support]
+                query_imgs = selected_images[n_support:n_support + n_query]
+            else:
+                support_imgs = available_images
+                query_imgs = available_images
+            
+            # Cargar imágenes de support
+            for img_path in support_imgs:
+                try:
+                    image = Image.open(img_path).convert('RGB')
+                    if self.transform:
+                        image = self.transform(image)
+                    support_images.append(image)
+                    support_labels.append(i)
+                    class_support_count += 1  # Contar exitosas
+                except Exception as e:
+                    continue
+            
+            # Cargar imágenes de query
+            for img_path in query_imgs:
+                try:
+                    image = Image.open(img_path).convert('RGB')
+                    if self.transform:
+                        image = self.transform(image)
+                    query_images.append(image)
+                    query_labels.append(i)
+                    class_query_count += 1  # Contar exitosas
+                except Exception as e:
+                    continue
+            
+            # Guardar counts para esta clase
+            support_count_per_class.append(class_support_count)
+            query_count_per_class.append(class_query_count)
+        
+        # Asegurar que tenemos datos válidos
+        if len(support_images) == 0 or len(query_images) == 0:
+            # Fallback
+            all_images = support_images + query_images if support_images or query_images else []
+            all_labels = support_labels + query_labels if support_labels or query_labels else []
+            
+            if len(all_images) > 0:
+                mid = len(all_images) // 2
+                support_images = all_images[:mid] if mid > 0 else all_images
+                query_images = all_images[mid:] if mid > 0 else all_images
+                support_labels = all_labels[:mid] if mid > 0 else all_labels
+                query_labels = all_labels[mid:] if mid > 0 else all_labels
+                
+                # Recalcular counts después del fallback
+                support_count_per_class = [len(support_images) // max(n_way, 1)] * n_way
+                query_count_per_class = [len(query_images) // max(n_way, 1)] * n_way
+        
+        # CORRECCIÓN CRÍTICA: n_support debe ser el máximo support POR CLASE
+        actual_n_support = max(support_count_per_class) if support_count_per_class else 0
+        actual_n_query = max(query_count_per_class) if query_count_per_class else 0
+        
+        return {
+            'support_images': torch.stack(support_images) if support_images else torch.empty(0, 3, 224, 224),
+            'support_labels': torch.tensor(support_labels) if support_labels else torch.empty(0, dtype=torch.long),
+            'query_images': torch.stack(query_images) if query_images else torch.empty(0, 3, 224, 224),
+            'query_labels': torch.tensor(query_labels) if query_labels else torch.empty(0, dtype=torch.long),
+            'n_way': n_way,
+            'n_support': actual_n_support,  # CORREGIDO: máximo support por clase
+            'n_query': actual_n_query       # CORREGIDO: máximo query por clase
+        }
+
+class ConvEncoder(nn.Module):
+    """Encoder mejorado con más capacidad para manejar muchas clases"""
+    def __init__(self, input_channels=3, hidden_dim=64, output_dim=256):
+        super(ConvEncoder, self).__init__()
+        
+        self.encoder = nn.Sequential(
+            # Primer bloque
+            nn.Conv2d(input_channels, hidden_dim, 3, padding=1),
+            nn.BatchNorm2d(hidden_dim),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(2),
+            
+            # Segundo bloque
+            nn.Conv2d(hidden_dim, hidden_dim * 2, 3, padding=1),
+            nn.BatchNorm2d(hidden_dim * 2),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(2),
+            
+            # Tercer bloque
+            nn.Conv2d(hidden_dim * 2, hidden_dim * 4, 3, padding=1),
+            nn.BatchNorm2d(hidden_dim * 4),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(2),
+            
+            # Cuarto bloque
+            nn.Conv2d(hidden_dim * 4, hidden_dim * 4, 3, padding=1),
+            nn.BatchNorm2d(hidden_dim * 4),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(2),
+        )
+        
+        # Pooling adaptivo
+        self.adaptive_pool = nn.AdaptiveAvgPool2d(1)
+        
+        # Proyección final con más capacidad
+        self.fc = nn.Sequential(
+            nn.Linear(hidden_dim * 4, hidden_dim * 4),
+            nn.ReLU(inplace=True),
+            nn.Dropout(0.2),
+            nn.Linear(hidden_dim * 4, output_dim),
+            nn.ReLU(inplace=True),
+            nn.Dropout(0.1)
+        )
+        
+        self.output_dim = output_dim
+        
+    def forward(self, x):
+        if x.size(0) == 0:
