diff --git a/README.md b/README.md
index 3bbf58494352de66fa9448355cbd2954475715ce..818a90b4b4f0578dab97fb836d50ab0651cb07d4 100644
--- a/README.md
+++ b/README.md
@@ -81,6 +81,6 @@ Install the packet to be used for getting the data : `!pip install gdown`.
 
 Get the data with the following command: `!gdown https://drive.google.com/uc?id=1D6z3UbCoBOhOs8lhasgm-ap58-uPdDY-`
 
-The basis for the tutorial is the contest of the `run.py` script in the folder of the cloned repository. You can gradulaly copy the commands from there and modify them.
+The basis for the tutorial is the conrent of the `run.py` script in the folder of the cloned repository. You can gradulaly copy the commands from there, modify and run them.
 
 
diff --git a/models/simple_cnn.py b/models/simple_cnn.py
index 14ac342d57e9996045965feb2a6116a94c289b2e..0f332bf5fce5435fa35382f9f784bbb701fbb1aa 100644
--- a/models/simple_cnn.py
+++ b/models/simple_cnn.py
@@ -2,10 +2,8 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
-#TODO: activation masks after each conv layer
-
 class SimpleCNN(nn.Module):
-    def __init__(self):
+    def __init__(self, num_classes=3):
         super(SimpleCNN, self).__init__()
         self.conv1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, padding=1)
         self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
@@ -14,7 +12,7 @@ class SimpleCNN(nn.Module):
         self.conv3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1)
         self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
         self.fc1 = nn.Linear(in_features=64 * 32 * 32, out_features=500)
-        self.fc2 = nn.Linear(in_features=500, out_features=3)
+        self.fc2 = nn.Linear(in_features=500, out_features=num_classes)
 
     def forward(self, x, return_activations=False):
         activations = {}
diff --git a/notebooks/train_model.ipynb b/notebooks/train_model.ipynb
index a8ec2e128751917cfdf2d87bb73575a1973dbb8a..faad226a92ba0b6fec3cfdffd9438bfb6b6c33f4 100644
--- a/notebooks/train_model.ipynb
+++ b/notebooks/train_model.ipynb
@@ -2,9 +2,18 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.8/dist-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
    "source": [
     "from torchvision import transforms, datasets\n",
     "from torch.utils.data import DataLoader\n",
@@ -36,27 +45,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.8/dist-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    },
-    {
-     "ename": "NameError",
-     "evalue": "name 'data_path' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[1], line 30\u001b[0m\n\u001b[1;32m     23\u001b[0m transform \u001b[38;5;241m=\u001b[39m transforms\u001b[38;5;241m.\u001b[39mCompose([\n\u001b[1;32m     24\u001b[0m     transforms\u001b[38;5;241m.\u001b[39mResize((\u001b[38;5;241m256\u001b[39m, \u001b[38;5;241m256\u001b[39m)),\n\u001b[1;32m     25\u001b[0m     transforms\u001b[38;5;241m.\u001b[39mToTensor(),\n\u001b[1;32m     26\u001b[0m     transforms\u001b[38;5;241m.\u001b[39mNormalize((\u001b[38;5;241m0.5\u001b[39m,), (\u001b[38;5;241m0.5\u001b[39m,))\n\u001b[1;32m     27\u001b[0m ])\n\u001b[1;32m     29\u001b[0m \u001b[38;5;66;03m# Create the train_dataset and train_loader as before\u001b[39;00m\n\u001b[0;32m---> 30\u001b[0m train_dataset \u001b[38;5;241m=\u001b[39m datasets\u001b[38;5;241m.\u001b[39mImageFolder(root\u001b[38;5;241m=\u001b[39m\u001b[43mdata_path\u001b[49m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/train\u001b[39m\u001b[38;5;124m'\u001b[39m, transform\u001b[38;5;241m=\u001b[39mtransform)\n\u001b[1;32m     31\u001b[0m train_loader \u001b[38;5;241m=\u001b[39m DataLoader(train_dataset, batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m, shuffle\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m     33\u001b[0m \u001b[38;5;66;03m# Get some random training images\u001b[39;00m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'data_path' is not defined"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
diff --git a/pipeline/data_loader.py b/pipeline/data_loader.py
index 1d7061d5b98eeb188f7666ea5dd2fa0d8e73efe4..60a3c369ac9b0513d7a1eab23769e8a7b025320f 100644
--- a/pipeline/data_loader.py
+++ b/pipeline/data_loader.py
@@ -16,7 +16,14 @@ def create_data_loaders(data_path, batch_size=8, num_workers=4):
     test_dataset = datasets.ImageFolder(root=data_path + '/test', transform=transform)
 
