Eran Feit Blog posts

Object detection heatmap is a simple idea with a lot of power behind it.Instead of just drawing bounding boxes around objects, you aggregate all those detections into a colorful map that shows where activity is concentrated.Each new detection slightly “warms up” the corresponding region of the frame, so after processing many frames you get a

Train YOLOv8 on Stanford Dogs to build a multi-class dog breed detection model that can find dogs in real photos and label them as one of 120 breeds.This article walks through the full workflow from raw Stanford Dogs files to a trained YOLOv8 model you can run on new images. Readers get value here because

YOLOv8 YouTube object detection in Python: a full pipeline you can reuse YOLOv8 YouTube object detection is one of the fastest ways to move from “demo code” to a real computer-vision workflow.Instead of training on random images, you build a dataset from actual video footage that matches what you want the model to learn. In

Automatic image annotation is all about teaching machines to tag images for us.Instead of a human drawing every bounding box and typing every label, models learn to recognize patterns and automatically assign classes like horse, car, or person to each object in a picture or video frame.This drastically reduces the manual work needed to build

To train YOLO-NAS on a custom dataset effectively, you must understand how to bridge the gap between pre-trained weights and domain-specific data. In this guide, we will use the SuperGradients library to build a robust object detection system for an aquarium environment. Whether you are dealing with low-light underwater footage or complex reflections, this workflow

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

Modern hospitals see thousands of fracture cases every year, and most of them are confirmed or ruled out using X-ray images. Interpreting those X-rays quickly and accurately is critical, but it’s also repetitive, tiring work for radiologists and orthopedic teams. That’s exactly where yolov8 bone fracture detection comes in: it combines state-of-the-art object detection with