diff --git a/odir_image_crop.py b/odir_image_crop.py
index d5e6441..282782e 100644
--- a/odir_image_crop.py
+++ b/odir_image_crop.py
@@ -1,4 +1,4 @@
-# Copyright 2019 Jordi Corbilla. All Rights Reserved.
+# Copyright 2019-2020 Jordi Corbilla. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/odir_image_crop_job.py b/odir_image_crop_job.py
index be97b87..b419f6f 100644
--- a/odir_image_crop_job.py
+++ b/odir_image_crop_job.py
@@ -1,4 +1,4 @@
-# Copyright 2019 Jordi Corbilla. All Rights Reserved.
+# Copyright 2019-2020 Jordi Corbilla. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/odir_image_resizer.py b/odir_image_resizer.py
index b94aebd..c3b2cc7 100644
--- a/odir_image_resizer.py
+++ b/odir_image_resizer.py
@@ -1,4 +1,4 @@
-# Copyright 2019 Jordi Corbilla. All Rights Reserved.
+# Copyright 2019-2020 Jordi Corbilla. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/odir_image_testing_crop_job.py b/odir_image_testing_crop_job.py
index 514f64c..fca1f15 100644
--- a/odir_image_testing_crop_job.py
+++ b/odir_image_testing_crop_job.py
@@ -1,4 +1,4 @@
-# Copyright 2019 Jordi Corbilla. All Rights Reserved.
+# Copyright 2019-2020 Jordi Corbilla. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/odir_image_treatment.py b/odir_image_treatment.py
new file mode 100644
index 0000000..cc82aa3
--- /dev/null
+++ b/odir_image_treatment.py
@@ -0,0 +1,91 @@
+# Copyright 2019-2020 Jordi Corbilla. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+import numpy as np
+import tensorflow as tf
+from skimage import exposure
+
+
+class ImageTreatment:
+    def __init__(self, image_size):
+        self.image_size = image_size
+
+    def scaling(self, image, scale_vector):
+        # Resize to 4-D vector
+        image = np.reshape(image, (1, self.image_size, self.image_size, 3))
+        boxes = np.zeros((len(scale_vector), 4), dtype=np.float32)
+        for index, scale in enumerate(scale_vector):
+            x1 = y1 = 0.5 - 0.5 * scale
+            x2 = y2 = 0.5 + 0.5 * scale
+            boxes[index] = np.array([y1, x1, y2, x2], dtype=np.float32)
+        box_ind = np.zeros((len(scale_vector)), dtype=np.int32)
+        crop_size = np.array([self.image_size, self.image_size], dtype=np.int32)
+
+        output = tf.image.crop_and_resize(image, boxes, box_ind, crop_size)
+        output = np.array(output, dtype=np.uint8)
+        return output
+
+    def brightness(self, image, delta):
+        output = tf.image.adjust_brightness(image, delta)
+        output = np.array(output, dtype=np.uint8)
+        return output
+
+    def contrast(self, image, contrast_factor):
+        output = tf.image.adjust_contrast(image, contrast_factor)
+        output = np.array(output, dtype=np.uint8)
+        return output
+
+    def saturation(self, image, saturation_factor):
+        output = tf.image.adjust_saturation(image, saturation_factor)
+        output = np.array(output, dtype=np.uint8)
+        return output
+
+    def hue(self, image, delta):
+        output = tf.image.adjust_hue(image, delta)
+        output = np.array(output, dtype=np.uint8)
+        return output
+
+    def central_crop(self, image, central_fraction):
+        output = tf.image.central_crop(image, central_fraction)
+        output = np.array(output, dtype=np.uint8)
+        return output
+
+    def crop_to_bounding_box(self, image, offset_height, offset_width, target_height, target_width):
+        output = tf.image.crop_to_bounding_box(image, offset_height, offset_width, target_height, target_width)
+        output = tf.image.resize(output, (self.image_size, self.image_size))
+        output = np.array(output, dtype=np.uint8)
+        return output
+
+    def gamma(self, image, gamma):
+        output = tf.image.adjust_gamma(image, gamma)
+        output = np.array(output, dtype=np.uint8)
+        return output
+
+    def rot90(self, image, k):
+        output = tf.image.rot90(image, k)
+        output = np.array(output, dtype=np.uint8)
+        return output
+
+    def rescale_intensity(self, image):
+        p2, p98 = np.percentile(image, (2, 98))
+        img_rescale = exposure.rescale_intensity(image, in_range=(p2, p98))
+        return img_rescale
+
+    def equalize_histogram(self, image):
+        img_eq = exposure.equalize_hist(image)
+        return img_eq
+
+    def equalize_adapthist(self, image):
+        img_adapted = exposure.equalize_adapthist(image, clip_limit=0.03)
+        return img_adapted
diff --git a/odir_inception_testing_inference.py b/odir_inception_testing_inference.py
new file mode 100644
index 0000000..bd99ed5
--- /dev/null
+++ b/odir_inception_testing_inference.py
@@ -0,0 +1,77 @@
+# Copyright 2019 Jordi Corbilla. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging.config
+import tensorflow as tf
+from absl import app
+from odir_advance_plotting import Plotter
+from odir_kappa_score import FinalScore
+from odir_normalize_input import Normalizer
+from odir_predictions_writer import Prediction
+import odir
+
+def main(argv):
+    print(tf.version.VERSION)
+    image_size = 224
+    test_run = 'zC'
+
+    # load the data
+    (x_train, y_train), (x_test, y_test) = odir.load_data(image_size, 1)
+
+    class_names = ['Normal', 'Diabetes', 'Glaucoma', 'Cataract', 'AMD', 'Hypertension', 'Myopia', 'Others']
+
+    # plot data input
+    plotter = Plotter(class_names)
+    plotter.plot_input_images(x_train, y_train)
+
+    x_test_drawing = x_test
+
+    # normalize input based on model
+    normalizer = Normalizer()
+    x_test = normalizer.normalize_vgg16(x_test)
+
+    # load one of the test runs
+    model = tf.keras.models.load_model(r'C:\Users\thund\Source\Repos\TFM-ODIR\models\image_classification\modelvgg100.h5')
+    model.summary()
+
+    # display the content of the model
+    baseline_results = model.evaluate(x_test, y_test, verbose=2)
+    for name, value in zip(model.metrics_names, baseline_results):
+        print(name, ': ', value)
+    print()
+
+    # test a prediction
+    test_predictions_baseline = model.predict(x_test)
+    plotter.plot_confusion_matrix_generic(y_test, test_predictions_baseline, test_run, 0)
+
+    # save the predictions
+    prediction_writer = Prediction(test_predictions_baseline, 400)
+    prediction_writer.save()
+    prediction_writer.save_all(y_test)
+
+    # show the final score
+    score = FinalScore()
+    score.output()
+
+    # plot output results
+    plotter.plot_output(test_predictions_baseline, y_test, x_test_drawing)
+
+
+if __name__ == '__main__':
+    # create logger
+    logging.config.fileConfig('logging.conf')
+    logger = logging.getLogger('odir')
+    app.run(main)
diff --git a/odir_kappa_score.py b/odir_kappa_score.py
index 7187669..2b9e26e 100644
--- a/odir_kappa_score.py
+++ b/odir_kappa_score.py
@@ -1,26 +1,46 @@
+# Copyright 2019 Jordi Corbilla. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
 import csv
 import numpy as np
 from sklearn import metrics
 
