diff --git a/trainers/base_trainer.py b/trainers/base_trainer.py
new file mode 100644
index 0000000..0ad5410
--- /dev/null
+++ b/trainers/base_trainer.py
@@ -0,0 +1,59 @@
+import torch
+from utils.plotting import plot_loss
+from utils.logging import log_to_csv
+
+class BaseTrainer:
+    def __init__(self, model, device):
+        self.model = model
+        self.device = device
+        self.criterion = None
+        self.optimizer = None
+
+    def build(self, criterion, optimizer, scheduler=None):
+        """ Set the loss function, optimizer, and learning rate scheduler. """
+
+        self.criterion = criterion
+        self.optimizer = optimizer
+        self.scheduler = scheduler
+
+    def _train_epoch(self, train_loader, epoch, num_epochs, verbose=True):
+        """ Train the model for one epoch. """
+
+        raise NotImplementedError("The train_epoch method must be implemented by the subclass.")
+
+    def train(self, train_loader, num_epochs, log_path, plot_path, verbose=True):
+        """ Train the model for a given number of epochs. """
+
+        epoch_losses = []
+
+        for epoch in range(num_epochs):
+            epoch_loss = self._train_epoch(train_loader, epoch, num_epochs, verbose)
+            epoch_losses.append(epoch_loss)
+
+            if verbose:
+                print(f"Epoch {epoch+1}/{num_epochs}, Loss: {epoch_loss:.4f}")
+            log_to_csv(epoch_losses, log_path)
+
+        if verbose:
+            plot_loss(epoch_losses, plot_path)
+
+    def evaluate(self, test_loader, verbose=True):
+        """ Evaluate the model on the test set. """
+
+        correct = 0
+        total = 0
+        
+        self.model.eval()
+
+        with torch.no_grad():
+            for images, labels in test_loader:
+                images, labels = images.to(self.device), labels.to(self.device)
+                outputs = self.model(images)
+                _, predicted = torch.max(outputs, 1)
+                total += labels.size(0)
+                correct += (predicted == labels).sum().item()
+
+        accuracy = 100 * correct / total
+        if verbose:
+            print(f"Accuracy: {accuracy}%")
+        return accuracy