A Comparative Study of CNN and Vision Transformer for Fruit Image Classification

This study is a systematic comparative project of deep learning models, focusing on fruit image classification tasks to evaluate the performance differences between traditional Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), covering key techniques such as data augmentation, transfer learning, and model fine-tuning. It aims to answer core questions including whether ViTs can outperform CNNs on small and medium-sized datasets, the differential impacts of data augmentation and fine-tuning on different architectures, the trade-offs between training efficiency, inference speed, and final accuracy of the two models, and the application effect of transfer learning on ViTs, providing empirical evidence for model selection.

In the field of computer vision, CNNs have long dominated, while the emergence of ViTs has brought new possibilities for image classification. There are fundamental differences between the two in terms of inductive bias, feature extraction methods, and computational efficiency. This project systematically compares the actual performance of CNNs and ViTs through the fruit image classification scenario, providing empirical evidence for model selection.

The project attempts to answer the following key questions:

1. Can pre-trained ViTs outperform traditional CNNs on small and medium-sized datasets?
2. Are there differences in the impacts of data augmentation and fine-tuning strategies on different architectures?
3. What are the trade-offs between training efficiency, inference speed, and final accuracy of the two models?
4. How effective is transfer learning applied to ViTs?

CNN

CNNs use local receptive fields and weight sharing mechanisms, leveraging the spatial local correlation of images to effectively capture local features such as edges, textures, and colors, which is suitable for fruit classification. The project may adopt pre-trained architectures like ResNet and EfficientNet.

ViT

ViTs divide images into fixed-size patches, input them as sequences into the Transformer encoder, and model global dependencies. However, they usually require larger datasets for training, so pre-trained weights and fine-tuning strategies are used.

Data Augmentation Strategies

• Geometric transformations: Random cropping, horizontal flipping, rotation, etc., to simulate different shooting angles
• Color jitter: Adjust brightness, contrast, saturation to adapt to different lighting conditions
• Normalization: Standardize inputs to accelerate convergence

Transfer Learning and Fine-tuning

1. Load pre-trained weights from ImageNet or other sources for initialization
2. Freeze underlying parameters and only train the classification head (feature extraction)
3. Unfreeze all parameters and perform end-to-end fine-tuning with a small learning rate The quality of ViT's pre-trained weights is particularly critical.

The project establishes a comprehensive evaluation framework:

• Confusion matrix analysis: Identify easily confused fruit categories
• Prediction visualization: Display attention areas/activation maps to explain decisions
• Training process monitoring: Record loss curves and learning rate changes
• Automatic model saving: Save the optimal model based on the validation set to prevent overfitting Multi-dimensional evaluation helps understand the behavioral differences between architectures.

CNN Advantages: Low data requirements, stable performance on small and medium datasets, translation invariance, high training and inference efficiency ViT Advantages: Strong global modeling ability, powerful feature representation after large-scale pre-training, unified and scalable architecture Practical Considerations: Dataset size (limited fruit datasets pose challenges for ViTs), computing resources (ViTs require more memory and time), deployment scenarios (choose CNNs for edge devices)

Practical Value: Guidance for model selection, best practices for transfer learning, experimental design templates, visualization and interpretability.

This project is a rigorously designed comparative study of deep learning, focusing on the fair comparison of two mainstream architectures in fruit classification tasks. It does not pursue complex model stacking, and provides valuable references for learners who want to understand the differences between CNNs and Transformers, as well as developers who need to select models for practical projects. The simplicity of fruit classification makes the impact of architectural differences more clearly distinguishable.