-def import_data(filepath):
-    with open(filepath, 'r') as f:
-        reader = csv.reader(f)
-        header = next(reader)
-        pr_data = [[int(row[0])] + list(map(float, row[1:])) for row in reader]
-    pr_data = np.array(pr_data)
-    return pr_data
 
-def odir_metrics(gt_data, pr_data):
-    th = 0.5
-    gt = gt_data.flatten()
-    pr = pr_data.flatten()
-    kappa = metrics.cohen_kappa_score(gt, pr > th)
-    f1 = metrics.f1_score(gt, pr > th, average='micro')
-    auc = metrics.roc_auc_score(gt, pr)
-    final_score = (kappa + f1 + auc) / 3.0
-    return kappa, f1, auc, final_score
+class FinalScore:
+    def odir_metrics(self, gt_data, pr_data):
+        th = 0.5
+        gt = gt_data.flatten()
+        pr = pr_data.flatten()
+        kappa = metrics.cohen_kappa_score(gt, pr > th)
+        f1 = metrics.f1_score(gt, pr > th, average='micro')
+        auc = metrics.roc_auc_score(gt, pr)
+        final_score = (kappa + f1 + auc) / 3.0
+        return kappa, f1, auc, final_score
 
-gt_data = import_data('odir_ground_truth.csv')
-pr_data = import_data('odir_predictions.csv')
-kappa, f1, auc, final_score = odir_metrics(gt_data[:, 1:], pr_data[:, 1:])
-print("kappa score:", kappa, " f-1 score:", f1, " AUC vlaue:", auc, " Final Score:", final_score)
\ No newline at end of file
+    def import_data(self, filepath):
+        with open(filepath, 'r') as f:
+            reader = csv.reader(f)
+            header = next(reader)
+            pr_data = [[int(row[0])] + list(map(float, row[1:])) for row in reader]
+        pr_data = np.array(pr_data)
+        return pr_data
+
+    def output(self):
+        gt_data = self.import_data('odir_ground_truth.csv')
+        pr_data = self.import_data('odir_predictions.csv')
+        kappa, f1, auc, final_score = self.odir_metrics(gt_data[:, 1:], pr_data[:, 1:])
+        print("Kappa score:", kappa)
+        print("F-1 score:", f1)
+        print("AUC value:", auc)
+        print("Final Score:", final_score)
diff --git a/odir_load_ground_truth_files.py b/odir_load_ground_truth_files.py
new file mode 100644
index 0000000..6d7738c
--- /dev/null
+++ b/odir_load_ground_truth_files.py
@@ -0,0 +1,59 @@
+# Copyright 2019-2020 Jordi Corbilla. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+import csv
+
+
+class GroundTruthFiles:
+    def __init__(self):
+        self.amd = []
+        self.cataract = []
+        self.diabetes = []
+        self.glaucoma = []
+        self.hypertension = []
+        self.myopia = []
+        self.others = []
+
+    def populate_vectors(self, ground_truth_file):
+        with open(ground_truth_file) as csvDataFile:
+            csv_reader = csv.reader(csvDataFile)
+
+            for row in csv_reader:
+                column_id = row[0]
+                normal = row[1]
+                diabetes = row[2]
+                glaucoma = row[3]
+                cataract = row[4]
+                amd = row[5]
+                hypertension = row[6]
+                myopia = row[7]
+                others = row[8]
+                # just discard the first row
+                if column_id != "ID":
+                    print("Processing image: " + column_id + "_left.jpg")
+                    if diabetes == '1':
+                        self.diabetes.append([column_id, normal, diabetes, glaucoma, cataract, amd, hypertension, myopia, others])
+                    if glaucoma == '1':
+                        self.glaucoma.append([column_id, normal, diabetes, glaucoma, cataract, amd, hypertension, myopia, others])
+                    if cataract == '1':
+                        self.cataract.append([column_id, normal, diabetes, glaucoma, cataract, amd, hypertension, myopia, others])
+                    if amd == '1':
+                        self.amd.append([column_id, normal, diabetes, glaucoma, cataract, amd, hypertension, myopia, others])
+                    if hypertension == '1':
+                        self.hypertension.append([column_id, normal, diabetes, glaucoma, cataract, amd, hypertension, myopia, others])
+                    if myopia == '1':
+                        self.myopia.append([column_id, normal, diabetes, glaucoma, cataract, amd, hypertension, myopia, others])
+                    if others == '1':
+                        self.others.append([column_id, normal, diabetes, glaucoma, cataract, amd, hypertension, myopia, others])
+
diff --git a/odir_model_advanced.py b/odir_model_advanced.py
index b9bae46..ccd16f6 100644
--- a/odir_model_advanced.py
+++ b/odir_model_advanced.py
@@ -1,4 +1,4 @@
-# Copyright 2019 Jordi Corbilla. All Rights Reserved.
+# Copyright 2019-2020 Jordi Corbilla. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -30,7 +30,7 @@ def compile(self):
         model.add(layers.Flatten())
         model.add(layers.Dense(64, activation='relu'))
         model.add(layers.Dense(8, activation='sigmoid'))
-        model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+        model.compile(optimizer='adam', loss='binary_crossentropy', metrics=self.metrics)
         self.show_summary(model)
         self.plot_summary(model, 'model_advanced.png')
         return model
diff --git a/odir_model_base.py b/odir_model_base.py
index d752a31..e0b1416 100644
--- a/odir_model_base.py
+++ b/odir_model_base.py
@@ -1,4 +1,4 @@
-# Copyright 2019 Jordi Corbilla. All Rights Reserved.
+# Copyright 2019-2020 Jordi Corbilla. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -17,8 +17,9 @@
 
