diff --git a/main.py b/main.py
index 6a16edd..2c5e4bc 100644
--- a/main.py
+++ b/main.py
@@ -44,11 +44,6 @@ def __getitem__(self, idx):
         img_name = os.path.join(self.root_dir, self.data_frame.iloc[idx, 1]) # grabs the filepath column from the csv
         image = Image.open(img_name).convert('RGB') # Load image and converts into RGB values from grabbed filepath
         label = torch.tensor(self.data_frame.iloc[idx]['numeric_label'], dtype=torch.long) # gets the numeric version of the label from the label column of the csv
-
-        #For debugging and measuring progress in an epoch in a human readable way
-        length = self.__len__()
-        if (idx+1) % 16 == 0:
-            print(f"Batch: {int((idx+1)/16)} / {length // 16}") #Displays progress in number of batches per epoch completed
         
         #If the image is not the correct size and format, reshape it so it is as such
         if self.transform: 
diff --git a/tester.py b/tester.py
index 35a207e..c760dea 100644
--- a/tester.py
+++ b/tester.py
@@ -8,6 +8,7 @@
 import time
 import matplotlib.pyplot as plt
 import matplotlib.image as mpimg
+from datetime import datetime
 
 lognum = 0
 filename = rf"testlog\log{lognum}.txt"
@@ -121,10 +122,11 @@ def __getitem__(self, idx):
         img_name = os.path.join(self.root_dir, self.data_frame.iloc[idx, 1]) # grabs the filepath column from the csv
         image = Image.open(img_name).convert('RGB') # Load image and converts into RGB values from grabbed filepath
         label = torch.tensor(self.data_frame.iloc[idx]['numeric_label'], dtype=torch.long) # gets the numeric version of the label from the label column of the csv
-        
+
         print("")
         print("")
         print(f"Animal: {self.data_frame.iloc[idx]['labels']}")
+
         img = mpimg.imread(img_name)
         plt.imshow(img)
         plt.show(block = False)
@@ -135,11 +137,6 @@ def __getitem__(self, idx):
             image = self.transform(image)
 
         return image, label
-    
-# Test with dummy data
-img = mpimg.imread(path)
-plt.imshow(img)
-plt.show()
 
 #Cleans input
 # Define your transformations (e.g., resize, normalization) in case an image or not the correct format
@@ -154,7 +151,7 @@ def __getitem__(self, idx):
 test_loader = DataLoader(test_dataset, batch_size=1, shuffle=True)
 correct = 0
 f = open(filename, "w")
-
+f.write(f"Compiled on: {datetime.now().strftime('%Y-%m-%d')} at {datetime.now().strftime('%H:%M:%S')}")
 for image, labels in test_loader:
 
     #Cleans input
@@ -173,12 +170,7 @@ def __getitem__(self, idx):
         
 print(f"Percentage Correct: {round((correct / test_loader.__len__()) * 100, 1)}%")
 f.write("")
-f.write(f"\nAccuracy: {round((correct / test_loader.__len__()) * 100, 1)}%")
+f.write(f"\nAccuracy: {round((correct / test_loader.__len__()) * 100, 1)}%\n")
 for key, value in BigCatAccuracy.items():
     f.write(f"{BigCatLabel[key]} guessed correctly {(value / 5) * 100}% of the time\n")
-f.close()
-image = Image.open(path).convert('RGB')
-test_input = transform(image).unsqueeze(0)  # Add batch dimension: [1, 3, 224, 224]
-output = model(test_input)
-predicted_class = torch.argmax(output, dim=1).item()
-print(f"Predicted class: {BigCatLabel[predicted_class]}")
\ No newline at end of file
+f.close()
\ No newline at end of file
diff --git a/testlog/log0.txt b/testlog/log0.txt
index 9872168..e3359af 100644
--- a/testlog/log0.txt
+++ b/testlog/log0.txt
@@ -1,22 +1,30 @@
+Compiled on: 2025-01-01 at 22:54:14
 
+Animal: CLOUDED LEOPARD
+Predicted class: Clouded Leopard
 
-Animal: OCELOT
+
+Animal: TIGER
 Predicted class: Tiger
 
 
-Animal: CARACAL
-Predicted class: Caracal
+Animal: JAGUAR
+Predicted class: Ocelot
 
 
-Animal: JAGUAR
-Predicted class: African Leopard
+Animal: AFRICAN LEOPARD
+Predicted class: Jaguar
 
 
-Animal: OCELOT
-Predicted class: Ocelot
+Animal: TIGER
+Predicted class: Tiger
 
 
-Animal: SNOW LEOPARD
+Animal: CARACAL
+Predicted class: Caracal
+
+
+Animal: OCELOT
 Predicted class: Clouded Leopard
 
 
@@ -24,181 +32,175 @@ Animal: JAGUAR
 Predicted class: Ocelot
 
 
-Animal: CHEETA
-Predicted class: Cheeta
-
-
 Animal: SNOW LEOPARD
 Predicted class: Ocelot
 
 
-Animal: JAGUAR
-Predicted class: African Leopard
+Animal: OCELOT
+Predicted class: Tiger
 
 
-Animal: CLOUDED LEOPARD
-Predicted class: Clouded Leopard
+Animal: PUMA
+Predicted class: Puma
 
