How to handle overfitting in computer vision models?

Overfitting is a common problem in machine learning, especially in computer vision tasks where models can easily memorize training data instead of learning to generalize from it. Handling overfitting is crucial to ensure that the model performs well on unseen data.

In this article, we are going to explore the techniques and methods to handle overfitting in computer vision models.

How to detect an Overfitting Computer Vision model?

Overfitting can be detected in computer vision models using the following techniques:

1. Monitor Performance Metrics: Record the number of elements accurately classified or the number of misclassified elements on both the training and validation data. The sign and magnitude change in a certain round are overfitting.
2. Use Cross-Validation: Improve the approach further by partitioning the dataset into a combination of folds and then run the model to verify its performance across those folds.
3. Visualize Learning Curves: Prepare and plot the training and validation curves to show loss/accuracy estimated by model over the epoch. When remote points of the curves are far from each other, this is a sign of overfitting.
4. Evaluate with a Hold-Out Test Set: To evaluate the generality of the sources, validate the model on a test dataset that was not involved in the training.

Techniques to Prevent Overfitting in Computer Vision Models

When designing the Computer Vision models, it is necessary to integrate the solutions that can limit overfitting. Here are some effective techniques:

1. Data Augmentation

Image augmentation can be defined as the procedure of generating new training data from already processed data. This is done through various techniques such as:

• Flipping the Image: Image rotation – rotating the image 90, 180, or 270 degrees simply and flipping the image horizontally or vertically to create mirror images.
• Rotating the Image: Rotating images by various degrees (e.g., 90°, 180°) to add variation.
• Cropping: Selecting small portions of an image and centralizing a section of a picture or an image onto another.
• Scaling: Multiple scales: reducing the size of the image.
• Color Jittering: Changing the brightness, contrast, and saturation of images.
• Adding Noise: adding random noise to images so that the model is relevant to variations.

Advanced techniques like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) can also be used for data augmentation. GANs can generate realistic new images by learning the distribution of the training data, while VAEs can create new examples by learning latent representations of the data.

In Keras framework, we can also use ImageDataGenerator:

from keras.preprocessing.image import ImageDataGenerator

datagen = ImageDataGenerator(
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True
)

2. Early Stopping

Early stopping implies that after reaching a certain number of epochs or iteration steps, the learning process should be stopped and proceeded only after performance on the validation set begins to decline. This helps in avoiding complications of over fit on the model which would yield high accuracy but would not function optimally when called to do so on data that it has not been trained on.

To implement early stopping:

• Track the validation loss: When training a model, use the indices of the validation dataset to track the loss in real-time.
• Set patience: Specify the patience parameter which determines the number of epochs that the program has to wait before stopping even if the validation loss is not improving anymore.
• Stop training: Stop the training process when the validation loss stops decreasing in a defined number of epochs.

Example in Keras:

from keras.callbacks import EarlyStopping

early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)

model.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=50, callbacks=[early_stopping])

3. Dropout

Dropout is an optimization method that helps to reduce the overfitting problem as it restricts the combined effect on training data. At each training iteration, dropout chooses a certain proportion of the input units to schedule them to be zero. This prevents the learning of overlapping representations in the network; thus improving its capability to overlearn.

To implement dropout:

• Select a dropout rate: Typically between 0.2 and 0.5.
• Apply dropout layers: Propose complementing the architecture of the neural network with the dropout layers after the fully connected ones.

Example of Dropout in Keras:

from keras.layers import Dropout

model.add(Dense(512, activation='relu'))
model.add(Dropout(0.5))  # Dropout layer with 50% dropout rate

4. Regularization

Regularization techniques add a penalty to the loss function to constrain the model’s complexity, encouraging simpler models that generalize better.

1. L1 Regularization: An additional component that contributes the absolute value of weights to the loss function ensures influences the model parameters towards high sparsity.
2. L2 Regularization: This increases the squared value of weights in the loss function which reduces large weights.

To implement regularization:

• Add regularization terms: Add L1 or L2 penalties in the loss function to prevent the model from over fitting.
• Adjust regularization strength: Using such a function, tune the regularization coefficient to avoid a problem of either underfitting or overfitting.

5. Cross-Validation

It involves reusing resampling techniques to validate different scenarios, thereby enhancing the generalization capacity of the model. It involves training the model using the fold and testing it in the other part of the same fold, where the data has been divided into multiple folds.

To perform cross-validation:

• Split the data: Split the collected data set into k groups generally k=5 or k=10.
• Train and validate: Use k-1 folds for training the model and then test the model on the left out fold, k. This should be done k times to obtain k different models, each time using a different fold as the validation set.
• Aggregate results: Check the overall performance statistics for all folds and consider this information to have a more accurate idea of the model’s performance in unseen data.

Example :

from sklearn.model_selection import KFold

kf = KFold(n_splits=5)
for train_index, val_index in kf.split(x_data):
    x_train, x_val = x_data[train_index], x_data[val_index]
    y_train, y_val = y_data[train_index], y_data[val_index]
    model.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=50)

6. Transfer Learning

Transfer learning is a technique that uses pre-trained models on large data set to get the best results on a target task with limited data. The method entails repurposing a trained model that may have been trained on other datasets to perform a particular task.

To implement transfer learning:

• Select a pre-trained model: Pick a model that has been trained on a huge database for example Imagenet.
• Replace final layers: The final layers of the pre-trained model are replaced with new layers learned specifically for the particular task in the target domain.
• Fine-tune the model: Fine tune the modified model on target data set, tweaking the weights a little from the pre-trained model.

Example in Keras:

from keras.applications import VGG16

base_model = VGG16(weights='imagenet', include_top=False, input_shape=(150, 150, 3))
model = Sequential([
    base_model,
    Flatten(),
    Dense(256, activation='relu'),
    Dropout(0.5),
    Dense(10, activation='softmax')
])

7. Increase Training Data

Increasing the size of the training dataset helps the model learn more diverse patterns and reduces the likelihood of overfitting. Collecting more labeled data or using techniques like web scraping can be beneficial.

8. Hyperparameter Tuning

Carefully tuning hyperparameters such as learning rate, batch size, number of epochs, and regularization parameters can help in preventing overfitting. Techniques like grid search or random search can be used to find the optimal set of hyperparameters.

Example:

from sklearn.model_selection import GridSearchCV
from keras.wrappers.scikit_learn import KerasClassifier

def create_model(optimizer='adam'):
    model = Sequential([
        Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),
        MaxPooling2D((2, 2)),
        Flatten(),
        Dense(128, activation='relu'),
        Dropout(0.5),
        Dense(10, activation='softmax')
    ])
    model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
    return model

model = KerasClassifier(build_fn=create_model)
param_grid = {'batch_size': [32, 64], 'epochs': [10, 20], 'optimizer': ['adam', 'rmsprop']}
grid = GridSearchCV(estimator=model, param_grid=param_grid)
grid_result = grid.fit(x_train, y_train)

Conclusion

Handling overfitting in computer vision models is essential to ensure that the models perform well on new, unseen data. Techniques such as data augmentation, regularization, reducing model complexity, early stopping, cross-validation, transfer learning, increasing training data, and hyperparameter tuning are effective strategies to address overfitting. By applying these techniques, you can build robust models that generalize well and achieve better performance in real-world applications.

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