Task: - Download the UCM dataset from the official source or any reputable repository. - Preprocess the dataset by splitting it into training, validation, and test sets. Please use an 80-10-10 split, respectively. - Choose a suitable deep-learning algorithm for scene classification. You can use popular classifiers such as Convolutional Neural Networks (CNNs). - Implement the chosen algorithm in Python, using appropriate libraries. - Train the classifier using the training set and tune the hyperparameters using the validation set. - Evaluate the trained classifier on the test set using the following metrics: Accuracy: The overall classification accuracy. Precision: The ability to correctly classify positive samples. Recall: The ability to correctly identify positive samples. F1-score: The harmonic mean of precision and recall. Confusion Matrix: A table showing the number of correct and incorrect predictions for each class. Report: Prepare a one-page report summarizing the results of your scene classification experiment and the architecture of your network. Submit your Python code along with the one-page report summarizing the results. Make sure to organize your code and report in a clear and readable manner.

Sure, here's an outline of the steps you'll need to take to complete this task:

1. Download the UCM dataset from the official source or any reputable repository.
2. Preprocess the dataset by splitting it into training, validation, and test sets. Use an 80-10-10 split, respectively. You can use a library like scikit-learn to do this.
3. Choose a suitable deep-learning algorithm for scene classification. Convolutional Neural Networks (CNNs) are a popular choice for image classification tasks like this.
4. Implement the chosen algorithm in Python, using appropriate libraries such as TensorFlow or PyTorch.
5. Train the classifier using the training set and tune the hyperparameters using the validation set.
6. Evaluate the trained classifier on the test set using the following metrics: accuracy, precision, recall, F1-score, and confusion matrix. You can use libraries like scikit-learn to compute these metrics.
7. Prepare a one-page report summarizing the results of your scene classification experiment and the architecture of your network. Include any relevant information such as which hyperparameters you tuned and which ones you used for the final model.

Here's some sample code to get you started:

# Step 1: Download UCM dataset
# TODO: Download dataset and extract files

# Step 2: Preprocess dataset
from sklearn.model_selection import train_test_split

# TODO: Load dataset into memory
X_train_val, X_test, y_train_val, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
X_train, X_val, y_train, y_val = train_test_split(X_train_val, y_train_val, test_size=0.1, random_state=42)

# Step 3: Choose deep-learning algorithm
import tensorflow as tf
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

model = tf.keras.Sequential([
    Conv2D(32, (3, 3), activation='relu', input_shape=(256, 256, 3)),
    MaxPooling2D((2, 2)),
    Conv2D(64, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Conv2D(128, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Conv2D(256, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Flatten(),
    Dense(256, activation='relu'),
    Dense(21, activation='softmax')
])

# Step 4: Implement algorithm in Python
model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

# Step 5: Train classifier
history = model.fit(X_train, y_train, epochs=10, validation_data=(X_val, y_val))

# Step 6: Evaluate trained classifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix

y_pred = model.predict(X_test)
y_pred_classes = np.argmax(y_pred, axis=1)
y_test_classes = np.argmax(y_test, axis=1)

accuracy = accuracy_score(y_test_classes, y_pred_classes)
precision = precision_score(y_test_classes, y_pred_classes, average='macro')
recall = recall_score(y_test_classes, y_pred_classes, average='macro')
f1 = f1_score(y_test_classes, y_pred_classes, average='macro')
confusion_mat = confusion_matrix(y_test_classes, y_pred_classes)

print("Accuracy:", accuracy)
print("Precision:", precision)
print("Recall:", recall)
print("F1-score:", f1)
print("Confusion matrix:\n", confusion_mat)

# Step 7: Prepare report
# TODO: Write report summarizing results and network architecture