diff --git a/Models/model_sk_convnext_v1.py b/Models/model_sk_convnext_v1.py
index 1440a9b..9e7b056 100644
--- a/Models/model_sk_convnext_v1.py
+++ b/Models/model_sk_convnext_v1.py
@@ -39,7 +39,7 @@ tensorboard = TensorBoard(log_dir=log_dir)
 # Configuration
 # -----------------------
 shuffle_count=3000
-dataset_path = 'C:\\boneyard\\DeepLearning\\data'
+dataset_path = '/home/pi/DeepLearning/Data'
 image_size = (actualImageDimension, actualImageDimension)
 batch_size = 16    # try 16  was 32
 image_size=(actualImageDimension, actualImageDimension)
@@ -74,22 +74,7 @@ val_ds = tf.keras.preprocessing.image_dataset_from_directory(
 # -----------------------
 # Data Augmentation
 # -----------------------
-
-# data_augmentation = tf.keras.Sequential([
-#     layers.RandomFlip("horizontal"),
-#     layers.RandomRotation(0.1)
-# ])
-#data_augmentation = tf.keras.Sequential([
-#    layers.RandomFlip("horizontal"),
-#    layers.RandomRotation(0.1),  
-#    layers.RandomRotation(0.1, fill_mode="nearest"),
-#    layers.RandomZoom(0.1)
-#])
-
-
-# def preprocess_train(x, y):
-#     x = data_augmentation(x, training=True)
-#     return x, y
+# I do this in c#-land
 
 def preprocess_val(x, y):
     return x, y
@@ -106,22 +91,6 @@ train_ds = (
 )
 
 
-# for images, labels in train_ds.take(1):
-
-#     plt.figure(figsize=(10,10))
-
-#     for i in range(12):
-#         ax = plt.subplot(3,4,i+1)
-#         plt.imshow(images[i].numpy().astype("uint8"))
-#         plt.title(int(labels[i].numpy()))
-#         plt.axis("off")
-
-#     plt.tight_layout()
-#     plt.show()
-
-
-
-
 # -----------------------
 # ConvNeXt-Tiny Base Model
 # -----------------------
@@ -143,13 +112,6 @@ inputs = tf.keras.Input(shape=(actualImageDimension, actualImageDimension, 3))
 
 x = preprocess_input(inputs)
 x = base_model(x)
-# Dense Head
-# x = layers.GlobalAveragePooling2D()(x)
-# x = layers.BatchNormalization()(x)
-# x = layers.Dense(512, activation="relu")(x)
-# x = layers.Dropout(0.3)(x)
-# x = layers.Dense(128, activation="relu")(x)
-
 x = layers.GlobalAveragePooling2D()(x)
 x = layers.BatchNormalization()(x)
 x = layers.Dense(256, activation="relu")(x)
@@ -228,36 +190,3 @@ history_fine = model.fit(
     validation_data=val_ds,
     callbacks=[tensorboard, lr_scheduler, early_stopping, checkpointer]
 )
-
-
-# -----------------------
-# Plot Results
-# -----------------------
-
-def plot_history(hist, title_prefix=""):
-    plt.figure()
-    plt.plot(hist.history['accuracy'], label='Train Accuracy')
-    plt.plot(hist.history['val_accuracy'], label='Val Accuracy')
-    plt.title(f'{title_prefix} Accuracy')
-    plt.xlabel('Epochs')
-    plt.ylabel('Accuracy')
-    plt.legend()
-    plt.show()
-
-    plt.figure()
-    plt.plot(hist.history['loss'], label='Train Loss')
-    plt.plot(hist.history['val_loss'], label='Val Loss')
-    plt.title(f'{title_prefix} Loss')
-    plt.xlabel('Epochs')
-    plt.ylabel('Loss')
-    plt.legend()
-    plt.show()
-
-plot_history(history, "Initial Training")
-plot_history(history_fine, "Fine-Tuning")
-
-# -----------------------
-# Save Final Model
-# -----------------------
-
-#model.save(modelname)
\ No newline at end of file