 
 class ModelBase:
-    def __init__(self, input_shape):
+    def __init__(self, input_shape, metrics):
         self.input_shape = input_shape
+        self.metrics = metrics
 
     def show_summary(self, model):
         model.summary()
diff --git a/odir_model_factory.py b/odir_model_factory.py
index 6a3df22..24ff2cc 100644
--- a/odir_model_factory.py
+++ b/odir_model_factory.py
@@ -1,4 +1,4 @@
-# Copyright 2019 Jordi Corbilla. All Rights Reserved.
+# Copyright 2019-2020 Jordi Corbilla. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -27,11 +27,11 @@ class ModelTypes(enum.Enum):
 
 class Factory:
 
-    def __init__(self, input_shape):
+    def __init__(self, input_shape, metrics):
         self.Makers = {
-            ModelTypes.vgg16: Vgg16(input_shape),
-            ModelTypes.inception_v1: InceptionV1(input_shape),
-            ModelTypes.advanced_testing: Advanced(input_shape)
+            ModelTypes.vgg16: Vgg16(input_shape, metrics),
+            ModelTypes.inception_v1: InceptionV1(input_shape, metrics),
+            ModelTypes.advanced_testing: Advanced(input_shape, metrics)
         }
 
     def compile(self, model_type):
diff --git a/odir_model_inception_v1.py b/odir_model_inception_v1.py
index 9ef1433..1bba1ca 100644
--- a/odir_model_inception_v1.py
+++ b/odir_model_inception_v1.py
@@ -1,4 +1,4 @@
-# Copyright 2019 Jordi Corbilla. All Rights Reserved.
+# Copyright 2019-2020 Jordi Corbilla. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -41,7 +41,7 @@ def compile(self):
         output = Dense(8, activation='sigmoid')(dense_3)
         model = Model([input_img], output)
 
-        model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+        model.compile(optimizer='adam', loss='binary_crossentropy', metrics=self.metrics)
 
         self.show_summary(model)
         self.plot_summary(model, 'model_inception_v1.png')
diff --git a/odir_model_runner.py b/odir_model_runner.py
index 0fed976..6f6d2ed 100644
--- a/odir_model_runner.py
+++ b/odir_model_runner.py
@@ -1,4 +1,4 @@
-# Copyright 2019 Jordi Corbilla. All Rights Reserved.
+# Copyright 2019-2020 Jordi Corbilla. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/odir_model_vgg16.py b/odir_model_vgg16.py
index c30f5fc..af55d0a 100644
--- a/odir_model_vgg16.py
+++ b/odir_model_vgg16.py
@@ -1,4 +1,4 @@
-# Copyright 2019 Jordi Corbilla. All Rights Reserved.
+# Copyright 2019-2020 Jordi Corbilla. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,7 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
+import tensorflow
 from tensorflow.keras import models, layers
+from tensorflow.python.keras.optimizers import SGD
+
 from odir_model_base import ModelBase
 
 
@@ -20,39 +23,83 @@ class Vgg16(ModelBase):
 
     def compile(self):
         x = models.Sequential()
-
+        trainable = False
         # Block 1
-        x.add(layers.Conv2D(input_shape=self.input_shape, filters=64,kernel_size=(3,3),padding="same", activation="relu"))
-        x.add(layers.Conv2D(filters=64,kernel_size=(3,3),padding="same", activation="relu"))
-        x.add(layers.MaxPooling2D((2, 2), strides=(2, 2)))
+        layer = layers.Conv2D(input_shape=self.input_shape, filters=64,kernel_size=(3,3),padding="same", activation="relu")
+        layer.trainable = trainable
+        x.add(layer)
+        layer = layers.Conv2D(filters=64,kernel_size=(3,3),padding="same", activation="relu")
+        layer.trainable = trainable
+        x.add(layer)
+        layer = layers.MaxPooling2D((2, 2), strides=(2, 2))
+        layer.trainable = trainable
+        x.add(layer)
 
