diff --git a/src/experiments/knn/knn.py b/src/experiments/knn/knn.py
index d20a4e7..01f012f 100644
--- a/src/experiments/knn/knn.py
+++ b/src/experiments/knn/knn.py
@@ -3,9 +3,9 @@ import numpy as np
 import matplotlib.pyplot as plt
 import seaborn as sns
 import csv
-from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler, MaxAbsScaler
 from enum import Enum
 import random
+from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler, MaxAbsScaler
 from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, matthews_corrcoef
 
 class Tree(Enum):
@@ -19,7 +19,8 @@ class Tree(Enum):
     PLATAAN = 7
 
 # Open file
-file = open('dataset\\csv\\result-2023-10-14T16.13.30.csv', "r")
+# file = open('dataset\\csv\\result-2023-10-14T16.13.30.csv', "r")
+file = open('./out/result-2023-10-10T15.08.36.csv', "r")
 data = list(csv.reader(file, delimiter=","))
 file.close()
 
@@ -32,7 +33,7 @@ tags_int = []
 
 for row in data:
     tree = row.pop(0)
-    row.pop(1) # TODO: Doe dit niet
+    # photoId = row.pop(1)
     id = Tree[tree.upper()]
     
     # print("Tree name =", tree, " id =", id.value)
@@ -62,26 +63,26 @@ for idx, col in enumerate(data[0]):
     column = np.array(column).reshape(len(column))
     
     # DEBUG Print resulting column
-    print("NORM", header[idx + 1], "\n", column)
+    # print("NORM", header[idx + 1], "\n", column)
     
     # Replace original data array
     data[:, idx] = column
 
 # # Get a random number for testing
 # validateId = random.randint(0, tags_len - 1)
-tag_true = []
-tag_predict = []
+# tag_true = []
+# tag_predict = []
 
-print(tags_len)
+# print(tags_len)
 
-for validateId in range(0, tags_len - 1):
-    # Remove object from train set
-    validateTag = tags_int[validateId]
-    validateObj =np.array([data[validateId]])
-    np.delete(tags_int, validateId)
-    np.delete(data, validateTag)
+# for validateId in range(0, tags_len - 1):
+#     # Remove object from train set
+#     validateTag = tags_int[validateId]
+#     validateObj =np.array([data[validateId]])
+#     np.delete(tags_int, validateId)
+#     np.delete(data, validateTag)
 
-    tag_true.append(validateTag)
+#     tag_true.append(validateTag)
 
     # print(validateTag, validateObj)
 
@@ -89,28 +90,30 @@ for validateId in range(0, tags_len - 1):
     print(tags_int)
     print(data.dtype, type(data), tags_int.dtype, type(tags_int))
     knn.train(data, cv.ml.ROW_SAMPLE, tags_int)
+    
+    knn.save('./out/models/knn_nosift.pkl')
 
     # print (data)
     # print('--------------------')
     # print (validateObj)
 
-    ret, results, neighbours ,dist = knn.findNearest(validateObj, 3)
-    tag_predict.append(results[0][0])
+    # ret, results, neighbours ,dist = knn.findNearest(validateObj, 3)
+    # tag_predict.append(results[0][0])
     
     # print( "result: {}\n".format(results) )
     # print( "neighbours: {}\n".format(neighbours) )
     # print( "distance: {}\n".format(dist) )
     
 
-# Create a heatmap
-sns.heatmap(confusion_matrix(tag_true, tag_predict), annot=True)
-plt.title( "Confusion Matrix KNN" )
-plt.show()
+# # Create a heatmap
+# sns.heatmap(confusion_matrix(tag_true, tag_predict), annot=True)
+# plt.title( "Confusion Matrix KNN" )
+# plt.show()
 
