Self and semi-supervised Learning for vehicle make and model recognition in images

Recent advances in computer vision have demonstrated remarkable success in general object recognition tasks. However, fine-grained visual classification remains challenging due to subtle inter-class variations and the requirement for extensive labeled datasets. While self-supervised learning has shown promise in learning rich representations from unlabeled data, its application to specialized domains like fine-grained recognition has been less explored. This study explores various self-supervised learning architectures, such as a contrastive learning method and a teacher-student framework , namely SimCLR and DINO respectively, to evaluate their effectiveness in fine-grained classification tasks. Specifically, it focuses on the application of self and semisupervised learning techniques to vehicle make and model recognition in image datasets with few labels, providing a comprehensive analysis of their performance for this specialized classification challenge. We propose a multi-stage framework that leverages a backbone pretrained on the ImageNet dataset, followed by self-supervised fine-tuning on the Stanford Cars dataset, finalizing with few-shot classification with a linear classifier, also on Stanford Cars, to evaluate representation quality. Our experiments reveal significant challenges in domain transfer from general to specialized visual domains. We observe catastrophic forgetting when adapting models from broad object recognition to fine-grained automotive features, particularly pronounced with limited data availability. This work highlights critical research directions for improving domain adaptation in self-supervised learning, particularly for fine-grained applications with data constraints.