Eran Feit Blog posts

The bottleneck of every computer vision project isn’t the architecture—it’s the data. Manually drawing thousands of bounding boxes is slow, prone to human error, and expensive. In this guide, you will master auto-labeling YOLOv8 datasets with Autodistill, a revolutionary “teacher-student” framework. By leveraging massive foundation models like Grounding DINO to “teach” your compact YOLOv8 model, […]

Train YOLO-NAS on custom dataset in Python to achieve state-of-the-art object detection performance without the complexity of manual architecture design. While pre-trained models offer a great starting point, the real power of Neural Architecture Search (NAS) is unlocked when you apply it to specialized data, such as the underwater complexities of the Aquarium dataset. In

YOLO NAS object detection is all about combining modern deep learning with real-world practicality. At its core, YOLO NAS is a family of object detection models designed to spot and locate multiple objects in an image or video frame in real time. Instead of scanning an image piece by piece, the model “looks” at the

YOLOv8 small object detection with SAHI is one of the fastest ways to improve detections when targets are tiny (distant cars, small drones, pests on leaves) and full-image inference keeps missing them.In this tutorial, you’ll build a repeatable Python script that runs two passes on the same image: standard YOLOv8 inference, and then SAHI sliced

Automating medical diagnostics requires more than just high-speed algorithms; it requires precision. In this YOLOv8 bone fracture detection tutorial, you will master the end-to-end process of training a custom computer vision model to identify fractures in X-ray images. We will solve the critical challenge of detecting subtle structural breaks—a task where human fatigue can lead

Object detection becomes truly powerful when the model understands your world instead of just COCO-style benchmarks. That’s where yolov8 custom object detection comes in. Instead of detecting generic categories like dogs or cars, you fine-tune YOLOv8 on your own dataset, with your own labels, and tailor the model to a specific domain such as ships

When you train YOLOv8 on custom dataset, you turn a general-purpose object detector into a specialist that understands exactly the objects you care about.Instead of relying on COCO’s people, cars, and dogs, you can teach YOLOv8 to recognise things like playing cards, medical instruments, or products on a shelf with high speed and accuracy. Demo

Master the art of YouTube stream frame extraction for real-time computer vision projects. In this tutorial, we will dive deep into how to efficiently pull live video data from YouTube and process it through a YOLOv8 model. Whether you are building a live sports analytics tool or a traffic monitoring system, high-speed YouTube stream frame

Getting started with YOLOv8 dental object detection In modern dentistry, X-rays are no longer just static images on a screen. With yolov8 dental object detection, those images can be transformed into structured data that highlights teeth, restorations, lesions, and other findings automatically. Instead of manually scanning every millimeter of a radiograph, a trained YOLOv8 model

This tutorial focuses on EigenCAM YOLOv5 integration to reveal which image regions influence YOLOv5 object detection decisions. EigenCAM allows us to understand what parts of an image YOLOv5 relies on when detecting objects.Instead of treating the model as a black box, we can generate heatmaps that highlight the regions influencing each prediction. In this tutorial