Adapting YOLO11 for Classification of Unlabelled Data : A Semi-Supervised Approach
DOI:
https://doi.org/10.32628/IJSRSET25122107Keywords:
YOLO, Classification, Unlabeled Data, Object Detection, Semi-Supervised Learning, Pseudo-Labelling, Deep LearningAbstract
Computer vision has advanced rapidly, improving object detection and classification. The YOLO (You Only Look Once) model family is widely used because of its real-time processing efficiency and object detection accuracy. However, most YOLO-based methods focus on object detection rather than categorization. Deep learning models sometimes require large amounts of labelled data, which can be expensive and time-consuming, especially in specialized sectors. Traditional classification methods require extensive manual dataset annotation and fully supervised learning. When labelled data is scarce, this reliance is a major issue. Semi-supervised learning (SSL) improves model efficacy by using labelled and unlabeled data. SSL methods to reduce labelling burdens have been studied, however SSL with YOLO-based designs has not.
This project uses semi-supervised learning to adapt YOLO11, an advanced deep learning framework, for classification issues. This work aims to improve classification accuracy in environments with little tagged data, reducing manual tagging. This project will test self-training, pseudo-labeling, and consistency regularization in YOLO11-based classification models. This study uses semi-supervised learning to link object detection and categorization in the YOLO framework. The proposed method could be used in autonomous driving, surveillance, and medical imaging, where comprehensive labelling is often impossible. This work will explain YOLO11 classification optimization and validate SSL techniques in deep learning classification challenges. On the Adidas logo dataset, YOLOv11, the latest framework, was used to classify images according to the object box size into large, medium, and small groups, then chose the best group to use it for the next step, which is the object detection, these steps yielded encouraging results, with a MAP train of 0.913 and a 36-minute training time.
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