         # Block 2
-        x.add(layers.Conv2D(128, kernel_size=(3,3),padding="same", activation="relu"))
-        x.add(layers.Conv2D(128, kernel_size=(3,3),padding="same", activation="relu"))
-        x.add(layers.MaxPooling2D((2, 2), strides=(2, 2)))
+        layer = layers.Conv2D(128, kernel_size=(3,3),padding="same", activation="relu")
+        layer.trainable = trainable
+        x.add(layer)
+        layer = layers.Conv2D(128, kernel_size=(3,3),padding="same", activation="relu")
+        layer.trainable = trainable
+        x.add(layer)
+        layer = layers.MaxPooling2D((2, 2), strides=(2, 2))
+        layer.trainable = trainable
+        x.add(layer)
 
         # Block 3
-        x.add(layers.Conv2D(256, kernel_size=(3,3),padding="same", activation="relu"))
-        x.add(layers.Conv2D(256, kernel_size=(3,3),padding="same", activation="relu"))
-        x.add(layers.Conv2D(256, kernel_size=(3,3),padding="same", activation="relu"))
-        x.add(layers.MaxPooling2D((2, 2), strides=(2, 2)))
+        layer = layers.Conv2D(256, kernel_size=(3,3),padding="same", activation="relu")
+        layer.trainable = trainable
+        x.add(layer)
+        layer = layers.Conv2D(256, kernel_size=(3, 3), padding="same", activation="relu")
+        layer.trainable = trainable
+        x.add(layer)
+        layer = layers.Conv2D(256, kernel_size=(3, 3), padding="same", activation="relu")
+        layer.trainable = trainable
+        x.add(layer)
+        layer = layers.MaxPooling2D((2, 2), strides=(2, 2))
+        layer.trainable = trainable
+        x.add(layer)
 
         # Block 4
-        x.add(layers.Conv2D(512, kernel_size=(3,3),padding="same", activation="relu"))
-        x.add(layers.Conv2D(512, kernel_size=(3,3),padding="same", activation="relu"))
-        x.add(layers.Conv2D(512, kernel_size=(3,3),padding="same", activation="relu"))
-        x.add(layers.MaxPooling2D((2, 2), strides=(2, 2)))
+        layer = layers.Conv2D(512, kernel_size=(3, 3), padding="same", activation="relu")
+        layer.trainable = trainable
+        x.add(layer)
+        layer = layers.Conv2D(512, kernel_size=(3, 3), padding="same", activation="relu")
+        layer.trainable = trainable
+        x.add(layer)
+        layer = layers.Conv2D(512, kernel_size=(3, 3), padding="same", activation="relu")
+        layer.trainable = trainable
+        x.add(layer)
+        layer = layers.MaxPooling2D((2, 2), strides=(2, 2))
+        layer.trainable = trainable
+        x.add(layer)
 
         # Block 5
-        x.add(layers.Conv2D(512, kernel_size=(3,3),padding="same", activation="relu"))
-        x.add(layers.Conv2D(512, kernel_size=(3,3),padding="same", activation="relu"))
-        x.add(layers.Conv2D(512, kernel_size=(3,3),padding="same", activation="relu"))
-        x.add(layers.MaxPooling2D((2, 2), strides=(2, 2)))
+        layer = layers.Conv2D(512, kernel_size=(3, 3), padding="same", activation="relu")
+        layer.trainable = trainable
+        x.add(layer)
+        layer = layers.Conv2D(512, kernel_size=(3, 3), padding="same", activation="relu")
+        layer.trainable = trainable
+        x.add(layer)
+        layer = layers.Conv2D(512, kernel_size=(3, 3), padding="same", activation="relu")
+        layer.trainable = trainable
+        x.add(layer)
+        layer = layers.MaxPooling2D((2, 2), strides=(2, 2))
+        layer.trainable = trainable
+        x.add(layer)
 
-        x.add(layers.Flatten())
-        x.add(layers.Dense(4096, activation='relu'))
-        x.add(layers.Dense(4096, activation='relu'))
-        x.add(layers.Dense(1000, activation='softmax'))
+        layer = layers.Flatten()
+        layer.trainable = trainable
+        x.add(layer)
+        layer = layers.Dense(4096, activation='relu')
+        layer.trainable = trainable
+        x.add(layer)
+        layer = layers.Dense(4096, activation='relu')
+        layer.trainable = trainable
+        x.add(layer)
+        layer = layers.Dense(1000, activation='softmax')
+        layer.trainable = trainable
+        x.add(layer)
 
         # Transfer learning, load previous weights
         x.load_weights(r'C:\temp\vgg16_weights_tf_dim_ordering_tf_kernels.h5')
@@ -62,7 +109,9 @@ def compile(self):
 
         # Add new dense layer
         x.add(layers.Dense(8, activation='sigmoid'))
-        x.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+        #optimizer = tensorflow.keras.optimizers.SGD(learning_rate=1e-3)
+        #sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
+        x.compile(optimizer='adam', loss='binary_crossentropy', metrics=self.metrics)
 
         self.show_summary(x)
         self.plot_summary(x, 'model_vggnet.png')
diff --git a/odir_normalize_input.py b/odir_normalize_input.py
new file mode 100644
index 0000000..24fb75d
--- /dev/null
+++ b/odir_normalize_input.py
@@ -0,0 +1,25 @@
+# Copyright 2019 Jordi Corbilla. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+
+class Normalizer:
+    def normalize_vgg16(self, x_test):
+        x_test = x_test / 1.0
+        x_test = x_test[..., ::-1]
+        mean = [103.939, 116.779, 123.68]
+        x_test[..., 0] -= mean[0]
+        x_test[..., 1] -= mean[1]
+        x_test[..., 2] -= mean[2]
+        return x_test
diff --git a/odir_patients_to_numpy.py b/odir_patients_to_numpy.py
index d57af9c..6a6f066 100644
--- a/odir_patients_to_numpy.py
+++ b/odir_patients_to_numpy.py
@@ -1,4 +1,4 @@
-# Copyright 2019 Jordi Corbilla. All Rights Reserved.
+# Copyright 2019-2020 Jordi Corbilla. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -24,7 +24,7 @@
 
