- Load and preprocess the CIFAR-10 dataset: Prepare the dataset for training by loading the images and corresponding labels. Perform necessary preprocessing steps such as normalization.
- Comprehensive Exploratory Data Analysis (EDA): Document and explore the dataset thoroughly to gain insights into its characteristics, distribution, and potential challenges.
- Data Augmentation: Enhance the training dataset by applying techniques like rotation, flipping, and scaling to increase its diversity and improve model generalization.
- Define Baseline Model Architecture: Construct the baseline model. Design the architecture based on the requirements of the CIFAR-10 dataset.
- Compile the Model: Compile the model with appropriate loss functions, optimizers, and evaluation metrics to prepare it for training.
- Train and Validate: Train the baseline model on the training dataset and validate its performance on the test set.
- Evaluate Baseline Model: Assess the performance of the baseline model on the test dataset. Record key evaluation metrics such as accuracy and loss to gauge its effectiveness.
- Choose Pre-trained Model: Select a pre-trained model suitable for image classification tasks, such as EfficientNet, VGG, ResNet, or MobileNet.
- Modify Top Layers: Replace the top layer(s) of the pre-trained model to match the number of classes in the CIFAR-10 dataset.
- Freeze and Train: Freeze the initial layers of the pre-trained model to retain learned features and train the top layers on the CIFAR-10 dataset.
- Fine-tuning: Gradually unfreeze and fine-tune more layers of the pre-trained model as needed to improve performance.
- Compare Performance: Compare the performance of the transfer learning model with the baseline model. Evaluate metrics such as accuracy, loss, and computational efficiency.
- Optimization: Fine-tune hyperparameters, learning rates, and other training parameters to optimize the transfer learning model further. Experiment with different configurations to achieve the best results.