     # Create a DataLoader for each set
-    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
+    train_loader = DataLoader(
+        train_dataset, 
+        batch_size=batch_size, 
+        shuffle=True, 
+        num_workers=num_workers, 
+        pin_memory=True,
+        persistent_workers=True
+        )
     val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
     test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
 
diff --git a/pipeline/run.py b/pipeline/run.py
index 08c9cc82c744f1e920ca794c73fa791b2f159ade..3ff0854ca31a2307e9868575e5e75799abeb9a40 100644
--- a/pipeline/run.py
+++ b/pipeline/run.py
@@ -1,9 +1,12 @@
-############################## this section prepares the data for training ##############################
-
+############################## this section prepares the data for training ###############################
+import shutil
+import os
 from prepare_data.clean_file_names import clean_file_names
 
 # RAW_DATA_PATH = "/home/nibio/mutable-outside-world/code/ml-department-workshop/ml-department-workshop-dataset/simple-needles-2-class"
-RAW_DATA_PATH = "/home/nibio/mutable-outside-world/code/ml-department-workshop/ml-department-workshop-dataset/simple-needles-3-class"
+# RAW_DATA_PATH = "/home/nibio/mutable-outside-world/code/ml-department-workshop/ml-department-workshop-dataset/simple-needles-3-class"
+RAW_DATA_PATH = "/home/nibio/mutable-outside-world/code/ml-department-workshop/ml-department-workshop-dataset/simple-needles-4-class"
+
 
 
 # Clean file and directory names
@@ -12,8 +15,13 @@ clean_file_names(RAW_DATA_PATH)
 
 from prepare_data.prepare_train_val_test import PrepareTrainValTest
 DATA_IN_PATH = RAW_DATA_PATH
+
 DATA_OUT_PATH = "/home/nibio/mutable-outside-world/code/ml-department-workshop/datasets/data_splited"
 
+# check if DATA_OUT_PATH exists and delete it if it does
+if os.path.exists(DATA_OUT_PATH):
+    shutil.rmtree(DATA_OUT_PATH)
+
 # Create train, validation, and test data sets
 prepare_data = PrepareTrainValTest(DATA_IN_PATH, DATA_OUT_PATH)
 
@@ -23,7 +31,7 @@ prepare_data.prepare_train_val_test()
 from pipeline.data_loader import create_data_loaders
 DATA_PATH = DATA_OUT_PATH
 BATCH_SIZE = 8
-NUM_WORKERS = 4
+NUM_WORKERS = 8
 
 # Create data loaders
 train_loader, val_loader, test_loader = create_data_loaders(DATA_PATH, BATCH_SIZE, NUM_WORKERS)
@@ -40,6 +48,8 @@ TRAIN = True
 
 if TRAIN:
     # Import necessary packages for training
+    import matplotlib.pyplot as plt
+    import numpy as np
     import torch
     import torch.nn as nn
     import torch.nn.functional as F
@@ -49,16 +59,24 @@ if TRAIN:
     from models.simple_cnn import SimpleCNN 
 
     # Create an instance of the model
-    model = SimpleCNN()
+    model = SimpleCNN(num_classes=len(train_loader.dataset.classes))
 
     # Define the loss function and optimizer
     criterion = nn.CrossEntropyLoss()
 
+    # # use focal loss
+    # from focal_loss import FocalLoss
+    # criterion = FocalLoss(alpha=0.25, gamma=2.0)
+
     # Use Adam optimizer
     optimizer = optim.Adam(model.parameters(), lr=0.001)
 
+
+    train_acc = []
+    val_acc = []
+
     # Train the model
-    num_epochs = 5
+    num_epochs = 15
     for epoch in range(num_epochs):
         running_loss = 0.0
         for i, data in enumerate(train_loader):
@@ -68,19 +86,69 @@ if TRAIN:
             # Zero the parameter gradients
             optimizer.zero_grad()
 
-            # Forward + backward + optimize
-            outputs = model(inputs)
-            loss = criterion(outputs, labels)
-            loss.backward()
-            optimizer.step()
+            # Forward + loss + backward + optimize
+            outputs = model(inputs)  # forward
+            loss = criterion(outputs, labels)  # loss
+            loss.backward()  # backward
+            optimizer.step()  # optimize
 
             # Print statistics
             print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, loss))
+            
+        print('Train accuracy: %.2f%%' % (100 * (labels == outputs.argmax(dim=1)).sum().item() / len(labels)))
+        train_acc.append(100 * (labels == outputs.argmax(dim=1)).sum().item() / len(labels))
+        # Validation step
+        model.eval()  # Set the model to evaluation mode
+        val_loss = 0.0
+        val_correct = 0
+        val_total = 0
+
+        with torch.no_grad():
+            for val_data in val_loader:
+                val_inputs, val_labels = val_data
+                val_outputs = model(val_inputs)
+                val_loss += criterion(val_outputs, val_labels).item()
+                _, val_predicted = torch.max(val_outputs.data, dim=1)
+                val_total += val_labels.size(0)
+                val_correct += (val_predicted == val_labels).sum().item()
+
+        val_accuracy = 100 * val_correct / val_total
+        val_acc.append(val_accuracy)
+        val_loss /= len(val_loader)
+
+        print('Validation: loss = %.3f, Validation accuracy = %.2f%%' % (val_loss, val_accuracy))
+
+        model.train()  # Set the model back to training mode
 
