train_resisc45.py

import tensorflow as tf
from tensorflow.keras.applications import EfficientNetB0
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout
from tensorflow.keras.models import Model
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
import matplotlib.pyplot as plt
import os

# Paths
DATASET_DIR = r"C:\Path\To\NWPU-RESISC45"  # Path to your Dataset
IMG_SIZE = (128, 128)
BATCH_SIZE = 16
NUM_CLASSES = 45
EPOCHS = 50
SEED = 42

# Sanity check: verify class folders
class_names = sorted(os.listdir(DATASET_DIR))
print(f"Detected {len(class_names)} classes:", class_names)

# Augmentation for training set
data_augmentation = tf.keras.Sequential([
    tf.keras.layers.RandomFlip("horizontal_and_vertical"),
    tf.keras.layers.RandomRotation(0.2),
    tf.keras.layers.RandomZoom(0.1),
])

# Load Datasets
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
    DATASET_DIR,
    validation_split=0.2,
    subset="training",
    seed=SEED,
    image_size=IMG_SIZE,
    batch_size=BATCH_SIZE,
    label_mode='categorical'
)

val_ds = tf.keras.preprocessing.image_dataset_from_directory(
    DATASET_DIR,
    validation_split=0.2,
    subset="validation",
    seed=SEED,
    image_size=IMG_SIZE,
    batch_size=BATCH_SIZE,
    label_mode='categorical'
)

# Apply augmentation only to training
train_ds = train_ds.map(lambda x, y: (data_augmentation(x, training=True), y))

# Prefetch
AUTOTUNE = tf.data.AUTOTUNE
train_ds = train_ds.prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.prefetch(buffer_size=AUTOTUNE)

# Build Model
base_model = EfficientNetB0(weights='imagenet', include_top=False, input_shape=(*IMG_SIZE, 3))
base_model.trainable = False  # Freeze base model initially

x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dropout(0.3)(x)
output = Dense(NUM_CLASSES, activation='softmax')(x)

model = Model(inputs=base_model.input, outputs=output)

model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

model.summary()

# Callbacks
callbacks = [
    ModelCheckpoint("best_model.h5", save_best_only=True, monitor="val_accuracy", mode="max"),
    EarlyStopping(patience=10, restore_best_weights=True),
    ReduceLROnPlateau(factor=0.2, patience=5, min_lr=1e-6)
]

# Train
history = model.fit(
    train_ds,
    validation_data=val_ds,
    epochs=EPOCHS,
    callbacks=callbacks
)

# Optional: Fine-tune after warmup (unfreeze base model)
base_model.trainable = True
model.compile(optimizer=tf.keras.optimizers.Adam(1e-5),  # Lower LR for fine-tuning
              loss='categorical_crossentropy',
              metrics=['accuracy'])

history_finetune = model.fit(
    train_ds,
    validation_data=val_ds,
    epochs=10,
    callbacks=callbacks
)

# Plotting
plt.plot(history.history["accuracy"] + history_finetune.history["accuracy"], label="train acc")
plt.plot(history.history["val_accuracy"] + history_finetune.history["val_accuracy"], label="val acc")
plt.xlabel("Epochs")
plt.ylabel("Accuracy")
plt.legend()
plt.title("Training vs Validation Accuracy")
plt.grid()
plt.savefig("training_accuracy.png")
plt.show()

# Save class names
with open("class_names.txt", "w") as f:
    for name in class_names:
        f.write(name + "\n")