+            return torch.empty(0, self.output_dim, device=x.device)
+            
+        x = self.encoder(x)
+        x = self.adaptive_pool(x)
+        x = x.view(x.size(0), -1)
+        x = self.fc(x)
+        return x
+
+class PrototypicalNetwork(nn.Module):
+    """Red Prototípica con mejoras para clasificación global"""
+    def __init__(self, encoder):
+        super(PrototypicalNetwork, self).__init__()
+        self.encoder = encoder
+        
+    def forward(self, support, query, n_way, n_support):
+        if support.size(0) == 0 or query.size(0) == 0:
+            return torch.empty(0, n_way), torch.empty(0, n_way)
+        
+        # Extraer características
+        support_features = self.encoder(support)
+        query_features = self.encoder(query)
+        
+        # Normalizar características (importante para muchas clases)
+        support_features = F.normalize(support_features, p=2, dim=1)
+        query_features = F.normalize(query_features, p=2, dim=1)
+        
+        # Calcular prototipos
+        if n_support > 0 and support_features.size(0) >= n_way * n_support:
+            support_features = support_features.view(n_way, n_support, -1)
+            prototypes = support_features.mean(dim=1)
+        else:
+            prototypes = support_features[:n_way] if support_features.size(0) >= n_way else support_features
+            if prototypes.size(0) < n_way:
+                repeat_factor = (n_way + prototypes.size(0) - 1) // prototypes.size(0)
+                prototypes = prototypes.repeat(repeat_factor, 1)[:n_way]
+        
+        # Normalizar prototipos
+        prototypes = F.normalize(prototypes, p=2, dim=1)
+        
+        # Calcular distancias (usar cosine similarity es mejor para muchas clases)
+        similarities = torch.mm(query_features, prototypes.t())
+        
+        # Escalar por temperatura (learnable o fija)
+        temperature = 10.0
+        log_probabilities = F.log_softmax(similarities * temperature, dim=1)
+        
+        return log_probabilities, -similarities
+
+def custom_collate_fn(batch):
+    """Función de collate personalizada para manejar episodios con diferentes tamaños"""
+    # Simplemente devolver una lista de episodios en lugar de intentar apilarlos
+    return batch
+
+class PrototypicalTrainerWithCurriculum:
+    """Entrenador con curriculum learning para clasificación global"""
+    def __init__(self, image_size=224, hidden_dim=64, output_dim=256):
+        self.device = setup_device()
+        self.image_size = image_size
+        
+        # Transformaciones mejoradas
+        self.train_transform = transforms.Compose([
+            transforms.Resize((image_size + 32, image_size + 32)),
+            transforms.RandomCrop((image_size, image_size)),
+            transforms.RandomHorizontalFlip(p=0.5),
+            transforms.RandomRotation(15),
+            transforms.ColorJitter(brightness=0.3, contrast=0.3, 
+                                 saturation=0.3, hue=0.15),
+            transforms.RandomGrayscale(p=0.1),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406], 
+                               std=[0.229, 0.224, 0.225])
+        ])
+        
+        self.eval_transform = transforms.Compose([
+            transforms.Resize((image_size, image_size)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406], 
+                               std=[0.229, 0.224, 0.225])
+        ])
+        
+        # Modelo con más capacidad
+        self.encoder = ConvEncoder(hidden_dim=hidden_dim, output_dim=output_dim).to(self.device)
+        self.model = PrototypicalNetwork(self.encoder).to(self.device)
+        
+    def train(self, train_path, valid_path, test_path, epochs=150, lr=0.001, 
+              save_path='prototypical_model.pth', batch_size=8,  # OPTIMIZADO: batch_size aumentado
+              min_n_way=6, max_n_way=None):
+        """Entrena con curriculum learning optimizado"""
+        print(f"\n=== ENTRENAMIENTO OPTIMIZADO ===")
+        print(f"Dispositivo: {self.device}")
+        print(f"Batch size: {batch_size}")
+        
+        # Configurar rutas de guardado
+        save_dir = os.path.dirname(save_path)
+        if save_dir and not os.path.exists(save_dir):
+            os.makedirs(save_dir)
+        
+        best_model_path = save_path
+        last_model_path = save_path.replace('.pth', '_last_epoch.pth')
+        
+        # Crear datasets
+        train_dataset = ProductDataset(
+            train_path, 
+            transform=self.train_transform, 
+            mode='train',
+            min_n_way=min_n_way,
+            max_n_way=max_n_way,
+            current_epoch=0,
+            total_epochs=epochs
+        )
+        
+        valid_dataset = ProductDataset(
+            valid_path, 
+            transform=self.eval_transform, 
+            mode='valid',
+            min_n_way=min_n_way,
+            max_n_way=max_n_way
+        )
+        
+        # CONFIGURACIÓN OPTIMIZADA basada en DEBUG results
