The study delves into the underexplored problem of foreground-foreground class imbalance in object detection. It introduces the COCO-ZIPF dataset, crafted to reflect real-world scenarios with limited object classes. The research evaluates sampling, loss reweighing, and augmentation methods using the YOLOv5 model. Results show that data augmentation techniques like mosaic and mixup significantly enhance model performance compared to sampling and loss reweighing methods. The study emphasizes the importance of addressing class imbalance for accurate object detection in practical applications.
The methodology involved constructing a specialized dataset, implementing various strategies within the YOLOv5 framework, and analyzing performance metrics like mean Average Precision (mAP). The training setup included details on dataset construction, model architecture, and training parameters. Results indicated that augmentation techniques played a crucial role in improving model accuracy across different classes.
The implementation details highlighted the use of PyTorch Lightning for model development and Hydra for configuration management. The framework aimed at simplifying complex network training while ensuring reproducibility and scalability for object detection research.
In conclusion, the study underscores the significance of addressing foreground-foreground class imbalance in object detection using advanced techniques like data augmentation within the YOLOv5 framework.
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by Nieves Crast... a las arxiv.org 03-13-2024
https://arxiv.org/pdf/2403.07113.pdfConsultas más profundas