 
     # save the model
     torch.save(model.state_dict(), 'simple_cnn.pth')
 
+    # print the train and validation accuracy
+    # reset the figure
+    plt.clf()
+
+    plt.plot(train_acc, label='Train accuracy')
+    plt.plot(val_acc, label='Validation accuracy')
+    # plot also smooth curves
+    x = np.arange(len(train_acc))
+    y = np.array(train_acc)
+    z = np.polyfit(x, y, 3)
+    p_train = np.poly1d(z)
+
+    x = np.arange(len(val_acc))
+    y = np.array(val_acc)
+    z = np.polyfit(x, y, 3)
+    p_val = np.poly1d(z)
+
+    plt.plot(x, p_train(x), "r--", label='Train accuracy smooth')
+    plt.plot(x, p_val(x), "g--", label='Validation accuracy smooth')
+
+    plt.xlabel('Epoch')
+    plt.xticks(np.arange(0, num_epochs, step=1))
+    plt.ylabel('Accuracy')
+    plt.legend()
+    plt.savefig('train_val_accuracy.png')
 
 ############################## this section evaluates the model ##############################
 # load the model
@@ -88,7 +156,7 @@ import torch
 
 from models.simple_cnn import SimpleCNN
 
-model = SimpleCNN()
+model = SimpleCNN(num_classes=len(train_loader.dataset.classes))
 model.load_state_dict(torch.load('simple_cnn.pth'))
 
 # run the model on the test set and print the accuracy
@@ -129,7 +197,7 @@ cm = confusion_matrix(y_true, y_pred)
 
 # Plot the confusion matrix
 plt.figure(figsize=(10, 10))
-sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')
+sns.heatmap(cm, annot=True, fmt='d', cmap='Blues') # 'Blues', 'Greens', 'Greys', 'Purples', 'Reds', etc.
 plt.xlabel('Predicted label')
 plt.ylabel('True label')
 # save the confusion matrix
@@ -180,7 +248,7 @@ def save_activations(activations, save_dir):
         num_features = act.size(1)
         for i in range(num_features):
             plt.figure()
-            plt.imshow(act[0, i].detach().numpy(), cmap='hot')
+            plt.imshow(act[0, i].detach().numpy(), cmap='gray')
             plt.axis('off')
             
             # Save each channel's activation with a proper file name
diff --git a/prepare_data/prepare_train_val_test.py b/prepare_data/prepare_train_val_test.py
index 19241f3c71dde96e0591dca897fbb05794036404..be6d028508479c5d75b97eea7b59d1068b9b9659 100644
--- a/prepare_data/prepare_train_val_test.py
+++ b/prepare_data/prepare_train_val_test.py
@@ -1,4 +1,5 @@
 import os
+import argparse
 import shutil
 import numpy as np
 
@@ -98,9 +99,7 @@ class PrepareTrainValTest:
 
 
 if __name__ == "__main__":
-    # use argparse to get command line arguments
-    import argparse
-
+    # parse command-line arguments
     parser = argparse.ArgumentParser(
         description="Prepare train, validation, and test data sets from raw data."
     )
diff --git a/visualization/show_sample_images.py b/visualization/show_sample_images.py
index 7975ef1d329762ebd215838b7add21d63a38bd4f..d3025489d25a5f745195f3eabcd1402a8da83671 100644
--- a/visualization/show_sample_images.py
+++ b/visualization/show_sample_images.py
@@ -8,9 +8,14 @@ import torch
 def show_sample_images(data_path, save_path=None):
     # Function to show an image with labels
     def imshow(img, labels, classes, save_path=None):
-        img = img / 2 + 0.5  # unnormalize
-        npimg = img.numpy()
-        plt.imshow(np.transpose(npimg, (1, 2, 0)))
+        # Denormalize image
+        mean = np.array([0.485, 0.456, 0.406])
+        std = np.array([0.229, 0.224, 0.225])
+        img = img.numpy().transpose((1, 2, 0)) # Convert from tensor image
+        img = std * img + mean
+        img = np.clip(img, 0, 1) # Clip values to be in the range [0, 1]
+
+        plt.imshow(img)
         # Display labels below the image
         plt.xticks([])  # Remove x-axis ticks
         plt.yticks([])  # Remove y-axis ticks
@@ -20,6 +25,7 @@ def show_sample_images(data_path, save_path=None):
         else:
             plt.show()
 
+
     # Define transformations
     transform = transforms.Compose([
         transforms.Resize((256, 256)),