diff --git a/requirements.txt b/requirements.txt
index 8cdba01..03e2997 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,6 +4,8 @@ tensorflow>=2.10.0
 matplotlib>=3.7.2
 Pillow>=10.0.0
 keras>=2.10.0
+# Hyperparameter optimization
+keras-tuner<=1.4.6
 # Inference
 rembg>=2.0.50
 onnxruntime>=1.15.1
diff --git a/training/train.py b/training/train.py
index 59f9a56..b4c7c86 100644
--- a/training/train.py
+++ b/training/train.py
@@ -12,6 +12,7 @@
 from keras.callbacks import EarlyStopping, ModelCheckpoint
 import os
 import random
+import keras_tuner
 
 import tensorflow as tf
 # Ignore warnings
@@ -45,6 +46,8 @@
     "img_width": img_width,
     "seed": random.randint(0, 1000) if random_seed else 123
 }
+tune_model = False
+train = True
 
 # Load dataset and classes
 train_ds, val_ds, class_names = load_dataset(**config)
@@ -125,16 +128,25 @@
 device = tf.test.gpu_device_name() if tf.test.is_gpu_available() else '/CPU:0'
 print("Using Device:", device)
 
-with tf.device(device):
-    history = model.fit(
-        train_ds,
-        validation_data=val_ds,
-        epochs=epochs,
-        callbacks=[lr, early_stopping, model_checkpoint, discord_callback],
-        class_weight=class_weights
-    )
-# Plot and save model score
-plot_model_score(history, name, model_type)
+if tune_model:
+    tuner = keras_tuner.RandomSearch(
+        model,
+        objective='val_loss',
+        max_trials=5)
+    tuner.search(train_ds, epochs=5, validation_data=val_ds)
+    model = tuner.get_best_models()[0]
+
+if train or not tune_model:
+    with tf.device(device):
+        history = model.fit(
+            train_ds,
+            validation_data=val_ds,
+            epochs=epochs,
+            callbacks=[lr, early_stopping, model_checkpoint, discord_callback],
+            class_weight=class_weights
+        )
+    # Plot and save model score
+    plot_model_score(history, name, model_type)
 
 # Save model
 model.save(f"{save_path}{name}.h5")