 
 class NumpyDataGenerator:
-    def __init__(self, training_path, testing_path, csv_path, csv_testing_path):
+    def __init__(self, training_path, testing_path, csv_path, csv_testing_path, augmented_path, csv_augmented_file):
         self.training_path = training_path
         self.testing_path = testing_path
         self.csv_path = csv_path
@@ -32,6 +32,8 @@ def __init__(self, training_path, testing_path, csv_path, csv_testing_path):
         self.logger = logging.getLogger('odir')
         self.total_records_training = 0
         self.total_records_testing = 0
+        self.csv_augmented_path = csv_augmented_file
+        self.augmented_path = augmented_path
 
     def npy_training_files(self, file_name_training, file_name_training_labels):
         training = []
@@ -196,12 +198,16 @@ def is_sickness(self, row, sickness):
 
     def npy_training_files_split_all(self, split_number, file_name_training, file_name_training_labels,
                                      file_name_testing,
-                                     file_name_testing_labels):
+                                     file_name_testing_labels, include_augmented):
+        split_factor = 10820
         training = []
         training_labels = []
+        training_2 = []
+        training_labels_2 = []
         testing = []
         testing_labels = []
         images_used = []
+        count_images = 0
 
         class_names = ['normal', 'diabetes', 'glaucoma', 'cataract', 'amd',
                        'hypertension', 'myopia', 'others']
@@ -237,8 +243,7 @@ def npy_training_files_split_all(self, split_number, file_name_training, file_na
                         if self.is_sickness(row, sickness) and class_count[sickness] < split_pocket:
                             testing.append(image)
                             images_used.append(row[0] + ',' + sickness + ',' + str(class_count[sickness]))
-                            testing_labels.append(
-                                [normal, diabetes, glaucoma, cataract, amd, hypertension, myopia, others])
+                            testing_labels.append([normal, diabetes, glaucoma, cataract, amd, hypertension, myopia, others])
                             self.total_records_testing = self.total_records_testing + 1
                             class_count[sickness] = class_count[sickness] + 1
                             found = True
@@ -246,9 +251,38 @@ def npy_training_files_split_all(self, split_number, file_name_training, file_na
 
                     if not found:
                         training.append(image)
-                        training_labels.append(
-                            [normal, diabetes, glaucoma, cataract, amd, hypertension, myopia, others])
+                        training_labels.append([normal, diabetes, glaucoma, cataract, amd, hypertension, myopia, others])
                         self.total_records_training = self.total_records_training + 1
+                        count_images = count_images + 1
+
+        if include_augmented:
+            with open(self.csv_augmented_path) as csvDataFile:
+                csv_reader = csv.reader(csvDataFile)
+                for row in csv_reader:
+                    column_id = row[0]
+                    normal = row[1]
+                    diabetes = row[2]
+                    glaucoma = row[3]
+                    cataract = row[4]
+                    amd = row[5]
+                    hypertension = row[6]
+                    myopia = row[7]
+                    others = row[8]
+                    # just discard the first row
+                    if column_id != "ID":
+                        self.logger.debug("Processing image: " + column_id)
+                        # load first the image from the folder
+                        eye_image = os.path.join(self.augmented_path, column_id)
+                        image = cv2.imread(eye_image)
+                        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+                        if count_images >= split_factor:
+                            training_2.append(image)
+                            training_labels_2.append([normal, diabetes, glaucoma, cataract, amd, hypertension, myopia, others])
+                        else:
+                            training.append(image)
+                            training_labels.append([normal, diabetes, glaucoma, cataract, amd, hypertension, myopia, others])
+                        self.total_records_training = self.total_records_training + 1
+                        count_images = count_images + 1
 
         testing = np.array(testing, dtype='uint8')
         testing_labels = np.array(testing_labels, dtype='uint8')
@@ -264,16 +298,31 @@ def npy_training_files_split_all(self, split_number, file_name_training, file_na
         # for example (6069 * 28 * 28 * 3)-> (6069 x 2352) (14,274,288)
         training = np.reshape(training, [training.shape[0], training.shape[1], training.shape[2], training.shape[3]])
 
+        training_2 = np.array(training_2, dtype='uint8')
+        training_labels_2 = np.array(training_labels_2, dtype='uint8')
+        # convert (number of images x height x width x number of channels) to (number of images x (height * width *3))
+        # for example (6069 * 28 * 28 * 3)-> (6069 x 2352) (14,274,288)
+        training_2 = np.reshape(training_2, [training_2.shape[0], training_2.shape[1], training_2.shape[2], training_2.shape[3]])
+
+        self.logger.debug(testing.shape)
+        self.logger.debug(testing_labels.shape)
+        self.logger.debug(training.shape)
+        self.logger.debug(training_labels.shape)
+        self.logger.debug(training_2.shape)
+        self.logger.debug(training_labels_2.shape)
+
         # save numpy array as .npy formats
-        np.save(file_name_training, training)
-        np.save(file_name_training_labels, training_labels)
+        np.save(file_name_training + '_1', training)
+        np.save(file_name_training_labels + '_1', training_labels)
+        np.save(file_name_training + '_2', training_2)
+        np.save(file_name_training_labels + '_2', training_labels_2)
         self.logger.debug("Closing CSV file")
         for sickness in class_names:
             self.logger.debug('found ' + sickness + ' ' + str(class_count[sickness]))
         csv_writer = csv.writer(open("files_used.csv", 'w', newline=''))
         for item in images_used:
             self.logger.debug(item)
-            entries = item.split(",");
+            entries = item.split(",")
             csv_writer.writerow(entries)
 
