Add convnext mode and weights.

Models/model_sk_convnext_v1.py

@@ -0,0 +1,256 @@
+import tensorflow as tf
+from tensorflow.keras import layers, models, regularizers
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.callbacks import TensorBoard, ModelCheckpoint, EarlyStopping, ReduceLROnPlateau
+from tensorflow.keras.applications import ConvNeXtTiny
+from tensorflow.keras.applications.convnext import preprocess_input
+from time import time
+import matplotlib.pyplot as plt
+
+# This is the new 2026 version
+# This model was trained with 13,185 images
+# See CNNImageProcessor solution for create the test images for training this model
+
+
+
+# Figure out if we are training in CPU or GPU
+print("GPUs:", tf.config.list_physical_devices('GPU'))
+# -----------------------
+# ConvNeXt-Tiny Base Model
+# -----------------------
+modelname='convnext_20260228_90.h5.keras'
+
+# convneXt was pretrained with 224 but our image data is 128 so we upscale our images to match the 224 requirements of the model
+actualImageDimension=224
+convneXtImageDimension=224
+
+# Tensorboard
+log_dir = f'logs/convnext_{int(time())}'
+tensorboard = TensorBoard(log_dir=log_dir)
+
+# -----------------------
+# Configuration
+# -----------------------
+shuffle_count=3000
+dataset_path = 'C:\\boneyard\\DeepLearning\\data'
+image_size = (actualImageDimension, actualImageDimension)
+batch_size = 16    # try 16  was 32
+image_size=(actualImageDimension, actualImageDimension)
+
+
+# -----------------------
+# Dataset Loading
+# -----------------------
+
+train_ds = tf.keras.preprocessing.image_dataset_from_directory(
+    dataset_path,
+    label_mode="binary",
+    subset="training",
+    validation_split=0.2,
+    image_size=image_size,
+    color_mode='rgb',   # IMPORTANT for grayscale datasets
+    batch_size=batch_size,
+    seed=50
+)
+
+val_ds = tf.keras.preprocessing.image_dataset_from_directory(
+    dataset_path,
+    label_mode="binary",
+    subset="validation",
+    validation_split=0.2,
+    image_size=image_size,
+    color_mode='rgb',
+    batch_size=batch_size,
+    seed=50
+)
+
+# -----------------------
+# 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
+
+def preprocess_val(x, y):
+    return x, y
+
+val_ds = (
+    val_ds
+    .prefetch(tf.data.AUTOTUNE)
+)
+
+train_ds = (
+    train_ds
+    .shuffle(3000)
+    .prefetch(tf.data.AUTOTUNE)
+)
+
+
+# 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
+# -----------------------
+
+base_model = ConvNeXtTiny(
+    weights='imagenet',
+    include_top=False,
+    input_shape=(convneXtImageDimension, convneXtImageDimension, 3)
+)
+
+base_model.trainable = False  # Freeze for initial training
+
+# -----------------------
+# Build Full Model (Preprocessing Inside Model)
+# -----------------------
+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)
+x = layers.Dropout(0.4)(x)
+
+# End Dense Head
+outputs = layers.Dense(1, activation="sigmoid")(x)
+model = tf.keras.Model(inputs, outputs)
+
+
+model.compile(
+    optimizer=Adam(learning_rate=1e-4),
+    loss='binary_crossentropy',
+    metrics=['accuracy']
+)
+
+model.summary()
+
+# -----------------------
+# Callbacks
+# -----------------------
+
+
+early_stopping = EarlyStopping(
+    monitor='val_loss',
+    patience=15,
+    restore_best_weights=True,
+    verbose=1
+)
+
+checkpointer = ModelCheckpoint(
+    filepath=modelname,
+    monitor='val_accuracy',
+    save_best_only=True,
+    verbose=1
+)
+
+lr_scheduler = ReduceLROnPlateau(
+    monitor='val_loss',
+    factor=0.5,
+    patience=5,
+    min_lr=1e-6,
+    verbose=1
+)
+
+# -----------------------
+# Initial Training
+# -----------------------
+
+history = model.fit(
+    train_ds,
+    epochs=50,
+    validation_data=val_ds,
+    callbacks=[tensorboard, lr_scheduler, early_stopping, checkpointer]
+)
+
+# -----------------------
+# Fine-Tuning
+# -----------------------
+
+base_model.trainable = True
+
+# Freeze early layers (recommended)
+for layer in base_model.layers[:-40]:
+    layer.trainable = False
+
+model.compile(
+    optimizer=Adam(1e-5),
+    loss='binary_crossentropy',
+    metrics=['accuracy']
+)
+
+history_fine = model.fit(
+    train_ds,
+    epochs=50,
+    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)