Eran Feit

Eran Feit is a Computer Vision Engineer, AI Researcher, and Deep Learning Educator with over a decade of hands-on experience in the tech industry. Specializing in object detection, edge AI deployment, and advanced neural network architectures, Eran bridges the gap between complex AI theory and practical implementation. He is dedicated to empowering the global developer community through comprehensive, code-driven tutorials and guides.

Fast YOLOv8 Dog Segmentation Tutorial for Video & Images

Image Segmentation /
YOLOv8 Dog Segmentation

Understanding YOLOv8 Segmentation for Real Projects YOLOv8 has quickly become one of the most powerful tools for real-time object detection and segmentation, combining speed, accuracy, and a clean developer experience into one flexible framework. With its segmentation capabilities, you can move beyond simple bounding boxes and generate precise masks that separate objects from their background

Fast YOLOv8 Dog Segmentation Tutorial for Video & Images Read More »

YOLOv8 Multi-Class Segmentation Tutorial for Football Analytics

Image Segmentation /
YOLOv8 Segmentation Tutorial for Multi-Class Football

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

YOLOv8 Multi-Class Segmentation Tutorial for Football Analytics Read More »

YOLOv8 Segmentation Tutorial for Real Flood Detection

Image Segmentation /
flood segmentation

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

YOLOv8 Segmentation Tutorial for Real Flood Detection Read More »

YOLOv8 + SAM in Python: Fast, Clean Segmentation Masks

Image Segmentation, Pytorch /
Build Custom Image Segmentation Model Using YOLOv8 and SAM

Getting started with Segment Anything (SAM) YOLOv8 SAM segmentation Python is a simple “detect then segment” workflow: YOLOv8 finds the object, and Segment Anything (SAM) turns that box into a clean pixel-accurate mask. In this tutorial, you’ll run the full pipeline in Python, visualize the masks, and learn the small details that keep results aligned

YOLOv8 + SAM in Python: Fast, Clean Segmentation Masks Read More »

One-Click Segment Anything in Python (SAM ViT-H)

Image Segmentation, Pytorch /
Segment Anything with One mouse click

Segment Anything in Python — Fast, One-Click Results Segment Anything in Python lets you segment any object with a single click using SAM ViT-H, delivering three high-quality masks instantly.In this tutorial, you’ll set up the environment, load the checkpoint, click a point, and export overlays—clean, practical code included.Whether you’re labeling datasets or prototyping, this one-click

One-Click Segment Anything in Python (SAM ViT-H) Read More »

Segment Anything Python — No-Training Image Masks

Image Segmentation, Pytorch, VIT /
Segment Anything Python

Why Segment Anything (SAM) is a Game-Changer for Python Developers Generating high-quality training data is often the biggest bottleneck in computer vision. In this Segment Anything Python tutorial, you will solve the problem of manual image labeling by leveraging Meta’s SAM model to produce pixel-perfect masks instantly. Instead of spending weeks annotating datasets or training

Segment Anything Python — No-Training Image Masks Read More »

Segment Anything Tutorial: Fast Auto Masks in Python

Image Segmentation, Pytorch, VIT /
Automated Mask Generation using Segment Anything

Getting comfortable with the plan This guide focuses on automatic mask generation using Segment Anything with the ViT-H checkpoint.You’ll start by preparing a reliable Python environment that supports CUDA (if available) for GPU acceleration.Then you’ll load the SAM model, configure the automatic mask generator, and select an image for inference.Finally, you’ll visualize the annotated results,

Segment Anything Tutorial: Fast Auto Masks in Python Read More »

Detectron2 custom dataset Training Made Easy

Image Segmentation /
etectron2 custom dataset

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

Detectron2 custom dataset Training Made Easy Read More »

Detectron2 Panoptic Segmentation Made Easy for Beginners

Image Segmentation /
Panoptic Segmentation

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),

Detectron2 Panoptic Segmentation Made Easy for Beginners Read More »

Eran Feit