 
@@ -282,10 +331,13 @@ def main(argv):
     image_width = 224
     training_path = r'C:\temp\ODIR-5K_Training_Dataset_treated' + '_' + str(image_width)
     testing_path = r'C:\temp\ODIR-5K_Testing_Images_treated' + '_' + str(image_width)
-    csv_file = 'ground_truth\odir.csv'
+    augmented_path = r'C:\temp\ODIR-5K_Training_Dataset_augmented' + '_' + str(image_width)
+    csv_file = r'ground_truth\odir.csv'
+    csv_augmented_file = r'ground_truth\odir_augmented.csv'
     training_file = 'ground_truth\XYZ_ODIR.csv'
     logger.debug('Generating npy files')
-    generator = NumpyDataGenerator(training_path, testing_path, csv_file, training_file)
+    generator = NumpyDataGenerator(training_path, testing_path, csv_file, training_file, augmented_path,
+                                   csv_augmented_file)
 
     # Generate testing file
     # generator.npy_testing_files('odir_testing', 'odir_testing_labels')
@@ -295,8 +347,11 @@ def main(argv):
     # generator.npy_training_files_split(1000, 'odir_training',
     # 'odir_training_labels', 'odir_testing', 'odir_testing_labels')
 
-    generator.npy_training_files_split_all(400, 'odir_training' + '_' + str(image_width), 'odir_training_labels' + '_' + str(image_width), 'odir_testing' + '_' + str(image_width),
-                                           'odir_testing_labels' + '_' + str(image_width))
+    generator.npy_training_files_split_all(400, 'odir_training' + '_' + str(image_width),
+                                           'odir_training_labels' + '_' + str(image_width),
+                                           'odir_testing' + '_' + str(image_width),
+                                           'odir_testing_labels' + '_' + str(image_width),
+                                           True)
     end = time.time()
     logger.debug('Training Records ' + str(generator.total_records_training))
     logger.debug('Testing Records ' + str(generator.total_records_testing))
diff --git a/odir_pipeline_runer.py b/odir_pipeline_runer.py
index 8dd0a5a..0eb4a36 100644
--- a/odir_pipeline_runer.py
+++ b/odir_pipeline_runer.py
@@ -1,4 +1,4 @@
-# Copyright 2019 Jordi Corbilla. All Rights Reserved.
+# Copyright 2019-2020 Jordi Corbilla. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/odir_plot_input.py b/odir_plot_input.py
new file mode 100644
index 0000000..bd99ed5
--- /dev/null
+++ b/odir_plot_input.py
@@ -0,0 +1,77 @@
+# Copyright 2019 Jordi Corbilla. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging.config
+import tensorflow as tf
+from absl import app
+from odir_advance_plotting import Plotter
+from odir_kappa_score import FinalScore
+from odir_normalize_input import Normalizer
+from odir_predictions_writer import Prediction
+import odir
+
+def main(argv):
+    print(tf.version.VERSION)
+    image_size = 224
+    test_run = 'zC'
+
+    # load the data
+    (x_train, y_train), (x_test, y_test) = odir.load_data(image_size, 1)
+
+    class_names = ['Normal', 'Diabetes', 'Glaucoma', 'Cataract', 'AMD', 'Hypertension', 'Myopia', 'Others']
+
+    # plot data input
+    plotter = Plotter(class_names)
+    plotter.plot_input_images(x_train, y_train)
+
+    x_test_drawing = x_test
+
+    # normalize input based on model
+    normalizer = Normalizer()
+    x_test = normalizer.normalize_vgg16(x_test)
+
+    # load one of the test runs
+    model = tf.keras.models.load_model(r'C:\Users\thund\Source\Repos\TFM-ODIR\models\image_classification\modelvgg100.h5')
+    model.summary()
+
+    # display the content of the model
+    baseline_results = model.evaluate(x_test, y_test, verbose=2)
+    for name, value in zip(model.metrics_names, baseline_results):
+        print(name, ': ', value)
+    print()
+
+    # test a prediction
+    test_predictions_baseline = model.predict(x_test)
+    plotter.plot_confusion_matrix_generic(y_test, test_predictions_baseline, test_run, 0)
+
+    # save the predictions
+    prediction_writer = Prediction(test_predictions_baseline, 400)
+    prediction_writer.save()
+    prediction_writer.save_all(y_test)
+
+    # show the final score
+    score = FinalScore()
+    score.output()
+
+    # plot output results
+    plotter.plot_output(test_predictions_baseline, y_test, x_test_drawing)
+
+
+if __name__ == '__main__':
+    # create logger
+    logging.config.fileConfig('logging.conf')
+    logger = logging.getLogger('odir')
+    app.run(main)
diff --git a/odir_predictions_writer.py b/odir_predictions_writer.py
index cb7ffc3..35db8eb 100644
--- a/odir_predictions_writer.py
+++ b/odir_predictions_writer.py
@@ -1,4 +1,4 @@
-# Copyright 2019 Jordi Corbilla. All Rights Reserved.
+# Copyright 2019-2020 Jordi Corbilla. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/odir_rule_engine.py b/odir_rule_engine.py
index 8ac98f6..b3b884a 100644
--- a/odir_rule_engine.py
+++ b/odir_rule_engine.py
@@ -1,4 +1,4 @@
-# Copyright 2019 Jordi Corbilla. All Rights Reserved.
+# Copyright 2019-2020 Jordi Corbilla. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/odir_runner.py b/odir_runner.py
index 258d117..fe88c89 100644
--- a/odir_runner.py
+++ b/odir_runner.py
@@ -1,4 +1,4 @@
-# Copyright 2019 Jordi Corbilla. All Rights Reserved.