+        # Tu dataset es pequeño (153 imágenes), configuración perfecta:
+        num_workers = 2  # Óptimo para dataset pequeño
+        print(f"🚀 Configuración optimizada para dataset pequeño:")
+        print(f"   - {num_workers} workers")
+        print(f"   - Batch size: {batch_size}")
+        print(f"   - Pin memory habilitado")
+        
+        train_loader = DataLoader(
+            train_dataset, 
+            batch_size=batch_size, 
+            shuffle=True, 
+            num_workers=num_workers,     # ✅ Configuración óptima
+            pin_memory=True,             # ✅ GPU acceleration
+            persistent_workers=True,     # ✅ Mantener workers vivos
+            collate_fn=custom_collate_fn
+        )
+        
+        valid_loader = DataLoader(
+            valid_dataset, 
+            batch_size=batch_size, 
+            shuffle=False, 
+            num_workers=num_workers,     # Mismo para validación
+            pin_memory=True,
+            persistent_workers=True,
+            collate_fn=custom_collate_fn
+        )
+        
+        # Optimizador
+        optimizer = torch.optim.Adam(self.model.parameters(), lr=lr, weight_decay=5e-4)
+        scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(
+            optimizer, T_0=30, T_mult=2, eta_min=lr/100
+        )
+        
+        best_valid_acc = 0.0
+        training_history = []
+        
+        for epoch in range(epochs):
+            # Actualizar el epoch en el dataset para curriculum learning
+            train_dataset.current_epoch = epoch
+            current_n_way = train_dataset.get_current_n_way()
+            
+            print(f"\nÉpoca {epoch+1}/{epochs} - N-way: {current_n_way}")
+            
+            # Entrenamiento
+            train_loss, train_acc = self._train_epoch(train_loader, optimizer)
+            
+            # Validación
+            valid_loss, valid_acc = self._validate_epoch(valid_loader)
+            
+            scheduler.step()
+            
+            print(f"  Train - Loss: {train_loss:.4f}, Acc: {train_acc:.4f}")
+            print(f"  Valid - Loss: {valid_loss:.4f}, Acc: {valid_acc:.4f}")
+            
+            # Guardar mejor modelo
+            if valid_acc > best_valid_acc:
+                best_valid_acc = valid_acc
+                self.save_model(best_model_path, epoch + 1, training_history)
+                print(f"  ✅ Mejor modelo guardado (Acc: {valid_acc:.4f})")
+            
+            # Guardar último modelo
+            self.save_model(last_model_path, epoch + 1, training_history)
+            
+            # Limpiar caché cada 10 épocas
+            if (epoch + 1) % 10 == 0:
+                torch.cuda.empty_cache()
+        
+        return training_history
+    
+    def _train_epoch(self, train_loader, optimizer):
+        """Entrena una época"""
+        self.model.train()
+        epoch_losses = []
+        epoch_accuracies = []
+        
+        for batch_idx, batch in enumerate(train_loader):
+            try:
+                # Procesar batch con múltiples episodios
+                batch_losses = []
+                batch_accuracies = []
+                
+                # Procesar cada episodio en el batch
+                for episode in batch:  # batch ahora es una lista de episodios
+                    support_images = episode['support_images'].to(self.device, non_blocking=True)
+                    support_labels = episode['support_labels'].to(self.device, non_blocking=True)
+                    query_images = episode['query_images'].to(self.device, non_blocking=True)
+                    query_labels = episode['query_labels'].to(self.device, non_blocking=True)
+                    n_way = episode['n_way']
+                    n_support = episode['n_support']
+                    
+                    if support_images.size(0) == 0 or query_images.size(0) == 0:
+                        continue
+                    
+                    # Forward pass
+                    log_probs, _ = self.model(support_images, query_images, n_way, n_support)
+                    
+                    if log_probs.size(0) == 0:
+                        continue
+                        
+                    loss = F.nll_loss(log_probs, query_labels)
+                    batch_losses.append(loss)
+                    
+                    # Métricas
+                    _, predictions = torch.max(log_probs, 1)
+                    accuracy = (predictions == query_labels).float().mean().item()
+                    batch_accuracies.append(accuracy)
+                
+                if not batch_losses:
+                    continue
+                
+                # Backward pass con acumulación de gradientes para el batch
+                total_loss = torch.stack(batch_losses).mean()
+                
+                optimizer.zero_grad()
+                total_loss.backward()
+                torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0)
+                optimizer.step()
