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# Eran Feit: Home of the best computer vision tutorials : Object detection , Image classification, vision transformers ,  Image segmentation

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## Posts
- [How to Build a Poker Hand Scanner via Card Recognition API](https://eranfeit.net/how-to-build-a-poker-hand-scanner-via-card-recognition-api/): Deploying a dedicated card recognition api allows developers to instantly embed advanced computer vision capabilities directly into production environments without managing underlying GPU clusters or machine learning dependencies. Building high-performance image recognition models manually requires immense computational power, months of data annotation, and fine-tuning intricate neural networks. By shifting this heavy architectural lifting to a cloud-based endpoint, engineering teams completely eliminate the friction of model versioning, dependency conflicts, and hardware resource constraints.
- [How to Customize Bounding Boxes in Ultralytics YOLO](https://eranfeit.net/how-to-customize-bounding-boxes-in-ultralytics-yolo/): By the end of this tutorial, you will fully understand how to implement the Ultralytics YOLO annotation tool within your own custom pipelines. Whether you are building an interactive dashboard or optimizing a video processing script, the steps outlined below will give you the precise programmatic control needed to manipulate bounding box geometry, handle colors dynamically, and export the finalized annotated video seamlessly.
- [How to Track and Filter Custom Classes with YOLO11 Object Counting](https://eranfeit.net/how-to-track-and-filter-custom-classes-with-yolo11-object-counting/): Computer vision is evolving rapidly, and the release of Ultralytics YOLO11 brings unprecedented speed and accuracy to real-time video analytics. This article provides a hands-on, production-ready guide to implementing a yolo11 object counting python workflow capable of detecting, tracking, and logging specific moving entities. By combining state-of-the-art deep learning models with practical frame-by-frame processing, we bridge the gap between theoretical machine learning and functional, real-world computer vision applications.
- [Ultralytics YOLO11 Analytics Guide | Build a Real-Time YOLO Tracker](https://eranfeit.net/ultralytics-yolo11-analytics-guide-build-a-real-time-yolo-tracker/): By the end of this Ultralytics YOLO11 analytics guide, you will have a deep understanding of how to manage high-speed inference while maintaining a responsive user interface. This technical roadmap is designed to simplify the complexities of modern tracking algorithms while giving you the freedom to customize the visual output. Whether you are building an automated surveillance system or a research tool, the methods shared here will help you deliver high-performance vision solutions with confidence.
- [Tomato Leaf Disease Detection: Faster R-CNN PyTorch Tutorial](https://eranfeit.net/tomato-leaf-disease-detection-faster-r-cnn-pytorch-tutorial/): 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 issues like Early Blight and Mosaic Virus, transforming how we monitor crop health at scale.
- [Accuracy vs. Speed: Comparing Faster R-CNN and SSD in PyTorch](https://eranfeit.net/accuracy-vs-speed-comparing-faster-r-cnn-and-ssd-in-pytorch/): 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 against the streamlined efficiency of single-shot models.
- [Train YOLO for African Wildlife Detection](https://eranfeit.net/train-yolo-for-african-wildlife-detection/): In the rapidly evolving field of artificial intelligence, transitioning from standard benchmarking datasets to real-world scenarios is where true mastery happens. Working through the complete implementation of African wildlife detection YOLO models provides you with concrete skills that go beyond basic theory, empowering you to solve complex visual recognition challenges such as handling diverse lighting, camouflaged subjects, and intricate background environments.
- [How to Fine-tune YOLOv8 Open Images V7 for 43 Aircraft classes](https://eranfeit.net/how-to-fine-tune-yolov8-open-images-v7-for-aircraft/): 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 and dataset handling. We will bridge the gap between theory and deployment by using a modern tech stack involving PyTorch 2.9.1 and CUDA 12.8, ensuring your environment is ready for the latest deep learning standards.
- [How to Train YOLO-World on Custom Dataset | Underwater trash dataset](https://eranfeit.net/how-to-train-yolo-world-on-custom-dataset-underwater-trash-dataset/): 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 its zero-shot performance, the true power for professional-grade projects lies in fine-tuning the model to recognize niche objects—like underwater debris—with the surgical precision required for real-world deployment.
- [How to Use YOLO-World for Zero-Shot Object Detection](https://eranfeit.net/how-to-use-yolo-world-for-zero-shot-object-detection/): 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 we interact with visual data, transforming text descriptions directly into actionable detection coordinates.
- [Object Tracking with Supervision and YOLOv11 ByteTrack for AI Developers](https://eranfeit.net/object-tracking-with-supervision-and-yolov11-bytetrack-for-ai-developers/): To achieve these results, we will utilize the YOLOv11 ByteTrack Python ecosystem, leveraging the latest advancements in hardware acceleration and library optimization. We will walk through the specific environment configuration needed for PyTorch 2.9.1 and CUDA 12.8, followed by a modular code breakdown that shows you exactly how to pass detections into a tracking logic. This hands-on approach ensures you aren't just copy-pasting code, but actually mastering the mechanics of modern motion analysis.
