Evaluating SAM3's Generalization Capabilities: A Head-to-Head Comparison with Fine-Tuned YOLO Detectors

This research provides a valuable contribution to the field of computer vision by comparing the zero-shot capabilities of SAM3 against specialized object detectors. Understanding the trade-offs between generalization and specialization is crucial for designing effective AI systems.

• •The research assesses the performance of a generalized model (SAM3) against specialized object detectors (YOLO).
• •It explores the advantages and disadvantages of zero-shot segmentation compared to fine-tuned detection.
• •The findings provide insights into choosing appropriate models for different computer vision tasks.