+                
+                epoch_losses.append(total_loss.item())
+                epoch_accuracies.append(np.mean(batch_accuracies))
+                
+                if batch_idx % 50 == 0:
+                    print(f"    Batch {batch_idx}/{len(train_loader)}, "
+                          f"Loss: {total_loss.item():.4f}, Acc: {np.mean(batch_accuracies):.4f}")
+                    
+            except Exception as e:
+                continue
+        
+        return np.mean(epoch_losses) if epoch_losses else 0.0, \
+               np.mean(epoch_accuracies) if epoch_accuracies else 0.0
+    
+    def _validate_epoch(self, valid_loader):
+        """Valida una época"""
+        self.model.eval()
+        epoch_losses = []
+        epoch_accuracies = []
+        
+        with torch.no_grad():
+            for batch in valid_loader:
+                try:
+                    batch_losses = []
+                    batch_accuracies = []
+                    
+                    # Procesar cada episodio en el batch
+                    for episode in batch:  # batch ahora es una lista de episodios
+                        support_images = episode['support_images'].to(self.device, non_blocking=True)
+                        query_images = episode['query_images'].to(self.device, non_blocking=True)
+                        query_labels = episode['query_labels'].to(self.device, non_blocking=True)
+                        n_way = episode['n_way']
+                        n_support = episode['n_support']
+                        
+                        if support_images.size(0) == 0 or query_images.size(0) == 0:
+                            continue
+                        
+                        log_probs, _ = self.model(support_images, query_images, n_way, n_support)
+                        
+                        if log_probs.size(0) == 0:
+                            continue
+                            
+                        loss = F.nll_loss(log_probs, query_labels)
+                        
+                        _, predictions = torch.max(log_probs, 1)
+                        accuracy = (predictions == query_labels).float().mean().item()
+                        
+                        batch_losses.append(loss.item())
+                        batch_accuracies.append(accuracy)
+                    
+                    if batch_losses:
+                        epoch_losses.extend(batch_losses)
+                        epoch_accuracies.extend(batch_accuracies)
+                        
+                except Exception as e:
+                    continue
+        
+        return np.mean(epoch_losses) if epoch_losses else 0.0, \
+               np.mean(epoch_accuracies) if epoch_accuracies else 0.0
+    
+    def save_model(self, path, epoch=None, training_history=None):
+        """Guarda el modelo con información adicional"""
+        checkpoint = {
+            'encoder_state_dict': self.encoder.state_dict(),
+            'model_state_dict': self.model.state_dict(),
+            'device': str(self.device),
+            'output_dim': self.encoder.output_dim,
+            'epoch': epoch,
+            'training_history': training_history
+        }
+        torch.save(checkpoint, path)
+    
+    def load_model(self, path):
+        """Carga el modelo"""
+        checkpoint = torch.load(path, map_location=self.device)
+        self.encoder.load_state_dict(checkpoint['encoder_state_dict'])
+        self.model.load_state_dict(checkpoint['model_state_dict'])
+        self.encoder.to(self.device)
+        self.model.to(self.device)
+        return checkpoint
+
+def main():
+    """Función principal optimizada"""
+    # Configurar rutas
+    train_path = "datasets/detergentes_v2/train"
+    valid_path = "datasets/detergentes_v2/valid"
+    test_path = "datasets/detergentes_v2/test"
+    
+    # Verificar que las carpetas existen
+    for path in [train_path, valid_path, test_path]:
+        if not os.path.exists(path):
+            print(f"Error: La carpeta {path} no existe")
+            return
+    
+    # Inicializar entrenador
+    trainer = PrototypicalTrainerWithCurriculum(
+        image_size=224, 
+        hidden_dim=64,
+        output_dim=256
+    )
+    
+    print("\n🚀 OPTIMIZACIONES APLICADAS:")
+    print("✅ DataLoader con workers habilitados")
+    print("✅ Pin memory para transferencia rápida GPU")
+    print("✅ Batch size aumentado a 8")
+    print("✅ Sin pre-computación problemática")
+    
+    # Entrenar modelo
+    history = trainer.train(
+        train_path=train_path,
+        valid_path=valid_path,
+        test_path=test_path,
+        epochs=150,
+        lr=0.001,
+        save_path='model_optimized/prototypical_model_optimized.pth',
+        batch_size=8,
+        min_n_way=6,
+        max_n_way=None
+    )
+    
+    print("\n✅ Entrenamiento completado!")
+
+if __name__ == "__main__":
+    main()
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