Eran Feit Blog posts

In this guide, we dive deep into the intersection of computer vision and modern agriculture by building a robust system for identifying plant pathologies. This Faster R-CNN PyTorch Tutorial focuses specifically on tomato leaf disease detection, a critical challenge for farmers and agronomists worldwide. By leveraging deep learning, we can automate the diagnosis of common […]

In the world of computer vision, choosing the right architecture is often a game of trade-offs between precision and performance. This guide provides a hands-on exploration of two foundational architectures used in modern AI development. We dive deep into the practical implementation of Faster R-CNN vs SSD PyTorch, contrasting the heavy-duty accuracy of two-stage detectors

This guide is a comprehensive, hands-on technical tutorial to building a state-of-the-art computer vision pipeline specifically tailored for detecting diverse animal species in their natural habitats. Using modern training techniques, you will learn how to configure your environment, train a deep learning model on a custom dataset, and accurately run inference to recognize wildlife in

This guide dives deep into the practical implementation of computer vision by showing you how to Fine-tune YOLOv8 Open Images V7 specifically for the complex task of identifying military aircraft. While generic object detection is a common starting point for many developers, moving into a high-precision niche requires a more nuanced approach to model training

Modern object detection has reached a pivotal moment with the release of open-vocabulary models that can identify objects they have never seen during training. This tutorial focuses on bridging the gap between general AI capabilities and specialized industrial applications by showing you how to train YOLO-World on custom dataset files. While YOLO-World is renowned for

In this YOLO-World tutorial, we explore the groundbreaking shift in computer vision from supervised learning to zero-shot inference. We are moving away from the tedious days of manual bounding box labeling and toward a future where natural language prompts define detection logic in real-time. This transition allows for an unprecedented level of flexibility in how

Object tracking has evolved from a complex experimental challenge into a mandatory requirement for modern vision-based applications. This guide provides a comprehensive roadmap for building a high-performance detection and tracking pipeline, focusing on the seamless integration of state-of-the-art models and robust tracking algorithms. We explore the transition from simple frame-by-frame detection to persistent identity management,

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