Pytorch

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 […]

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Create Synthetic Data for Computer Vision Pipelines

Image Classification, Image Segmentation, Pytorch /
Synthetic Data for Computer Vision

The process of manual data annotation has long been the most significant bottleneck in developing high-performance machine learning models. This tutorial focuses on a revolutionary shift in the industry: leveraging Synthetic Data for Computer Vision to bypass the tedious weeks spent in labeling software. By combining the generative power of Stable Diffusion with the intelligent

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How to Train ConvNeXt in PyTorch on a Custom Dataset

Image Classification, Pytorch /
ConvNext

ConvNeXt has become one of the most practical “modern CNN” choices when you want strong accuracy without giving up the speed and simplicity that make convolutional networks so useful in real projects. This article is about training ConvNeXt in PyTorch on a custom dataset—the kind you actually have in day-to-day work: folders of images organized

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Generate synthetic images for image classification in Python

Image Classification, Pytorch /
generate synthetic images for image classification

This article explains how to generate synthetic images for image classification using Python, Hugging Face Diffusers, and Stable Diffusion. It focuses on building a practical workflow that turns text prompts into high-quality training images, helping developers and researchers create datasets without scraping the web or manually collecting photos. By following a reproducible pipeline, you can

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Brain Tumor Segmentation with YOLOv11 in Python

Image Segmentation, Pytorch /
Brain Tumor Segmentation

Brain Tumor Segmentation with YOLOv11 in Python: What You’ll Build This article walks through a complete, practical workflow for brain tumor segmentation using YOLOv11 and Python, from environment setup and training to inference and mask export.Instead of stopping at “the model predicts something,” you’ll go all the way to saving individual segmentation masks, combining them

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I tried the Ultralytics SAM2 tutorial with YOLO11. Here’s what happened.

Image Segmentation, Pytorch /
ultralytics sam2 tutorial

Ultralytics SAM2 Tutorial, Explained Like You’d Code It An ultralytics sam2 tutorial is really about one idea: using a strong detector to tell SAM2 “where to look,” then letting SAM2 handle the hard part—drawing object boundaries.In this pipeline, YOLO11 produces bounding boxes for each image, and those boxes become box prompts for SAM2.1.This is a

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

Image Segmentation, Pytorch /
teeth segmentation

Building a practical teeth segmentation pipeline with YOLOv11 + SAM2 This article is about automating teeth segmentation so you can generate accurate masks without hand-drawing pixel labels for every dental image.That matters because segmentation projects often fail at the dataset stage, where annotation time and inconsistency become the biggest bottlenecks. The article walks through a

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Interactive SAM2 Segmentation: Points, Boxes, and Masks

Image Segmentation, Pytorch /
SAM2 tutorial

SAM2 Tutorial is quickly becoming one of the most practical ways for Python developers to get high-quality segmentation without training a model from scratch.Instead of building a dataset, tuning a network, and waiting for epochs to finish, you can load a pretrained SAM2 checkpoint and start extracting pixel-accurate masks right away.This is especially useful when

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How to Train Mask R‑CNN on Lung Segmentation Data

Image Segmentation, Pytorch /
Lung segmentation

Introduction Lung segmentation is one of the most important tasks in medical image analysis, especially when working with chest X-rays and CT scans.By accurately isolating lung regions from the rest of the image, it becomes much easier to analyze structure, detect abnormalities, and build reliable downstream models for diagnosis and monitoring.In recent years, deep learning

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