- [Incredible AI Athlete Tracking: Professional Tutorial](https://eranfeit.net/incredible-ai-athlete-tracking-professional-tutorial/): Manual data labeling has long been the bottleneck of modern computer vision, especially in the high-stakes world of sports analytics. This article explores a professional-grade methodology for building an AI Athlete Tracking system that bypasses the traditional, grueling process of hand-annotating thousands of frames. By orchestrating a pipeline of GroundingDINO for discovery, YOLO11 for speed, and Meta’s SAM for precision, we bridge the gap between raw video footage and production-ready segmentation.
- [How to Use Automated Data Annotation for YOLO11](https://eranfeit.net/how-to-use-automated-data-annotation-for-yolo11/): 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 by using automated data annotation. By integrating Grounded-SAM and Autodistill, we create a "teacher-student" dynamic where AI identifies objects like bees and flowers and labels them with surgical precision, effectively turning weeks of manual work into a few minutes of execution.
- [How to Build Dental Cavity Detection AI with RT-DETR](https://eranfeit.net/how-to-build-dental-cavity-detection-ai-with-rt-detr/): 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 AI, leveraging the latest advancements in real-time object detection to identify pathologies in X-ray and intraoral imagery. By transitioning from traditional convolutional architectures to transformer-based models, we can achieve a level of precision that was previously difficult to maintain in high-stakes medical environments.
- [How to Implement RT-DETR in Python with Ultralytics](https://eranfeit.net/how-to-implement-rt-detr-in-python-with-ultralytics/): 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 path for developers to integrate sophisticated AI into their existing workflows without the usual steep learning curve.
- [Ultimate Microsoft Florence-2 Tutorial for Incredible Results](https://eranfeit.net/ultimate-microsoft-florence-2-tutorial-for-incredible-results/): 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 model that handles nearly every visual task within a single, elegant architecture. We are moving away from "Frankenstein pipelines" and toward a streamlined, efficient approach that leverages the power of Microsoft’s groundbreaking unified representation.
- [How to Blur Objects in Real-time with YOLO11 and AI](https://eranfeit.net/how-to-blur-objects-in-real-time-with-yolo11-and-ai/): Modern data privacy is no longer a luxury; it is a technical and legal mandate. As video surveillance and public live-streaming become ubiquitous, the need to protect sensitive information like faces and license plates has skyrocketed. This article explores a cutting-edge approach to real-time AI video blurring using the high-performance YOLO11 model. By the end of this guide, you will understand how to leverage computer vision to identify specific objects and instantly obscure them, ensuring your visual data remains compliant and secure.
- [YOLOv11 Guide: Extract and Crop Objects from Video Python](https://eranfeit.net/yolov11-guide-extract-and-crop-objects-from-video-python/): Building a high-quality dataset is often the most time-consuming part of any computer vision project. This article provides a comprehensive guide on how to Extract Objects from Video Python using the latest YOLOv11 framework and OpenCV. We move beyond simple detection and focus on the practical necessity of isolating specific targets from raw footage, turning hours of manual labor into a few seconds of automated processing.
- [How to Use Supervision with YOLOv8 for Object Detection & Tracking](https://eranfeit.net/the-ultimate-guide-how-to-use-supervision-with-yolov8/): Are you struggling to build robust computer vision pipelines? While Ultralytics provides incredibly powerful models, writing custom logic for object tracking, zone counting, and bounding box annotations can quickly become a messy web of complex Python code. That is exactly where the Roboflow Supervision library comes in. In this guide, you will learn exactly how to use Supervision with YOLOv8 to streamline your workflows. By the end of this tutorial, you will transform raw model inferences into production-ready, highly visual applications with minimal boilerplate code, effectively solving the headache of manual OpenCV drawing and custom tracking math.
- [How to Make YOLOv8 10x Faster using TensorRT 10](https://eranfeit.net/how-to-make-yolov8-10x-faster-using-tensorrt-10/): Deploying high-accuracy object detection in production often reveals a critical bottleneck: inference latency. If you are struggling to achieve true real-time performance on NVIDIA hardware, learning how to optimize YOLOv8 with TensorRT 10 in Python is the ultimate solution. Standard PyTorch models introduce significant overhead that drains GPU memory and limits frame rates. By converting your models into specialized TensorRT 10 execution engines, you unlock hardware-level speedups—often boosting performance by up to 10x. In this tutorial, we will bridge the gap between model export and real-time inference using Ultralytics and CUDA.
