Deep Learning with CNNs: Classifying Images of the CIFAR-10 Dataset

A case study on developing a custom CNN and leveraging transfer learning with VGG16 for image classification.

This article explores a project focused on image classification using Convolutional Neural Networks (CNNs) on the CIFAR-10 dataset. The project involved developing a custom CNN model and employing transfer learning with the VGG16 architecture to achieve improved performance.

Motivation and Dataset

The initial motivation behind the project was to address a real-world application: developing smart glasses for elderly drivers to enhance navigation by recognizing objects in real-time. This required accurate image classification, which led the team to explore deep learning techniques, specifically CNNs.

The CIFAR-10 dataset was chosen over alternatives like Animals-10 due to its diversity. CIFAR-10 contains 10 object categories (airplanes, cars, animals, etc.) that are more representative of everyday environments. This balanced dataset, with 6,000 images per class, provides a good starting point for training and testing a CNN model.

A grid of sample images from the CIFAR-10 dataset, showing various objects like cars, birds, and ships.

Custom CNN Model

The team developed a custom CNN architecture consisting of three convolutional blocks. Each block included two Conv2D layers, Batch Normalization, and Dropout. The number of filters increased from 32 in the first block to 64 in the second and 128 in the third. A dense layer with 128 units and a final softmax layer were used for classification.

Key design decisions included:

  • Batch Normalization: To stabilize learning and allow for higher learning rates.
  • Dropout: To prevent overfitting by randomly turning off neurons during training.
  • Adam Optimizer: For efficient weight adjustment during training.
  • Regularization (L2): To penalize large weights and improve generalization.
  • Data Augmentation: Techniques like flipping, rotating, zooming, and translating images were used to increase dataset variability.
A diagram illustrating the architecture of the custom Convolutional Neural Network (CNN) model.

Transfer Learning with VGG16

To further improve performance, transfer learning with the VGG16 model, pre-trained on the ImageNet dataset, was implemented. VGG16's ability to capture low- and mid-level features was leveraged to avoid training from scratch.

The transfer learning strategy involved:

  • Freezing early layers: To retain general patterns learned from ImageNet.
  • Unfreezing later layers: To fine-tune and adapt to the specific patterns of CIFAR-10.
  • Adding fully connected layers: With Batch Normalization and Dropout for regularization.
  • Data Augmentation: To improve generalization.
  • Learning Rate Scheduling and Early Stopping: To optimize training and prevent overfitting.

Evaluation and Results

The performance of the custom CNN and the transfer learning approach was evaluated using metrics such as validation loss, accuracy, precision, recall, and F1-score. Confusion matrices were also used for a detailed comparison.

Key observations:

  • The transfer learning model outperformed the custom CNN, achieving higher accuracy and lower validation loss.
  • Fine-tuning the VGG16 model resulted in further improvements in accuracy, precision, recall, and F1-score.
  • Transfer learning provided the most significant boost in performance compared to the custom CNN.
Confusion matrices comparing the performance of the custom CNN and the VGG16 transfer learning model.

Optimization Techniques

Several optimization techniques were employed:

  • Adam Optimizer: For efficient weight updates.
  • Regularization (Dropout and L2): To prevent overfitting.
  • Learning Rate Scheduling (ReduceLROnPlateau): To adjust the learning rate during training.
  • Early Stopping: To halt training when validation loss stopped improving.
  • Batch Normalization: To stabilize training and improve convergence.

Future Work

Potential improvements and future work include:

  • Unfreezing more layers for further fine-tuning of the VGG16 model.
  • Using more aggressive data augmentation techniques.
  • Experimenting with different learning rate schedulers.
  • Trying other pre-trained models like ResNet or EfficientNet.
  • Exploring ensemble models.

Conclusion

This project demonstrated the effectiveness of CNNs for image classification on the CIFAR-10 dataset. Transfer learning with VGG16 significantly improved performance compared to a custom CNN. The combination of various optimization techniques and data augmentation played a crucial role in achieving high accuracy and preventing overfitting. The insights gained from this project can be applied to other real-world applications such as autonomous vehicles and CAPTCHA generation.

Adeteju Enunwa is an Engineering Program Lead who leverages emerging technologies to architect human-centric solutions and products, all built on a foundation of trust and responsible development.