In the field of modern computer vision, the standard practice for solving new problems is not to train a model from scratch but to adapt a massive, pre-trained model. This process, known as fine-tuning, is a cornerstone of deep learning. This paper investigates two fundamentally different philosophies for fine-tuning. The first is the traditional, widely used approach where the core of a pre-trained Convolutional Neural Network (CNN) is kept frozen, and only a small, new classification layer is trained. This method is fast and computationally cheap. The second is a more modern, parameter-efficient fine-tuning (PEFT) technique called Low-Rank Adaptation (LoRA), which allows for a more profound adaptation of a model’s internal workings without the immense cost of full retraining. To test these competing methods, we designed a benchmark using three powerful, pre-trained models. For the traditional approach, we used ResNet50 and EfficientNet-B0, two highly influential CNNs. For the modern approach, we used a Vision Transformer (ViT), an architecture that processes images using a self-attention mechanism, and adapted it with LoRA. We then evaluated these models on three datasets of increasing complexity: the simple MNIST (handwritten digits), the moderately complex Fashion-MNIST (clothing items), and the significantly more challenging CIFAR-10 (color photos of objects like cars, dogs, and ships). This ladder of complexity was designed to reveal under which conditions each fine-tuning strategy excels or fails. The outcomes were striking on the intricate CIFAR-10 dataset: the ViT-LoRA model performed exceptionally well, achieving 97.3% validation accuracy, while the ResNet50 and EfficientNet-B0 models only managed 83.4% and 80.5%, respectively. The task was not difficult enough to distinguish between the models on the much easier MNIST dataset, though, and all of them received nearly flawless scores of 99%. Critically, the superior performance of the ViT-LoRA model was achieved with incredible efficiency. The LoRA method required training only 0.58% of the total model parameters, a tiny fraction of the 8-11% of parameters that needed to be trained for the traditional CNN approach. This leads to the central conclusion of our work: LoRA is not just a more efficient method, but a more effective one. For complex, real-world tasks, the ability to adapt a model’s internal representations, as LoRA does for the ViT’s attention layers, provides a decisive performance advantage that the rigid, classifier-only fine-tuning method cannot match.