diff --git a/.gitignore b/.gitignore
index 21d0b898f..a38501f98 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,3 @@
 .venv/
+CIFAKE/
+CIEDGE/
diff --git a/eval_model_ciedge.py b/eval_model_ciedge.py
new file mode 100644
index 000000000..25db04427
--- /dev/null
+++ b/eval_model_ciedge.py
@@ -0,0 +1,121 @@
+import os
+import torch
+import torchvision
+import torchvision.transforms as transforms
+from torch.utils.data import DataLoader
+from torchvision import datasets, models
+import torch.nn as nn
+import torch.optim as optim
+from tqdm import tqdm # https://tqdm.github.io/
+
+# https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
+
+# ======= Settings =======
+CIEDGE_DIR = "./CIEDGE"
+CIFAKE_DIR = "./CIFAKE"
+BATCH_SIZE = 16
+NUM_CLASSES = 2
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# ======= Transforms =======
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),  # Ensure fixed input size
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406],  # ImageNet mean
+                         [0.229, 0.224, 0.225])  # ImageNet std
+])
+
+cifake_dataset = datasets.ImageFolder(os.path.join(CIFAKE_DIR, "test"), transform=transform)
+ciedge_dataset = datasets.ImageFolder(os.path.join(CIEDGE_DIR, "test"), transform=transform)
+cifake_loader = DataLoader(cifake_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2, pin_memory=True)
+ciedge_loader = DataLoader(ciedge_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2, pin_memory=True)
+
+# ======= Load Model =======
+cifake_model = models.resnet18(weights=models.ResNet18_Weights.IMAGENET1K_V1)
+ciedge_model = models.resnet18(weights=models.ResNet18_Weights.IMAGENET1K_V1)
+
+cifake_model.fc = nn.Linear(cifake_model.fc.in_features, NUM_CLASSES)
+ciedge_model.fc = nn.Linear(ciedge_model.fc.in_features, NUM_CLASSES)
+
+ciedge_state = torch.load("resnet18_ciedge.pth", map_location=torch.device('cpu'))
+cifake_state = torch.load("resnet18_cifake.pth", map_location=torch.device('cpu'))
+
+cifake_model.load_state_dict(cifake_state)
+ciedge_model.load_state_dict(ciedge_state)
+
+
+cifake_model = cifake_model.to(DEVICE)
+ciedge_model = ciedge_model.to(DEVICE)
+
+
+
+cifake_model.eval()
+correct, total = 0, 0
+with torch.no_grad():
+    for inputs, labels in cifake_loader:
+        inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
+        inputs = inputs.contiguous()
+        outputs = cifake_model(inputs)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+        if total > 400:
+            print(f"CIFAKE model evaluating CIFAKE data: {100 * correct / total:.2f}%")
+            break
+
+
+
+cifake_model.eval()
+correct, total = 0, 0
+with torch.no_grad():
+    for inputs, labels in ciedge_loader:
+        inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
+        inputs = inputs.contiguous()
+        outputs = cifake_model(inputs)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+        if total > 400:
+            print(f"CIFAKE model evaluating CIEDGE data: {100 * correct / total:.2f}%")
+            break
+
+
+
+ciedge_model.eval()
+correct, total = 0, 0
+with torch.no_grad():
+    for inputs, labels in cifake_loader:
+        inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
+        inputs = inputs.contiguous()
+        outputs = ciedge_model(inputs)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+        if total > 400:
+            print(f"CIEDGE model evaluating CIFAKE data: {100 * correct / total:.2f}%")
+            break
+
+
+
+ciedge_model.eval()
+correct, total = 0, 0
+with torch.no_grad():
+    for inputs, labels in ciedge_loader:
+        inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
+        inputs = inputs.contiguous()
+        outputs = ciedge_model(inputs)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+        if total > 400:
+            print(f"CIEDGE model evaluating CIEDGE data: {100 * correct / total:.2f}%")
+            break
+
+
+
+
+
+
+
+
+
diff --git a/eval_model_cifake.py b/eval_model_cifake.py
new file mode 100644
index 000000000..5ef0e8629
--- /dev/null
+++ b/eval_model_cifake.py
@@ -0,0 +1,57 @@
+import os
+import torch
+import torchvision
+import torchvision.transforms as transforms
+from torch.utils.data import DataLoader
+from torchvision import datasets, models
+import torch.nn as nn
+import torch.optim as optim
+from tqdm import tqdm # https://tqdm.github.io/
+
+# https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
+
+# ======= Settings =======
+DATA_DIR = "./CIFAKE"
+BATCH_SIZE = 16
