diff --git a/models/MyModel/Model - Architecture.jpg b/models/MyModel/Model - Architecture.jpg
new file mode 100644
index 0000000..ba24f9a
Binary files /dev/null and b/models/MyModel/Model - Architecture.jpg differ
diff --git a/models/MyModel/Model - Train and Validation Accuracy.jpg b/models/MyModel/Model - Train and Validation Accuracy.jpg
new file mode 100644
index 0000000..219eaf5
Binary files /dev/null and b/models/MyModel/Model - Train and Validation Accuracy.jpg differ
diff --git a/models/MyModel/lite.py b/models/MyModel/lite.py
new file mode 100644
index 0000000..e4d9165
--- /dev/null
+++ b/models/MyModel/lite.py
@@ -0,0 +1,67 @@
+# import the necessary packages
+from keras.models import Sequential
+from keras.layers.normalization import BatchNormalization
+from keras.layers.convolutional import Conv2D
+from keras.layers.convolutional import MaxPooling2D
+from keras.layers.core import Activation
+from keras.layers.core import Flatten
+from keras.layers.core import Dropout
+from keras.layers.core import Dense
+from keras import backend as K
+
+class Lite:
+	@staticmethod
+	def build(width, height, depth, classes):
+		# initialize the model along with the input shape to be
+		# "channels last" and the channels dimension itself
+		model = Sequential()
+		inputShape = (height, width, depth)
+		chanDim = -1
+
+		# if we are using "channels first", update the input shape
+		# and channels dimension
+		if K.image_data_format() == "channels_first":
+			inputShape = (depth, height, width)
+			chanDim = 1
+
+		# CONV => RELU => POOL
+		model.add(Conv2D(32, (3, 3), padding="same",
+			input_shape=inputShape))
+		model.add(Activation("relu"))
+		model.add(BatchNormalization(axis=chanDim))
+		model.add(MaxPooling2D(pool_size=(3, 3)))
+		model.add(Dropout(0.25))
+
+		# (CONV => RELU) * 2 => POOL
+		model.add(Conv2D(64, (3, 3), padding="same"))
+		model.add(Activation("relu"))
+		model.add(BatchNormalization(axis=chanDim))
+		model.add(Conv2D(64, (3, 3), padding="same"))
+		model.add(Activation("relu"))
+		model.add(BatchNormalization(axis=chanDim))
+		model.add(MaxPooling2D(pool_size=(2, 2)))
+		model.add(Dropout(0.25))
+
+		# (CONV => RELU) * 2 => POOL
+		model.add(Conv2D(128, (3, 3), padding="same"))
+		model.add(Activation("relu"))
+		model.add(BatchNormalization(axis=chanDim))
+		model.add(Conv2D(128, (3, 3), padding="same"))
+		model.add(Activation("relu"))
+		model.add(BatchNormalization(axis=chanDim))
+		model.add(MaxPooling2D(pool_size=(2, 2)))
+		model.add(Dropout(0.25))
+
+		# first (and only) set of FC => RELU layers
+		model.add(Flatten())
+		model.add(Dense(1024))
+		model.add(Activation("relu"))
+		model.add(BatchNormalization())
+		model.add(Dropout(0.5))
+
+		# softmax classifier
+		model.add(Dense(classes))
+		model.add(Activation("softmax"))
+
+		# return the constructed network architecture
+		return model
\ No newline at end of file