Object Detection

An anchor box is a predefined bounding box used in object detection models to help identify and locate objects in images.

CenterNet is an object detection framework that detects objects as points, simplifying the detection process.

CornerNet is a deep learning model for object detection that predicts corners of objects to identify their bounding boxes.

Faster R-CNN is a deep learning model for object detection that combines region proposal and classification in a single framework.

Feature Pyramid Network (FPN) enhances object detection by using multi-scale feature maps for better recognition.

Intersection over Union (IoU) measures the overlap between two bounding boxes in object detection.

IoU Loss measures the overlap between predicted and actual bounding boxes in object detection tasks.

Mask R-CNN is a deep learning model for object detection and segmentation in images.

RetinaNet is a deep learning model designed for object detection, balancing speed and accuracy using a novel loss function.

RoI Align is a technique used in computer vision to improve object detection accuracy by precisely aligning regions of interest.

RoI Pooling is a technique used in computer vision to extract features from specific regions in an image.

An SSD Detector is a type of computer vision model used for object detection in images and videos.

YOLO (You Only Look Once) is a real-time object detection system that identifies multiple objects in images and videos.

YOLOv5 is an advanced, real-time object detection model known for its speed and accuracy.

YOLOv8 is the latest version of the YOLO (You Only Look Once) model for real-time object detection and recognition.