Practical Optimization of CIFAR-10 Image Classification: A Comparative Study of CNN Hyperparameter Tuning and EfficientNet Transfer Learning

This article explores a deep learning project that systematically compares the performance of traditional CNN hyperparameter tuning and EfficientNetB0 transfer learning on the CIFAR-10 dataset, analyzing the effects of different optimization strategies to provide practical references for model selection and optimization strategies. Image classification is a fundamental task in computer vision, and CIFAR-10 is widely used as a standard benchmark. The research in this project is of reference value to deep learning practitioners.

CIFAR-10 is released by the Canadian Institute for Advanced Research (CIFAR), containing 60,000 32x32 color images divided into 10 categories: airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks. Among them, 50,000 are used for training and 10,000 for testing. The challenges of this dataset include low image resolution, large variations in object scales, complex backgrounds, and visual similarities between categories (e.g., cats and dogs, deer and horses). It is an ideal benchmark for testing model generalization ability, but requires careful design of model architecture and training strategies.

The first technical route is to train a convolutional neural network from scratch, improving performance through hyperparameter tuning and architecture design. Its advantage is that the model is fully customized for the target task without pre-training bias. Hyperparameter tuning involves learning rate (and decay strategies such as step decay, cosine annealing), batch size, optimizer selection, regularization strength, network depth and width, etc. Architectures can be VGG, ResNet, or custom-designed, requiring a balance between model capacity and generalization ability. Data augmentation techniques like random cropping, horizontal flipping, and color jitter are also important means to improve generalization.

The second technical route uses EfficientNetB0 transfer learning. EfficientNet is an efficient convolutional network proposed by Google, which balances accuracy and computational efficiency through a compound scaling strategy (simultaneously adjusting depth, width, and resolution). B0 is the smallest variant in this series. Transfer learning uses general features pre-trained on ImageNet and adapts to specific tasks through fine-tuning, with advantages of fast convergence, good generalization, and resource saving. Fine-tuning strategies include freezing convolutional layers and only training the classification layer (for small datasets) or end-to-end fine-tuning (for large datasets). Attention should be paid to the resolution matching problem between CIFAR-10 image size (32x32) and ImageNet (224x224).

Comparison: CNNs are flexible and customizable but require large amounts of data and resources, and are prone to overfitting; transfer learning converges quickly but has a fixed architecture, and large differences between the source domain and target domain will affect the effect. Experimental design needs to control variables (random seed, number of training epochs, hardware environment). Evaluation metrics include accuracy, convergence speed, model parameter count, and inference time. Confusion matrix analysis is used to evaluate category performance, and feature maps and attention heatmaps help understand the basis of model decisions.

Practical experience: Transfer learning has higher cost-effectiveness on standard benchmark datasets; hyperparameter tuning is necessary for task optimization and should not rely on default configurations; data preprocessing (normalization, augmentation, class balance) has a significant impact; model ensembling (average of multiple model predictions) can stably improve accuracy.

Limitations: Limited experimental scale (small hyperparameter search space, insufficient repetition times), single evaluation metric (ignoring robustness and fairness), and lack of systematic comparison with other advanced methods. Improvement directions: Introduce architectures such as Vision Transformer and ConvNeXt; try semi-supervised/self-supervised pre-training; validate on more datasets; use AutoML technologies (e.g., neural architecture search) to explore the architecture space.