+NUM_CLASSES = 2
+NUM_EPOCHS = 10
+LEARNING_RATE = 1e-4
+DEVICE = "cpu"
+
+# ======= Transforms =======
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),  # Ensure fixed input size
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406],  # ImageNet mean
+                         [0.229, 0.224, 0.225])  # ImageNet std
+])
+
+# ======= Load Data =======
+test_dataset = datasets.ImageFolder(os.path.join(DATA_DIR, "test"), transform=transform)
+test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2, pin_memory=True)
+
+# ======= Load Model =======
+model = models.resnet18(pretrained=True)
+model.fc = nn.Linear(model.fc.in_features, NUM_CLASSES)
+state = torch.load("resnet18_cifake.pth", map_location=torch.device('cpu'))
+model.load_state_dict(state)
+model = model.to(DEVICE)
+
+
+# ======= Evaluation =======
+model.eval()
+correct, total = 0, 0
+with torch.no_grad():
+    for inputs, labels in tqdm(test_loader):
+        inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
+        inputs = inputs.contiguous()
+        outputs = model(inputs)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+        #print(f"total={total}, correct={correct}")
+        if total >= 400:
+            break
+
+print(f"Test Accuracy: {100 * correct / total:.2f}%")
+
diff --git a/genpath.awk b/genpath.awk
new file mode 100644
index 000000000..32abe00dc
--- /dev/null
+++ b/genpath.awk
@@ -0,0 +1 @@
+{a = $0; sub("CIFAKE", "CIEDGE", a); beton = "python main.py \'"$0"\' \'"a"\' &"; system(beton)}
diff --git a/main.py b/main.py
new file mode 100644
index 000000000..724d57a0f
--- /dev/null
+++ b/main.py
@@ -0,0 +1,45 @@
+import sys
+import cv2
+from pathlib import Path
+
+def laplace_edge_detection(image_path, output_path):
+    """
+    Opens an image, applies Laplace filtering for edge detection,
+    and saves the resulting grayscale image.
+
+    Args:
+        image_path (str): Path to the input image file.
+        output_path (str): Path to save the processed image.
+    """
+
+    try:
+        # Open the image in grayscale mode
+        img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
+
+        if img is None:
+            print(f"Error: Could not open or read image at {image_path}")
+            return
+
+        # Apply Laplace filter for edge detection
+        laplace_kernel = np.array([[0, 1, 0],
+                                   [1, -4, 1],
+                                   [0, 1, 0]])  # Standard Laplace kernel
+        laplace_filtered = cv2.filter2D(img, -1, laplace_kernel)
+
+        # Convert to uint8 (grayscale 8-bit) and clip values to the valid range [0, 255]
+        laplace_filtered = np.uint8(laplace_filtered)  # Important for saving correctly
+
+        # Save the resulting image
+        cv2.imwrite(output_path, laplace_filtered)
+
+        print(f"Laplace edge detection complete. Image saved to {output_path}")
+
+    except Exception as e:
+        print(f"An error occurred: {e}")
+
+
+if __name__ == "__main__":
+    import numpy as np  # Import here, only if needed for the main execution
+    input_path = sys.argv[1]
+    output_path = sys.argv[2]
+    laplace_edge_detection(input_path, output_path)
diff --git a/resnet18_ciedge.pth b/resnet18_ciedge.pth
new file mode 100644
index 000000000..3400d22b6
Binary files /dev/null and b/resnet18_ciedge.pth differ
diff --git a/resnet18_cifake.pth b/resnet18_cifake.pth
new file mode 100644
index 000000000..48a6398b9
Binary files /dev/null and b/resnet18_cifake.pth differ
diff --git a/train_model_ciedge.py b/train_model_ciedge.py
new file mode 100644
index 000000000..21b744a00
--- /dev/null
+++ b/train_model_ciedge.py
@@ -0,0 +1,85 @@
+import os
+import torch
+import torchvision
+import torchvision.transforms as transforms
+from torch.utils.data import DataLoader
+from torchvision import datasets, models
+import torch.nn as nn
+import torch.optim as optim
+from tqdm import tqdm # https://tqdm.github.io/
+
+# https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
+
+# ======= Settings =======
+DATA_DIR = "./CIEDGE"
+BATCH_SIZE = 16
+NUM_CLASSES = 2
+NUM_EPOCHS = 10
+LEARNING_RATE = 1e-4
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# ======= Transforms =======
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),  # Ensure fixed input size
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406],  # ImageNet mean
+                         [0.229, 0.224, 0.225])  # ImageNet std
+])
+
+# ======= Load Data =======
+train_dataset = datasets.ImageFolder(os.path.join(DATA_DIR, "train"), transform=transform)
