Update README.md

README.md

@@ -58,9 +58,7 @@ model = build_model() # Architecture defined in the `build_model` function
 model.load_weights(model_path)
 ```
 
-#### 3\. Model Definition
-
-The `build_model` function defines the architecture:
+The `build_model` function defines the architecture as:
 
 ```python
 import tensorflow as tf
@@ -68,7 +66,6 @@ from tensorflow import keras
 from tensorflow.keras import layers
 
 # Global parameters for model input shape (ensure these are defined before calling build_model)
-# Example:
 # TIME_STEPS = 30
 # HEIGHT = 299
 # WIDTH = 299
@@ -76,22 +73,17 @@ from tensorflow.keras import layers
 def build_model(lstm_hidden_size=256, num_classes=2, dropout_rate=0.5):
     # Input shape: (batch_size, TIME_STEPS, HEIGHT, WIDTH, 3)
     inputs = layers.Input(shape=(TIME_STEPS, HEIGHT, WIDTH, 3))
-
     # TimeDistributed layer to apply the base model to each frame
     base_model = keras.applications.Xception(weights='imagenet', include_top=False, pooling='avg')
     # For inference, we don't need to set trainable, but if you plan to retrain, you can set accordingly
     # base_model.trainable = False
-
     # Apply TimeDistributed wrapper
     x = layers.TimeDistributed(base_model)(inputs)
     # x shape: (batch_size, TIME_STEPS, 2048)
-
     # LSTM layer
     x = layers.LSTM(lstm_hidden_size)(x)
-
     x = layers.Dropout(dropout_rate)(x)
     outputs = layers.Dense(num_classes, activation='softmax')(x)
-
     model = keras.Model(inputs, outputs)
     return model
 ```