diff --git a/binary-compile.py b/binary-compile.py
index 87f07e3..587a070 100644
--- a/binary-compile.py
+++ b/binary-compile.py
@@ -1,34 +1,43 @@
-import numpy as np
-import matplotlib.pyplot as plt
 import tensorflow as tf 
 import os 
-
+from safetensors.numpy import save_file
 DIR = "data/binary-classification"
 
 #Import Data
 PATH = os.path.join(os.getcwd(), DIR)
 
-training_data = os.path.join(PATH, "train")
-validation_data = os.path.join(PATH, "valid")
-test_data = os.path.join(PATH, "test")
+training_path = os.path.join(PATH, "train")
+test_path = os.path.join(PATH, "test")
 
 BATCH_SIZE = 64
 IMG_SIZE = (224,224)
 
 #TODO: Import data from both directories, then resplit into test, train, and validation
 
-print(f"Train: {len(training_data)}\nValid: {len(validation_data)}\nTest: {len(test_data)}")
+training_data = tf.keras.utils.image_dataset_from_directory(training_path,
+                                             shuffle=True,
+                                             batch_size=BATCH_SIZE,
+                                             image_size=IMG_SIZE,
+                                             validation_split=0.2,
+                                             subset="training",
+                                             seed=1234)
+validation_data = tf.keras.utils.image_dataset_from_directory(training_path,
+                                               shuffle=True,
+                                               batch_size=BATCH_SIZE,
+                                               image_size=IMG_SIZE,
+                                               validation_split=0.2,
+                                               subset="validation",
+                                               seed=1234)
+test_data = tf.keras.utils.image_dataset_from_directory(test_path,
+                                         shuffle=True,
+                                         batch_size=BATCH_SIZE,
+                                         image_size=IMG_SIZE)
+
+
 
 
 #View Data
-plt.figure(figsize=(10,10))
-for images, labels in training_data.take(1):
-    for i in range(9):
-        ax = plt.subplot(3, 3, i+1)
-        plt.imshow(images[i].numpy().astype("uint8"))
-        plt.title(class_names[labels[i]])
-        plt.axis("off")
-plt.show()
+
 
 
 #Init Prefetching
@@ -67,10 +76,10 @@ base_model.summary()
 global_avg_layer = tf.keras.layers.GlobalAveragePooling2D()
 feature_batch_avg = global_avg_layer(feature_batch)
 
-prediction_layer = tf.keras.layers.Dense(38, activation="softmax")
+prediction_layer = tf.keras.layers.Dense(2, activation="softmax")
 predication_batch = prediction_layer(feature_batch_avg)
 
-inputs = tf.keras.Input(shape=(160,160,3))
+inputs = tf.keras.Input(shape=IMG_SHAPE)
 x = data_augmentation(inputs)
 x = base_model(x, training=False)
 x = global_avg_layer(x)
@@ -86,16 +95,14 @@ model.summary()
 #Compile the Model
 base_learning_rate = 0.0001
 
-training_data = training_data.map(lambda x,y: (x, tf.one_hot(y,38)))
-validation_data = validation_data.map(lambda x,y: (x, tf.one_hot(y,38)))
-test_data = test_data.map(lambda x,y: (x, tf.one_hot(y,38)))
+training_data = training_data.map(lambda x,y: (x, tf.one_hot(y,2)))
+validation_data = validation_data.map(lambda x,y: (x, tf.one_hot(y,2)))
+test_data = test_data.map(lambda x,y: (x, tf.one_hot(y,2)))
 
 optimizer = tf.keras.optimizers.Adam(learning_rate=base_learning_rate)
 loss = tf.keras.losses.CategoricalCrossentropy()
 metrics = [tf.keras.metrics.CategoricalAccuracy()]
 
-
-
 model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
 
 #Train the Model
@@ -124,7 +131,6 @@ history = model.fit(training_data,
                     validation_data=validation_data,
                     callbacks=[lr_schedule, early_stopping])
 
-model.save("crop-classifier-better-test.keras")
 
 #Evaluate Model
 results = model.evaluate(validation_data)
@@ -134,3 +140,8 @@ print(f"Validation Accuracy: {results[1]}")
 results = model.evaluate(test_data)
 print(f"Test Loss: {results[0]}")
 print(f"Test Accuracy: {results[1]}")
+
+weights = model.get_weights()
+weights_dict = {f"weight_{i}": w for i, w in enumerate(weights)}
+
+save_file(weights_dict, "models/mobilenet_v3.safetensors")
diff --git a/pyproject.toml b/pyproject.toml
deleted file mode 100644
index cda0458..0000000
--- a/pyproject.toml
+++ /dev/null
@@ -1,20 +0,0 @@
-[build-system]
-requires = ["setuptools", "wheel"]
-build-backend = "setuptools.build_meta"
-
-[project]
-name = "dermy-model"
-version = "0.1.0"
-description = "A Image Classification Model for classifying Moles"
-authors = [{ name = "r0r5chach", email = "r0r-5chach.xyz@proton.me" }]
-readme = "README.md"
-license = { file = "LICENSE" }
-dependencies = [
-	"matplotlib",
-	"numpy",
-	"tensorflow",
-]
-
-[too.setuptools]
-packages = ["dermy-model"]
-include_package_data = true