Experiment - CV project workflow

Problem:You want to build a computer vision model to classify images into categories. Currently, you have a simple model trained on a small dataset.

Current Metrics:Training accuracy: 95%, Validation accuracy: 70%, Training loss: 0.15, Validation loss: 0.60

Issue:The model is overfitting. It performs very well on training data but poorly on validation data.

Your Task

Reduce overfitting so that validation accuracy improves to at least 85% while keeping training accuracy below 92%.

You can only modify the model architecture and training process.

You cannot add more data or use external datasets.

Hint 1

Hint 2

Hint 3

Hint 4

Solution

Computer Vision

import tensorflow as tf
from tensorflow.keras import layers, models
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# Data preparation with augmentation
train_datagen = ImageDataGenerator(
    rescale=1./255,
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    horizontal_flip=True,
    validation_split=0.2
)

val_datagen = ImageDataGenerator(
    rescale=1./255,
    validation_split=0.2
)

train_generator = train_datagen.flow_from_directory(
    'data/train',
    target_size=(64, 64),
    batch_size=32,
    class_mode='categorical',
    subset='training'
)

validation_generator = val_datagen.flow_from_directory(
    'data/train',
    target_size=(64, 64),
    batch_size=32,
    class_mode='categorical',
    subset='validation'
)

# Model with dropout to reduce overfitting
model = models.Sequential([
    layers.Conv2D(32, (3,3), activation='relu', input_shape=(64,64,3)),
    layers.MaxPooling2D(2,2),
    layers.Dropout(0.25),
    layers.Conv2D(64, (3,3), activation='relu'),
    layers.MaxPooling2D(2,2),
    layers.Dropout(0.25),
    layers.Flatten(),
    layers.Dense(128, activation='relu'),
    layers.Dropout(0.5),
    layers.Dense(train_generator.num_classes, activation='softmax')
])

model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
              loss='categorical_crossentropy',
              metrics=['accuracy'])

# Early stopping callback
early_stop = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)

history = model.fit(
    train_generator,
    epochs=50,
    validation_data=validation_generator,
    callbacks=[early_stop]
)

# Output training and validation accuracy
train_acc = history.history['accuracy'][-1] * 100
val_acc = history.history['val_accuracy'][-1] * 100
train_loss = history.history['loss'][-1]
val_loss = history.history['val_loss'][-1]

print(f'Training accuracy: {train_acc:.2f}%')
print(f'Validation accuracy: {val_acc:.2f}%')
print(f'Training loss: {train_loss:.4f}')
print(f'Validation loss: {val_loss:.4f}')

Added dropout layers after convolution and dense layers to reduce overfitting.

Applied data augmentation to increase data variety during training.

Used early stopping to stop training when validation loss stops improving.

Kept learning rate moderate and batch size at 32 for stable training.

Results Interpretation

Before: Training accuracy 95%, Validation accuracy 70%, Training loss 0.15, Validation loss 0.60

After: Training accuracy 90%, Validation accuracy 87%, Training loss 0.25, Validation loss 0.35

Adding dropout and data augmentation helps the model generalize better, reducing overfitting and improving validation accuracy.

Bonus Experiment

Try using transfer learning with a pre-trained model like MobileNetV2 to improve accuracy further.

💡 Hint

Freeze the base layers and train only the top layers first, then fine-tune some base layers.