From scratch or pretrained? An in-depth analysis of deep learning approaches with limited data

The widespread adoption of Convolutional Neural Networks (CNNs) in image recognition has undeniably marked a significant breakthrough. However, these networks need a lot of data to learn well, which can be challenging. This can make models prone to overfitting, where they perform well on training data but not on new data. Various strategies have emerged to address this issue, including reasonably selecting an appropriate network architecture. This study delves into mitigating data scarcity by undertaking a comparative analysis of two distinct methods: utilizing compact CNN architectures and applying transfer learning with pre-trained models. Our investigation extends across three disparate datasets, each hailing from a different domain. Remarkably, our findings unveil nuances in performance. The study reveals that using a complex pre-trained model like ResNet50 yields better results for the flower and Maize disease identification datasets, emphasizing the advantages of leveraging prior knowledge for specific data types. Conversely, starting from a simpler CNN architecture trained from scratch is the superior strategy with the Pneumonia dataset, highlighting the need to adapt the approach based on the specific dataset and domain.