 
-Animal: CLOUDED LEOPARD
-Predicted class: Tiger
+Animal: LIONS
+Predicted class: Lions
 
 
-Animal: AFRICAN LEOPARD
-Predicted class: African Leopard
+Animal: PUMA
+Predicted class: Caracal
+
+
+Animal: OCELOT
+Predicted class: Clouded Leopard
 
 
 Animal: JAGUAR
 Predicted class: African Leopard
 
 
-Animal: LIONS
+Animal: CARACAL
+Predicted class: Lions
+
+
+Animal: PUMA
 Predicted class: Puma
 
 
-Animal: AFRICAN LEOPARD
-Predicted class: Jaguar
+Animal: LIONS
+Predicted class: Lions
 
 
-Animal: AFRICAN LEOPARD
-Predicted class: Jaguar
+Animal: JAGUAR
+Predicted class: African Leopard
 
 
-Animal: CLOUDED LEOPARD
+Animal: OCELOT
 Predicted class: Ocelot
 
 
-Animal: LIONS
-Predicted class: Lions
+Animal: SNOW LEOPARD
+Predicted class: Snow Leopard
 
 
-Animal: OCELOT
-Predicted class: Clouded Leopard
+Animal: TIGER
+Predicted class: Tiger
 
 
-Animal: PUMA
-Predicted class: Caracal
+Animal: TIGER
+Predicted class: Tiger
 
 
-Animal: PUMA
-Predicted class: Puma
+Animal: SNOW LEOPARD
+Predicted class: Snow Leopard
 
 
-Animal: PUMA
-Predicted class: Caracal
+Animal: SNOW LEOPARD
+Predicted class: Clouded Leopard
 
 
-Animal: AFRICAN LEOPARD
-Predicted class: African Leopard
+Animal: OCELOT
+Predicted class: Ocelot
 
 
-Animal: JAGUAR
-Predicted class: Ocelot
+Animal: AFRICAN LEOPARD
+Predicted class: Cheeta
 
 
-Animal: TIGER
-Predicted class: Tiger
+Animal: CLOUDED LEOPARD
+Predicted class: Ocelot
 
 
-Animal: CARACAL
+Animal: PUMA
 Predicted class: Caracal
 
 
-Animal: LIONS
-Predicted class: Lions
+Animal: CLOUDED LEOPARD
+Predicted class: Clouded Leopard
 
 
-Animal: SNOW LEOPARD
-Predicted class: Snow Leopard
+Animal: CHEETA
+Predicted class: Ocelot
 
 
 Animal: CHEETA
 Predicted class: Cheeta
 
 
-Animal: OCELOT
-Predicted class: Clouded Leopard
-
-
 Animal: CARACAL
-Predicted class: Lions
+Predicted class: Caracal
 
 
 Animal: TIGER
 Predicted class: Tiger
 
 
-Animal: LIONS
-Predicted class: Lions
+Animal: CHEETA
+Predicted class: Tiger
 
 
-Animal: CARACAL
+Animal: PUMA
 Predicted class: Puma
 
 
-Animal: TIGER
-Predicted class: Tiger
-
-
-Animal: CARACAL
-Predicted class: Caracal
+Animal: AFRICAN LEOPARD
+Predicted class: Jaguar
 
 
-Animal: SNOW LEOPARD
-Predicted class: Snow Leopard
+Animal: JAGUAR
+Predicted class: African Leopard
 
 
 Animal: CHEETA
-Predicted class: Tiger
+Predicted class: African Leopard
 
 
-Animal: TIGER
-Predicted class: Tiger
+Animal: AFRICAN LEOPARD
+Predicted class: African Leopard
 
 
 Animal: LIONS
 Predicted class: Lions
 
 
-Animal: CHEETA
-Predicted class: African Leopard
+Animal: CARACAL
+Predicted class: Caracal
 
 
-Animal: TIGER
+Animal: CLOUDED LEOPARD
 Predicted class: Tiger
 
 
-Animal: CHEETA
-Predicted class: Ocelot
+Animal: AFRICAN LEOPARD
+Predicted class: African Leopard
+
+
+Animal: LIONS
+Predicted class: Lions
 
 
 Animal: CLOUDED LEOPARD
 Predicted class: Clouded Leopard
 
 
-Animal: PUMA
+Animal: LIONS
 Predicted class: Puma
 
 
-Animal: AFRICAN LEOPARD
-Predicted class: Cheeta
+Animal: CARACAL
+Predicted class: Puma
 
 
 Animal: SNOW LEOPARD
 Predicted class: Ocelot
 
 
-Animal: OCELOT
-Predicted class: Ocelot
-
-
-Animal: CLOUDED LEOPARD
-Predicted class: Clouded Leopard
-
+Animal: CHEETA
+Predicted class: Cheeta
 
-Animal: PUMA
-Predicted class: Puma
-Accuracy: 52.0%African Leopard guessed correctly 40.0% of the time
+Accuracy: 52.0%
+African Leopard guessed correctly 40.0% of the time
 Caracal guessed correctly 60.0% of the time
 Cheeta guessed correctly 40.0% of the time
 Clouded Leopard guessed correctly 60.0% of the time