 # Score
-print("Accuracy score", accuracy_score(tag_true, tag_predict))
-print("Precision score (macro)", precision_score(tag_true, tag_predict, average='macro'))
-print("Precision score (micro)", precision_score(tag_true, tag_predict, average='micro'))
-print("Recall score (macro)", recall_score(tag_true, tag_predict, average='macro'))
-print("Recall score (micro)", recall_score(tag_true, tag_predict, average='micro'))
-print("MCC", matthews_corrcoef(tag_true, tag_predict))
\ No newline at end of file
+# print("Accuracy score", accuracy_score(tag_true, tag_predict))
+# print("Precision score (macro)", precision_score(tag_true, tag_predict, average='macro'))
+# print("Precision score (micro)", precision_score(tag_true, tag_predict, average='micro'))
+# print("Recall score (macro)", recall_score(tag_true, tag_predict, average='macro'))
+# print("Recall score (micro)", recall_score(tag_true, tag_predict, average='micro'))
+# print("MCC", matthews_corrcoef(tag_true, tag_predict))
\ No newline at end of file
diff --git a/src/helpers/gui/main.ui b/src/helpers/gui/main.ui
index 0f899d7..eee225c 100644
--- a/src/helpers/gui/main.ui
+++ b/src/helpers/gui/main.ui
@@ -323,6 +323,7 @@
         <child>
           <object class="tk.Text" id="testdata" named="True">
             <property name="height">15</property>
+            <property name="state">disabled</property>
             <property name="text" translatable="yes">No tests have been run yet</property>
             <property name="undo">false</property>
             <property name="width">25</property>
diff --git a/src/helpers/test/knn.py b/src/helpers/test/knn.py
new file mode 100644
index 0000000..35f9204
--- /dev/null
+++ b/src/helpers/test/knn.py
@@ -0,0 +1,103 @@
+import cv2 as cv
+import numpy as np
+import csv
+from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler, MaxAbsScaler
+import argparse
+from enum import Enum
+
+parser = argparse.ArgumentParser(prog='KNN Train CLI')
+parser.add_argument('-i', '--input', help='Input CSV file', required=True)
+parser.add_argument('-o', '--output', help='Output model file', required=True)
+
+class Tree(Enum):
+    ACCASIA = 0
+    BERK = 1
+    EIK = 2
+    ELS = 3
+    ESDOORN = 4
+    ES = 5
+    LINDE = 6
+    PLATAAN = 7
+
+class CVSuiteTestKNN: 
+    def __init__(self, model = None):
+        if model is None:
+            self.knn = cv.ml.KNearest_create()
+            self.trained = False
+        else:
+            self.knn = cv.ml.KNearest_load(model)
+            self.trained = True
+            
+    def trainCSV(self, path, output):
+        '''
+        Takes preprocessed data from CVSuite, normalises it and trains the model
+        Function expects first two columns of the dataset to be tag and photoId, the first row should be the CSV header
+        '''
+        file = open(path, mode='r')
+        data = list(csv.reader(file, delimiter=","))
+        file.close()        
+        
+        header = data.pop(0)
+        print("CSV tags: ", header)
+        
+        # Get classifier tags
+        tags_int = []
+
+        for row in data:
+            tree = row.pop(0)
+            # photoId = row.pop(1)
+            id = Tree[tree.upper()]
+            
+            # print("Tree name =", tree, " id =", id.value)
+            tags_int.append(id.value)
+
+        # Make into numpy array cus OpenCV is dumb af
+        tags_len = len(tags_int)
+        tags_int = np.array(tags_int, dtype=np.int32)
+
+        # Transform array for normalisation
+        data = np.array(data, dtype=np.float32)
+
+        for idx, col in enumerate(data[0]):
+            # Get column from data
+            column = data[:, idx]
+            
+            # Shape it to 2 dimentional
+            column = np.array(column).reshape(-1, 1)
+            
+            # Perform Min - Max scaling
+            # scaler = MinMaxScaler()
+            scaler = MaxAbsScaler()
+
+            column = scaler.fit_transform(column)
+            
+            # Reshape it back cus scaler is dumb af
+            column = np.array(column).reshape(len(column))
+            
+            # DEBUG Print resulting column
+            # print("NORM", header[idx + 1], "\n", column)
+            
+            # Replace original data array
+            data[:, idx] = column
+        
+        # Pass data to train function
+        self.train(data, tags_int, output)
+        
+    def train(self, data, tags, output):
+        '''
+        Data should be normalised before being passed to this function
+        This function should not be run from within the suite
+        '''
+        if self.trained:
+            throw("Model already trained!")
+        else:
+            self.knn.train(data, cv.ml.ROW_SAMPLE, tags)
+            self.knn.save(output)
+    
+    def predict(self, data):
+        return self.knn.predict(data)
+    
+if __name__ == "__main__":
+    args = parser.parse_args()
+    test = CVSuiteTestKNN()
+    test.trainCSV(args.input, args.output)
\ No newline at end of file
diff --git a/src/suite.py b/src/suite.py
index 6ae9492..de321bd 100644
--- a/src/suite.py
+++ b/src/suite.py
@@ -19,6 +19,9 @@ from helpers.logger import CVSuiteLogger, C_DBUG
 from helpers.canvas import CVSuiteCanvas
 from helpers.sift import getSiftData
 
+# Tests
+from helpers.test.knn import CVSuiteTestKNN
+
 ## UI config load
 PROJECT_PATH = pathlib.Path(__file__).parent
 PROJECT_UI = "./src/helpers/gui/main.ui"
@@ -81,6 +84,9 @@ class CVSuite:
         )
         builder.connect_callbacks(self)
 
+        # Model tests
+        self.test_knn = CVSuiteTestKNN(config_json["models"]["knn"])
+
         # Load values from config after UI has been initialised
         self.img_path.set(config_json["path"])
         self.img_size.set(config_json["size"])
@@ -267,6 +273,15 @@ class CVSuite:
             self.log.add(f"Mean {label}", mean[idx])
             self.log.add(f"Std {label}", std[idx])
 
+    def runTest(self, event=None):
+        output = self.builder.get_object("testdata")
+        output.configure(state="normal")
+        output.delete(1.0, "end")
+        
+        output.insert("end", "test\n")
+        
+        output.configure(state="disabled")
+
     def updatePath(self):
         """
         Only update image name and path
@@ -406,6 +421,9 @@ class CVSuite:
             self.log.add("SIFT total response", siftData[5])
             self.log.add("SIFT average response", siftData[6])
 
+            # Run tests
+            self.runTest()
+
             # Write results to CSV file
             if not part_update:
                 self.log.update()
@@ -416,7 +434,6 @@ class CVSuite:
             plt.show(block=False)  ## Graphs
             self.canvas.draw(size)  ## Images
 
-
 if __name__ == "__main__":
     app = CVSuite()
     app.run()