+# Copyright 2019-2020 Jordi Corbilla. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/odir_runner_inception.py b/odir_runner_inception.py
new file mode 100644
index 0000000..0baaed3
--- /dev/null
+++ b/odir_runner_inception.py
@@ -0,0 +1,117 @@
+# Copyright 2019-2020 Jordi Corbilla. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import csv
+import logging.config
+import os
+
+import matplotlib.pyplot as plt
+import numpy as np
+import tensorflow as tf
+from absl import app
+
+from odir_model_factory import Factory, ModelTypes
+from odir_predictions_writer import Prediction
+
+os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'
+os.environ["CUDA_VISIBLE_DEVICES"]="-1"
+from sklearn import metrics
+import odir
+
+
+def main(argv):
+    print(tf.version.VERSION)
+    image_size = 128
+
+    (x_train, y_train), (x_test, y_test) = odir.load_data(image_size)
+
+    x_train, x_test = x_train / 255.0, x_test / 255.0
+    x_train = (x_train - x_train.mean()) / x_train.std()
+    x_test = (x_test - x_test.mean()) / x_test.std()
+
+    plt.figure(figsize=(9, 9))
+    for i in range(100):
+        plt.subplot(10, 10, i + 1)
+        plt.xticks([])
+        plt.yticks([])
+        plt.grid(False)
+        plt.imshow(x_train[i])
+
+    plt.subplots_adjust(bottom=0.04, right=0.94, top=0.95, left=0.06, wspace=0.20, hspace=0.17)
+    plt.show()
+
+    factory = Factory((image_size,image_size,3))
+    model = factory.compile(ModelTypes.inception_v1)
+
+    print("Training")
+
+    class_weight = { 0:1.,
+                    1:1.583802025,
+                    2:8.996805112,
+                    3:10.24,
+                    4:10.05714286,
+                    5:14.66666667,
+                    6:10.7480916,
+                    7:2.505338078 }
+
+    history = model.fit(x_train, y_train, epochs=30,batch_size=32,verbose=1,shuffle=True,
+                        validation_data=(x_test, y_test), class_weight=class_weight)
+
+    plt.plot(history.history['accuracy'], label='accuracy')
+    plt.plot(history.history['val_accuracy'], label='val_accuracy')
+    plt.xlabel('Epoch')
+    plt.ylabel('Accuracy')
+    plt.legend(loc='lower right')
+    plt.show()
+
+    test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)
+    print(test_acc)
+
+    predictions = model.predict(x_test)
+
+    def odir_metrics(gt_data, pr_data):
+        th = 0.5
+        gt = gt_data.flatten()
+        pr = pr_data.flatten()
+        kappa = metrics.cohen_kappa_score(gt, pr > th)
+        f1 = metrics.f1_score(gt, pr > th, average='micro')
+        auc = metrics.roc_auc_score(gt, pr)
+        final_score = (kappa + f1 + auc) / 3.0
+        return kappa, f1, auc, final_score
+
+    def import_data(filepath):
+        with open(filepath, 'r') as f:
+            reader = csv.reader(f)
+            header = next(reader)
+            pr_data = [[int(row[0])] + list(map(float, row[1:])) for row in reader]
+        pr_data = np.array(pr_data)
+        return pr_data
+
+    prediction_writer = Prediction(predictions, 400)
+    prediction_writer.save()
+    prediction_writer.save_all(y_test)
+
+    gt_data = import_data('odir_ground_truth.csv')
+    pr_data = import_data('odir_predictions.csv')
+    kappa, f1, auc, final_score = odir_metrics(gt_data[:, 1:], pr_data[:, 1:])
+    print("kappa score:", kappa, " f-1 score:", f1, " AUC vlaue:", auc, " Final Score:", final_score)
+
+if __name__ == '__main__':
+    # create logger
+    logging.config.fileConfig('logging.conf')
+    logger = logging.getLogger('odir')
+    app.run(main)
diff --git a/odir_runner_vgg.py b/odir_runner_vgg.py
new file mode 100644
index 0000000..5bdc118
--- /dev/null
+++ b/odir_runner_vgg.py
@@ -0,0 +1,128 @@
+# Copyright 2019-2020 Jordi Corbilla. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import csv
+import logging.config
+import os
+
+import matplotlib.pyplot as plt
+import numpy as np
+import tensorflow as tf
+from absl import app
+
+from odir_model_factory import Factory, ModelTypes
+from odir_predictions_writer import Prediction
+
+os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+from sklearn import metrics
+import odir
+
+
+def main(argv):
+    print(tf.version.VERSION)
+    image_size = 224
+
+    (x_train, y_train), (x_test, y_test) = odir.load_data(image_size)
+
+    x_train, x_test = x_train / 1.0, x_test / 1.0
+
+    x_train = x_train[..., ::-1]
+    x_test = x_test[..., ::-1]
+    mean = [103.939, 116.779, 123.68]
+    x_train[..., 0] -= mean[0]
+    x_train[..., 1] -= mean[1]
+    x_train[..., 2] -= mean[2]
+    x_test[..., 0] -= mean[0]
+    x_test[..., 1] -= mean[1]
+    x_test[..., 2] -= mean[2]
+
+    x_train = (x_train - x_train.mean()) / x_train.std()
+    x_test = (x_test - x_test.mean()) / x_test.std()
+
+    plt.figure(figsize=(9, 9))
+    for i in range(100):
+        plt.subplot(10, 10, i + 1)
+        plt.xticks([])
+        plt.yticks([])
+        plt.grid(False)
+        plt.imshow(x_train[i])
+    plt.subplots_adjust(bottom=0.04, right=0.94, top=0.95, left=0.06, wspace=0.20, hspace=0.17)
+    plt.show()
+
+    factory = Factory((image_size, image_size, 3))
+    model = factory.compile(ModelTypes.vgg16)
+
+    print("Training")
+
+    class_weight = {0: 1.,
+                    1: 1.583802025,
