Object Detection
This project undertakes a comparative analysis between two prominent object detection models - YOLOv5 and Faster R-CNN (implemented via Detectron2). The main aim was to assess and contrast the performance of these models using a tailored dataset containing images of laptops, drinks, and utensils. Evaluation criteria encompassed mean Average Precision (mAP), inference speed, and model size.