This paper presents a novel application of the YOLOv8 model for ad- dressing the task of detecting organelles in Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) biological images. The motivation be- hind this research stems from the need for accurate and efficient detection techniques to identify complex microstructures in biological mate- rial samples. The primary objective of this project involves fine-tuning of YOLOv8, a state-of-the-art object detection model, for semantic detection on FIB-SEM images. The study involves retraining the YOLOv8 model to suit the unique characteristics of FIB-SEM data, optimizing its performance for precise delineation of cellular structures. Experimental results showcase the model’s performance in effectively detecting intricate features while maintaining computational efficiency, enabling in-depth analysis and understanding of diverse microscopy samples in scientific re- search and industrial applications.