+test_dataset = datasets.ImageFolder(os.path.join(DATA_DIR, "test"), transform=transform)
+
+train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2, pin_memory=True)
+test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=2, pin_memory=True)
+
+# ======= Load Model =======
+model = models.resnet18(pretrained=True)
+model.fc = nn.Linear(model.fc.in_features, NUM_CLASSES)
+model = model.to(DEVICE)
+
+# ======= Loss and Optimizer =======
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)
+
+# ======= Training Loop =======
+for epoch in range(NUM_EPOCHS):
+    model.train()
+    running_loss = 0.0
+    correct, total = 0, 0
+
+    loop = tqdm(train_loader, desc=f"Epoch {epoch+1}/{NUM_EPOCHS}")
+    for inputs, labels in loop:
+        inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
+        inputs = inputs.contiguous()  # Ensure tensor layout for MIOpen
+
+        optimizer.zero_grad()
+        outputs = model(inputs)
+        loss = criterion(outputs, labels)
+        loss.backward()
+        optimizer.step()
+
+        running_loss += loss.item()
+        _, predicted = torch.max(outputs, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+
+        loop.set_postfix(loss=loss.item(), acc=100. * correct / total)
+
+# ======= Evaluation =======
+model.eval()
+correct, total = 0, 0
+with torch.no_grad():
+    for inputs, labels in test_loader:
+        inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
+        inputs = inputs.contiguous()
+        outputs = model(inputs)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+
+print(f"Test Accuracy: {100 * correct / total:.2f}%")
+
+# ======= Save Model =======
+torch.save(model.state_dict(), "resnet18_ciedge.pth")
+#
diff --git a/train_model_cifake.py b/train_model_cifake.py
new file mode 100644
index 000000000..c3aab1f4b
--- /dev/null
+++ b/train_model_cifake.py
@@ -0,0 +1,85 @@
+import os
+import torch
+import torchvision
+import torchvision.transforms as transforms
+from torch.utils.data import DataLoader
+from torchvision import datasets, models
+import torch.nn as nn
+import torch.optim as optim
+from tqdm import tqdm # https://tqdm.github.io/
+
+# https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
+
+# ======= Settings =======
+DATA_DIR = "./CIFAKE"
+BATCH_SIZE = 16
+NUM_CLASSES = 2
+NUM_EPOCHS = 10
+LEARNING_RATE = 1e-4
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# ======= Transforms =======
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),  # Ensure fixed input size
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406],  # ImageNet mean
+                         [0.229, 0.224, 0.225])  # ImageNet std
+])
+
+# ======= Load Data =======
+train_dataset = datasets.ImageFolder(os.path.join(DATA_DIR, "train"), transform=transform)
+test_dataset = datasets.ImageFolder(os.path.join(DATA_DIR, "test"), transform=transform)
+
+train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2, pin_memory=True)
+test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=2, pin_memory=True)
+
+# ======= Load Model =======
+model = models.resnet18(pretrained=True)
+model.fc = nn.Linear(model.fc.in_features, NUM_CLASSES)
+model = model.to(DEVICE)
+
+# ======= Loss and Optimizer =======
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)
+
+# ======= Training Loop =======
+for epoch in range(NUM_EPOCHS):
+    model.train()
+    running_loss = 0.0
+    correct, total = 0, 0
+
+    loop = tqdm(train_loader, desc=f"Epoch {epoch+1}/{NUM_EPOCHS}")
+    for inputs, labels in loop:
+        inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
+        inputs = inputs.contiguous()  # Ensure tensor layout for MIOpen
+
+        optimizer.zero_grad()
+        outputs = model(inputs)
+        loss = criterion(outputs, labels)
+        loss.backward()
+        optimizer.step()
+
+        running_loss += loss.item()
+        _, predicted = torch.max(outputs, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+
+        loop.set_postfix(loss=loss.item(), acc=100. * correct / total)
+
+# ======= Evaluation =======
+model.eval()
+correct, total = 0, 0
+with torch.no_grad():
+    for inputs, labels in test_loader:
+        inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
+        inputs = inputs.contiguous()
+        outputs = model(inputs)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+
+print(f"Test Accuracy: {100 * correct / total:.2f}%")
+
+# ======= Save Model =======
+torch.save(model.state_dict(), "resnet18_cifake.pth")
+#