Computer Vision Fundamentals - Lesson 6: Image Classification and Transfer Learning

Overview:

This lesson covers image classification and transfer learning, two essential techniques for training models to categorize images and leverage pre-trained models to solve specific tasks. Students will learn about various classification methods and how to use transfer learning to enhance model performance.

Objectives:

By the end of this lesson, students should be able to:

• Understand the principles of image classification.
• Implement basic image classification models using deep learning.
• Apply transfer learning to leverage pre-trained models for classification tasks.

Topics Covered:

1. Introduction to Image Classification:

   • Definition and significance of image classification.
   • Common applications (e.g., facial recognition, scene classification).

2. Basic Image Classification with Deep Learning:

   • Overview of Convolutional Neural Networks (CNNs).
   • Basic architecture of CNNs: Convolutional layers, pooling layers, and fully connected layers.
   • Implementing a simple CNN for image classification.

   Code Example:

   import tensorflow as tf
   from tensorflow.keras.models import Sequential
   from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
   from tensorflow.keras.datasets import cifar10
   from tensorflow.keras.utils import to_categorical
   
   # Load and preprocess CIFAR-10 dataset
   (x_train, y_train), (x_test, y_test) = cifar10.load_data()
   x_train, x_test = x_train / 255.0, x_test / 255.0
   y_train, y_test = to_categorical(y_train), to_categorical(y_test)
   
   # Define a simple CNN model
   model = Sequential([
       Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
       MaxPooling2D((2, 2)),
       Conv2D(64, (3, 3), activation='relu'),
       MaxPooling2D((2, 2)),
       Conv2D(64, (3, 3), activation='relu'),
       Flatten(),
       Dense(64, activation='relu'),
       Dense(10, activation='softmax')
   ])
   
   # Compile and train the model
   model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
   model.fit(x_train, y_train, epochs=5, validation_data=(x_test, y_test))
   
   # Evaluate the model
   test_loss, test_acc = model.evaluate(x_test, y_test)
   print(f'Test accuracy: {test_acc}')
   

3. Transfer Learning:

   • Introduction to transfer learning and its benefits.
   • Understanding pre-trained models (e.g., VGG16, ResNet, Inception).
   • Using pre-trained models for new classification tasks.

   Code Example:

   from tensorflow.keras.applications import VGG16
   from tensorflow.keras.models import Model
   from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
   from tensorflow.keras.preprocessing.image import ImageDataGenerator
   from tensorflow.keras.optimizers import Adam
   
   # Load VGG16 model with pre-trained weights
   base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
   
   # Add custom classification layers
   x = base_model.output
   x = GlobalAveragePooling2D()(x)
   x = Dense(1024, activation='relu')(x)
   predictions = Dense(10, activation='softmax')(x)
   
   # Create the model
   model = Model(inputs=base_model.input, outputs=predictions)
   
   # Freeze the base model layers
   for layer in base_model.layers:
       layer.trainable = False
   
   # Compile the model
   model.compile(optimizer=Adam(), loss='categorical_crossentropy', metrics=['accuracy'])
   
   # Load and preprocess data
   train_datagen = ImageDataGenerator(rescale=1./255, horizontal_flip=True, rotation_range=20)
   train_generator = train_datagen.flow_from_directory('path/to/train_data', target_size=(224, 224),
                                                       batch_size=32, class_mode='categorical')
   
   # Train the model
   model.fit(train_generator, epochs=5)
   
   # Evaluate the model (use a validation generator or data)
   

4. Fine-Tuning with Transfer Learning:

   • Unfreezing some layers of the pre-trained model and fine-tuning.
   • Techniques to improve model performance with transfer learning.

   Code Example:

   # Unfreeze the last few layers of the base model
   for layer in base_model.layers[-4:]:
       layer.trainable = True
   
   # Recompile the model
   model.compile(optimizer=Adam(lr=1e-5), loss='categorical_crossentropy', metrics=['accuracy'])
   
   # Retrain the model with the fine-tuned layers
   model.fit(train_generator, epochs=5)
   

5. Evaluating Classification Models:

   • Metrics: Accuracy, precision, recall, F1 score.
   • Visualizing model performance: Confusion matrix, ROC curve.

6. Challenges in Image Classification:

   • Handling imbalanced datasets.
   • Dealing with variations in image quality and content.

Activities and Quizzes:

• Activity: Implement and train an image classification model using a dataset of your choice. Experiment with both custom CNN and transfer learning approaches.
• Quiz: Multiple-choice questions on image classification techniques, transfer learning concepts, and evaluation metrics.

Assignments:

• Assignment 6: Develop a classification model using transfer learning to classify a custom dataset (e.g., animal species, plant types). Document the process, including data preparation, model training, and evaluation, and submit a report with your findings.

This lesson provides students with practical skills in image classification and transfer learning, preparing them for more advanced topics and real-world applications in computer vision.