Understanding Object Detection The primary task of object detection is to locate and classify objects within images. Current mainstream object detection algorithms can be categorized into two approaches: Two-stage methods: Such as the RCNN family, which generate region proposals and then classify an...
For object detection tasks in PyTorch using YOLOv8, the Ultralytics library provides a streamlined approach. Begin by ensuring the environment supports GPU acceleration if available. import torch from ultralytics import YOLO import os os.environ['KMP_DUPLICATE_LIB_OK'] = 'True' def setup_model(): de...
RPN Layer Overview The Region Proposal Network (RPN) generates candidate bounding boxes (proposals) from feature maps extracted by the backbone network. This section breaks down core RPN componentts including anchor generation, proposal prediction, and filtering. Anchor Generation Module import torc...
mAP (mean Average Precision) is a standard metric for evaluating object detectors. It summarizes precision–recall tradeoffs across cateegories, optionally averaged over multiple IoU thresholds. Precision = TP / (TP + FP) Recall = TP / (TP + FN) AP (per class) = area under the precision–recall curve...