Eran Feit

Detectron2 Panoptic Segmentation Made Easy for Beginners

Why panoptic segmentation matters

Build a Poker Hand Scanner: Card Recognition API Guide

Integrating a dedicated card recognition api into your workflow empowers software teams to inject production-ready computer vision into…

YOLO Image Segmentation for Crack Detection Projects

YOLO image segmentation is a practical way to move from “where is the object” to “which exact pixels belong to it.” Instead of stopping at…

Detectron2 custom dataset Training Made Easy

Detectron2 custom dataset training means taking your own images (not COCO), labeling them with polygon masks, registering them in…

Here’s What Combining YOLOv11 with SAM2 Taught Me About Segmentation

Building a practical teeth segmentation pipeline with YOLOv11 + SAM2

Automatic Image Annotation with Autodistill and YOLOv8

Instead of a human drawing every bounding box and typing every label, models learn to recognize patterns and automatically assign classes…

AI-Powered Neurology: Building an Alzheimer’s Classifier with Python and Xception

Leveraging the Xception Architecture for Early Dementia Detection

How to Fine-Tune YOLO-World for Vision Tasks | Underwater trash

The evolution of computer vision has reached a fascinating intersection where “open-vocabulary” models allow us to detect almost anything…

Efficiency vs. Precision: A Python Deep Dive into Faster R-CNN and SSD PyTorch

In the rapidly evolving landscape of artificial intelligence, selecting the optimal architecture for computer vision is rarely a simple…

Rethinking Computer Vision: Designing Custom Video Interfaces with YOLO11 and OpenCV

The world of computer vision moves incredibly fast, and the arrival of YOLO11 has raised the bar for real-time object detection efficiency…

How to UNet Image Segmentation TensorFlow on Custom Data | Dolphin Segmentation

U-Net image segmentation in TensorFlow is a go-to approach when you need pixel-level predictions, not just a single label per image…

Interactive SAM2 Segmentation: Points, Boxes, and Masks

SAM2 Tutorial is quickly becoming one of the most practical ways for Python developers to get high-quality segmentation without training a…

Brain Tumor Segmentation with YOLOv11 in Python: Train, Predict, and Export Masks

Brain Tumor Segmentation with YOLOv11 in Python: What You’ll Build

Segment Anything tutorial: Generate YOLOv8 Masks Fast

Getting started with Segment Anything (SAM)

How to Train Mask R‑CNN on Lung Segmentation Data

Introduction

Mask R‑CNN Tutorial: Guide to Instance Segmentation

Mask R-CNN has become one of the most practical ways to get high-quality instance segmentation results without needing to design a custom…

Detectron2 Panoptic Segmentation Made Easy for Beginners

Why panoptic segmentation matters ?

LLaVA Image Recognition in Python with Ollama and Vision Language Models

Introduction

I Built an AI to Grade Knee Osteoarthritis: A ResNet152V2 Deep Dive

In this ResNet152V2 TensorFlow tutorial, we aren’t just processing pixels; we are training a high-performance residual network to detect…

How to train YOLOv8 bone fracture detection on X-rays

Modern hospitals see thousands of fracture cases every year, and most of them are confirmed or ruled out using X-ray images. Interpreting…

How I Trained a Custom YOLO Model to Spot African Wildlife

Computer vision holds incredible potential when it comes to observing animals in their natural environments. This article provides a…

Xception Transfer Learning in TensorFlow: Build a Ship Classifier

xception transfer learning tensorflow is one of the most practical ways to train a strong image classifier without starting from zero. In…

Training an AI to Identify 43 Military Aircraft

Imagine trying to teach a machine to distinguish between the subtle silhouette of an F-22 Raptor and an F-35 Lightning II across thousands…

I Stopped Manually Labeling Images. A Practical Guide to Zero-Shot Object Detection with YOLO-World

I Generate Synthetic Images for Image Classification in Python (Prompt-to-Dataset Recipe)

When you need training images, the slow part is almost never the model. It’s the dataset. Collecting images, cleaning duplicates…