...

VIT

Build Your Own Medical AI with Google MedGemma and Python

VIT, Python Cool Stuff, Pytorch /
Build Your Own Medical AI Doctor in Python

Google MedGemma represents a groundbreaking leap forward for developers, researchers, and healthcare technology enthusiasts looking to harness the power of artificial intelligence locally. This article is a comprehensive, step-by-step tutorial focused on implementing this state-of-the-art multimodal model on consumer-grade hardware. We dive deep into the practicalities of setting up a medical visual question-answering pipeline using

Build Your Own Medical AI with Google MedGemma and Python Read More »

Build Real Time Video Captioning Python Pipelines with LiveCC

Python Cool Stuff, VIT /
LiveCC Build Your Own Real-Time AI Video Subtitler

Real time video captioning python pipelines allow developers to transform static AI video analysis into continuous, frame-by-frame commentary streams. This article walks through building a complete multimodal video intelligence system from scratch using Python, Qwen2-VL, and the LiveCC architecture. Instead of waiting for an entire video to finish processing before receiving output, you will discover

Build Real Time Video Captioning Python Pipelines with LiveCC Read More »

Build a Local SAM 2 & Nvidia Describe Anything Model Pipeline

Python Cool Stuff, Pytorch, VIT /
Describe Anything-Use AI to Auto-Describe Any Video Object!

Building a local pipeline around the nvidia describe anything model solves a critical engineering problem for developers seeking to pair pixel-level object segmentation with advanced multimodal reasoning. Traditional computer vision setups struggle to bridge the gap between isolating an object and genuinely understanding its semantic details, often forcing teams to rely on cloud-hosted Multimodal Large

Build a Local SAM 2 & Nvidia Describe Anything Model Pipeline 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 Use UNETR for Multiclass Image Segmentation

VIT, Image Segmentation, TensorFlow tutorials /
multiclass image segmentation

Introduction Multiclass image segmentation is a powerful deep learning approach that allows us to separate an image into multiple meaningful regions, where each pixel is assigned to a specific category. Instead of simply deciding whether a pixel belongs to an object or not, multiclass image segmentation goes further and recognizes several different classes within the

How to Use UNETR for Multiclass Image Segmentation Read More »

FasterViT Image Classification Using Custom Dataset | Star wars dataset

VIT, Image Classification, Pytorch /
FasterViT image classification

Why FasterViT? The Power of Hybrid CNN-ViT Architectures Moving beyond standard architectures often feels like a trade-off between speed and accuracy. If you are looking to train FasterViT PyTorch custom dataset models, you’ve likely realized that NVIDIA’s hybrid approach is the current SOTA for throughput. In this guide, we solve the challenge of preparing a

FasterViT Image Classification Using Custom Dataset | Star wars dataset Read More »

FasterViT Image Classification: Step-by-Step PyTorch & Video Pipeline

VIT, Image Classification, Pytorch /
FasterViT image classification

While standard Vision Transformers (ViTs) deliver impressive accuracy, their quadratic self-attention mechanism often creates prohibitive latency bottlenecks in production environments. In this guide, you will master FasterViT image classification using PyTorch and OpenCV to achieve high-throughput inference without sacrificing precision. We explore how NVIDIA’s hybrid CNN-Transformer architecture leverages hierarchical attention and carrier tokens, walking through

FasterViT Image Classification: Step-by-Step PyTorch & Video Pipeline Read More »

Amazing Guide to fine tune ConvNeXT Quickly

VIT, Image Classification, Pytorch /
Fine tune Image Classificatrion using ConvNext for custom dataset

Introduction If you are struggling to achieve high accuracy on niche image datasets using standard ResNet architectures, it’s time to modernize your pipeline. In this guide, you will learn exactly how to fine-tune ConvNeXt PyTorch custom dataset workflows to achieve state-of-the-art results. While Vision Transformers (ViT) are popular, ConvNeXt offers the efficiency of standard convolutions

Amazing Guide to fine tune ConvNeXT Quickly Read More »

Eran Feit