Search Results for: background estimation – Page 6

YOLOR – Paper Explanation & Inference – An In-Depth Analysis

YOLOR, inspired by how humans combine knowledge, is an object detection model that pushes the boundaries of real-time detection with improved speed & accuracy.

Computer Vision, Deep Learning, Object Detection, Tutorial, YOLO

FCOS- Anchor Free Object Detection Explained

FCOS: Fully Convolutional One-stage Object Detection is an anchor-free (anchorless) object detector. Inference on image and video with PyTorch.

Anchor-free, CenterNet, CNN, Computer Vision, FCOS, Object Detection, PyTorch

CenterNet: Objects as Points – Anchor Free Object Detection Explained

CenterNet: Object as Points is one of the milestones in the anchor-free (anchorless) object detection algorithm. Anchor-free object detection is more generalizable in other computer vision tasks, e.g., pose estimation,

Beginners, CenterNet, Object Detection, Pose Estimation, Tensorflow

YOLOv6 Object Detection – Paper Explanation and Inference

In this blog post we review the YOLOv6 paper, carry out inference using the YOLOv6 models, and also compare YOLOv6 with YOLOv5.

Computer Vision, Deep Learning, Object Detection, Paper Overview, YOLO

YOLOX Object Detector Paper Explanation and Custom Training

YOLOX object detector is a recent addition in the YOLO family. Read the article for detailed YOLOX paper explanation and learn how to train YOLOX on a custom dataset.

CNN, Paper Overview, YOLO

Driver Drowsiness Detection Using Mediapipe In Python

Driver drowsiness detection systems help reduce mishaps due to tired or sleepy drivers. Learn to build such a robust system using MediaPipe in Python.

Computer Vision, Deep Learning, Face Application, Face Detection, MediaPipe, Streamlit