Eran Feit Blog posts

Traditional object detection often fails in sports analytics because bounding boxes overlap in crowded scenes, making it impossible to calculate precise player distances or pitch coverage. To solve this, we must move to pixel-level understanding. In this YOLOv8 Multi-Class Segmentation Tutorial for Football Analytics, you will learn how to build a model that doesn’t just

How YOLOv8 Flood Segmentation Helps You Map Real Floods In this YOLOv8 instance segmentation tutorial, we will explore how to leverage cutting-edge computer vision to detect and monitor real-world flood events. Flood detection is a critical task for environmental organizations and emergency services. By using YOLOv8 segmentation, we move beyond simple bounding boxes to precise

Understanding YOLOv5 in a Practical Way YOLOv5 is one of the most popular deep learning frameworks for real-time object detection, and for good reason.It’s fast, lightweight, and flexible, making it a great fit for everything from quick experiments on your laptop to full production pipelines running on GPUs or edge devices. This Yolov5 segmentation tutorial

Detectron2 custom dataset training means taking your own images (not COCO), labeling them with polygon masks, registering them in Detectron2, and fine-tuning Mask R-CNN so it can detect and segment your specific objects.In this tutorial, we’ll walk through that full process using a fruit dataset (apples, bananas, grapes, strawberries, oranges, lemons): annotation, COCO export, dataset

Understanding a visual scene requires more than just drawing boxes around cars; it requires identifying every pixel, from the individual vehicles to the road and sky. In this Detectron2 Panoptic Segmentation Python Tutorial, you will solve the complex problem of ‘complete scene understanding.’ While instance segmentation tracks objects (things) and semantic segmentation labels regions (stuff),

Introduction – Detectron2—what it is and why it’s useful Detectron2 is Facebook AI Research’s modern computer-vision framework built on PyTorch.It focuses on object detection, instance segmentation, semantic segmentation, panoptic segmentation, and keypoint detection.Think of it as a toolkit of proven research models plus a clean training and inference engine.You get state-of-the-art architectures, strong defaults, and