+                    2: 8.996805112,
+                    3: 10.24,
+                    4: 10.05714286,
+                    5: 14.66666667,
+                    6: 10.7480916,
+                    7: 2.505338078}
+
+    history = model.fit(x_train, y_train, epochs=30, batch_size=32, verbose=1, shuffle=True,
+                        validation_data=(x_test, y_test), class_weight=class_weight)
+
+    plt.plot(history.history['accuracy'], label='accuracy')
+    plt.plot(history.history['val_accuracy'], label='val_accuracy')
+    plt.xlabel('Epoch')
+    plt.ylabel('Accuracy')
+    plt.legend(loc='lower right')
+    plt.show()
+
+    test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)
+    print(test_acc)
+
+    predictions = model.predict(x_test)
+
+    def odir_metrics(gt_data, pr_data):
+        th = 0.5
+        gt = gt_data.flatten()
+        pr = pr_data.flatten()
+        kappa = metrics.cohen_kappa_score(gt, pr > th)
+        f1 = metrics.f1_score(gt, pr > th, average='micro')
+        auc = metrics.roc_auc_score(gt, pr)
+        final_score = (kappa + f1 + auc) / 3.0
+        return kappa, f1, auc, final_score
+
+    def import_data(filepath):
+        with open(filepath, 'r') as f:
+            reader = csv.reader(f)
+            header = next(reader)
+            pr_data = [[int(row[0])] + list(map(float, row[1:])) for row in reader]
+        pr_data = np.array(pr_data)
+        return pr_data
+
+    prediction_writer = Prediction(predictions, 400)
+    prediction_writer.save()
+    prediction_writer.save_all(y_test)
+
+    gt_data = import_data('odir_ground_truth.csv')
+    pr_data = import_data('odir_predictions.csv')
+    kappa, f1, auc, final_score = odir_metrics(gt_data[:, 1:], pr_data[:, 1:])
+    print("kappa score:", kappa, " f-1 score:", f1, " AUC vlaue:", auc, " Final Score:", final_score)
+
+
+if __name__ == '__main__':
+    # create logger
+    logging.config.fileConfig('logging.conf')
+    logger = logging.getLogger('odir')
+    app.run(main)
diff --git a/odir_testing_image_treatment_job.py b/odir_testing_image_treatment_job.py
index 9583098..c1a925e 100644
--- a/odir_testing_image_treatment_job.py
+++ b/odir_testing_image_treatment_job.py
@@ -1,4 +1,4 @@
-# Copyright 2019 Jordi Corbilla. All Rights Reserved.
+# Copyright 2019-2020 Jordi Corbilla. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/odir_training_data_parser.py b/odir_training_data_parser.py
index 0f99f11..3b338e8 100644
--- a/odir_training_data_parser.py
+++ b/odir_training_data_parser.py
@@ -1,4 +1,4 @@
-# Copyright 2019 Jordi Corbilla. All Rights Reserved.
+# Copyright 2019-2020 Jordi Corbilla. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/odir_training_image_treatment_job.py b/odir_training_image_treatment_job.py
index ac2f747..ee04018 100644
--- a/odir_training_image_treatment_job.py
+++ b/odir_training_image_treatment_job.py
@@ -1,4 +1,4 @@
-# Copyright 2019 Jordi Corbilla. All Rights Reserved.
+# Copyright 2019-2020 Jordi Corbilla. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/odir_vgg_testing_inference.py b/odir_vgg_testing_inference.py
new file mode 100644
index 0000000..bd99ed5
--- /dev/null
+++ b/odir_vgg_testing_inference.py
@@ -0,0 +1,77 @@
+# Copyright 2019 Jordi Corbilla. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging.config
+import tensorflow as tf
+from absl import app
+from odir_advance_plotting import Plotter
+from odir_kappa_score import FinalScore
+from odir_normalize_input import Normalizer
+from odir_predictions_writer import Prediction
+import odir
+
+def main(argv):
+    print(tf.version.VERSION)
+    image_size = 224
+    test_run = 'zC'
+
+    # load the data
+    (x_train, y_train), (x_test, y_test) = odir.load_data(image_size, 1)
+
+    class_names = ['Normal', 'Diabetes', 'Glaucoma', 'Cataract', 'AMD', 'Hypertension', 'Myopia', 'Others']
+
+    # plot data input
+    plotter = Plotter(class_names)
+    plotter.plot_input_images(x_train, y_train)
+
+    x_test_drawing = x_test
+
+    # normalize input based on model
+    normalizer = Normalizer()
+    x_test = normalizer.normalize_vgg16(x_test)
+
+    # load one of the test runs
+    model = tf.keras.models.load_model(r'C:\Users\thund\Source\Repos\TFM-ODIR\models\image_classification\modelvgg100.h5')
+    model.summary()
+
+    # display the content of the model
+    baseline_results = model.evaluate(x_test, y_test, verbose=2)
+    for name, value in zip(model.metrics_names, baseline_results):
+        print(name, ': ', value)
+    print()
+
+    # test a prediction
+    test_predictions_baseline = model.predict(x_test)
+    plotter.plot_confusion_matrix_generic(y_test, test_predictions_baseline, test_run, 0)
+
+    # save the predictions
+    prediction_writer = Prediction(test_predictions_baseline, 400)
+    prediction_writer.save()
+    prediction_writer.save_all(y_test)
+
+    # show the final score
+    score = FinalScore()
+    score.output()
+
+    # plot output results
+    plotter.plot_output(test_predictions_baseline, y_test, x_test_drawing)
+
+
+if __name__ == '__main__':
+    # create logger
+    logging.config.fileConfig('logging.conf')
+    logger = logging.getLogger('odir')
+    app.run(main)