Eran Feit Blog posts

Incredible AI Athlete Tracking: Professional Tutorial

Image Segmentation, Object Detection, Pytorch /
How to Build a Pro Sports Tracker with No Dataset

Manual data labeling has long been the bottleneck of modern computer vision, especially in the high-stakes world of sports analytics. This article explores a professional-grade methodology for building an AI Athlete Tracking system that bypasses the traditional, grueling process of hand-annotating thousands of frames. By orchestrating a pipeline of GroundingDINO for discovery, YOLO11 for speed, […]

Incredible AI Athlete Tracking: Professional Tutorial Read More »

How to Use Automated Data Annotation for YOLO11

Object Detection, Pytorch /
GroundedSAM Auto Annotation

Building a high-performance computer vision pipeline in 2026 shouldn’t feel like a manual labor job from the last decade. This article is a comprehensive deep dive into bypassing the traditional “data bottleneck” by leveraging a sophisticated, code-driven workflow. We are exploring how to bridge the gap between raw video footage and a production-ready YOLO11 model

How to Use Automated Data Annotation for YOLO11 Read More »

How to Build Dental Cavity Detection AI with RT-DETR

Object Detection, Pytorch, VIT /
RT-DETR Tutorial - Detect Cavities

By Eran Feit — Computer Vision engineer and educator with 10+ years in deep learning. Integrating artificial intelligence into the world of dentistry is no longer a concept confined to academic papers; it is becoming a critical tool for diagnostic accuracy in modern clinics. This guide focuses on the practical implementation of Dental Cavity Detection

How to Build Dental Cavity Detection AI with RT-DETR Read More »

How to Implement RT-DETR in Python with Ultralytics

Object Detection, Pytorch, VIT /
RT-DETR Tutorial Detection

This RT-DETR tutorial is your complete guide to mastering the first real-time end-to-end object detector built on the revolutionary Transformer architecture. This article is about transitioning from standard convolutional models to a more efficient, attention-driven system that delivers state-of-the-art results. By focusing on the practical application of the Real-Time Detection Transformer, we provide a clear

How to Implement RT-DETR in Python with Ultralytics Read More »

Ultimate Microsoft Florence-2 Tutorial for Incredible Results

Object Detection, Pytorch, VIT /
Florence-2 object detection

Modern computer vision has often felt like a jigsaw puzzle where the pieces don’t quite fit—historically, you might use YOLO for detection, a separate transformer for captioning, and an entirely different OCR engine for text extraction. This Microsoft Florence-2 tutorial is designed to dismantle that fragmented workflow by introducing you to a unified vision-language foundation

Ultimate Microsoft Florence-2 Tutorial for Incredible Results Read More »

How to Blur Objects in Real-time with YOLO11 and AI

Object Detection, Pytorch /
YoloV11-Blur objects

Modern data privacy is no longer a luxury; it is a technical and legal mandate. As video surveillance and public live-streaming become ubiquitous, the need to protect sensitive information like faces and license plates has skyrocketed. This article explores a cutting-edge approach to real-time AI video blurring using the high-performance YOLO11 model. By the end

How to Blur Objects in Real-time with YOLO11 and AI Read More »

YOLOv11 Guide: Extract and Crop Objects from Video Python

Object Detection, Pytorch /
Auto-Crop Objects with YOLOv11

Master Automation: Extract Objects from Video Python Building a high-quality dataset is often the most time-consuming part of any computer vision project. This article provides a comprehensive guide on how to Extract Objects from Video Python using the latest YOLOv11 framework and OpenCV. We move beyond simple detection and focus on the practical necessity of

YOLOv11 Guide: Extract and Crop Objects from Video Python Read More »

How to Use Supervision with YOLOv8 for Object Detection & Tracking

Object Detection, Pytorch /
Object Tracker with Supervision & YOLO

Are you struggling to build robust computer vision pipelines? While Ultralytics provides incredibly powerful models, writing custom logic for object tracking, zone counting, and bounding box annotations can quickly become a messy web of complex Python code. That is exactly where the Roboflow Supervision library comes in. In this guide, you will learn exactly how

How to Use Supervision with YOLOv8 for Object Detection & Tracking Read More »

How to Make YOLOv8 10x Faster using TensorRT 10

Object Detection, Pytorch /
YOLOv8 TensorRT 10

Deploying high-accuracy object detection in production often reveals a critical bottleneck: inference latency. If you are struggling to achieve true real-time performance on NVIDIA hardware, learning how to optimize YOLOv8 with TensorRT 10 in Python is the ultimate solution. Standard PyTorch models introduce significant overhead that drains GPU memory and limits frame rates. By converting

How to Make YOLOv8 10x Faster using TensorRT 10 Read More »

Build Your Own YouTube AI Tracking System | YOLOv8 Norfair

Object Detection, Pytorch /
Object Tracker with Norfair

This article provides a comprehensive technical walkthrough on implementing a professional-grade YOLOv8 Norfair tracking pipeline. By bridging the gap between raw object detection and persistent identity management, the guide addresses one of the most common hurdles in computer vision: maintaining a stable lock on subjects as they move through dynamic environments. Readers will learn how

Build Your Own YouTube AI Tracking System | YOLOv8 Norfair Read More »

Eran Feit