- [Build Your Own YouTube AI Tracking System | YOLOv8 Norfair](https://eranfeit.net/build-your-own-youtube-ai-tracking-system-yolov8-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 to transition from basic bounding boxes that flicker and reset to a robust system that assigns unique, long-term IDs to every individual on screen.
- [The Ultimate AI Kit: 40 Models in 1 Python Script](https://eranfeit.net/the-ultimate-ai-kit-40-models-in-1-python-script/): Imagine having a library of the world’s most advanced computer vision models at your fingertips, ready to deploy with a single script. This article is a deep dive into the TensorFlow 2 Object Detection Tutorial ecosystem, specifically focusing on the "Model Zoo"—a repository of pre-trained architectures that allow you to skip the expensive and time-consuming process of training AI from scratch. Whether you are a researcher aiming for high-precision results or a developer building real-time mobile apps, the ability to rapidly swap between 40+ different models is a game-changer for your workflow.
- [Fast Object Detection in Python with MediaPipe](https://eranfeit.net/fast-object-detection-in-python-with-mediapipe/): In the rapidly evolving landscape of computer vision, building efficient, high-performance applications often feels like a choice between heavy, resource-hungry frameworks or overly simplified tools. This article focuses on MediaPipe Object Detection Python, a powerful solution from Google designed to bridge that gap by offering professional-grade accuracy with a lightweight footprint. Whether you are a student looking to start your first AI project or an experienced developer seeking a deployment-friendly alternative to bulky models, the following guide provides a direct path to success.
- [Create Synthetic Data for Computer Vision Pipelines](https://eranfeit.net/create-synthetic-data-for-computer-vision-pipelines/): 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 labeling capabilities of GroundingDINO, you will learn how to create a self-sustaining data factory that produces training-ready datasets in minutes rather than months.
- [Detect Alzheimer’s: Deep Learning Python & Xception](https://eranfeit.net/detect-alzheimers-deep-learning-python-xception/): In the rapidly evolving landscape of medical AI, the ability to translate raw clinical data into actionable diagnostic insights is a defining skill for the modern developer. This article is a deep-dive technical guide into building an Alzheimer’s detection deep learning python pipeline from scratch, specifically designed to bridge the gap between theoretical neural networks and practical healthcare applications. By focusing on the Xception architecture—a model renowned for its efficiency in feature extraction—we move beyond basic "Hello World" tutorials and into the complex world of multi-class medical image classification.
- [Butterfly Species Identification CNN with TensorFlow & Python](https://eranfeit.net/butterfly-species-identification-cnn-with-tensorflow-python/): Manual classification of Lepidoptera is a time-consuming task that requires significant expertise in entomology. In this comprehensive guide, you will master Butterfly Species Identification using CNN with TensorFlow and Python, transforming raw image data into a predictive computer vision model. We solve the challenge of automated biodiversity monitoring by building a custom Convolutional Neural Network (CNN) capable of distinguishing between diverse species with high precision. Whether you are a student or an AI researcher, this walkthrough bridges the gap between theoretical deep learning and practical Python implementation, ensuring your model achieves both high accuracy and robust generalization.
- [Build a 100-Class Sports Classifier with EfficientNetB0](https://eranfeit.net/build-a-100-class-sports-classifier-with-efficientnetb0/): This EfficientNetB0 image classification tutorial is designed to teach you how to build a robust system capable of identifying 100 different sports categories from scratch. By utilizing the power of transfer learning and the high-efficiency architecture of the EfficientNetB0 model, you will learn how to transform raw image data into a sophisticated classification engine. This project is designed to bridge the gap between theoretical deep learning and practical, real-world deployment.
- [Fast Keras Hub Image Classification Tutorial](https://eranfeit.net/fast-keras-hub-image-classification-tutorial/): In this modern Keras Hub ImageClassifier from preset tutorial, you will learn how to leverage the latest Keras 3 framework to perform high-performance computer vision tasks in Python. When deploying deep learning pipelines, loading weights securely and seamlessly is a common bottleneck. By adopting the from_preset() method within the Keras Hub ecosystem, you bypass complex architecture rebuilding and jump straight to backend-agnostic inference. This guide breaks down exactly how to initialize your model, handle preprocessing, and use your own images to get rapid classification predictions.
- [Classifying Knee X-Rays with ResNet152V2 & TensorFlow](https://eranfeit.net/classifying-knee-x-rays-with-resnet152v2-tensorflow/): Are you struggling to accurately identify abnormalities in medical imaging? In this tutorial, we will dive into deep learning for knee X-ray classification using TensorFlow and the powerful ResNet152V2 architecture. Medical image classification poses unique challenges—such as high visual variability, subtle bone structures, and limited datasets—that standard neural networks struggle to handle. By the end of this guide, you will know exactly how to leverage transfer learning to build a computer vision pipeline capable of automatically classifying knee radiographs with high precision. We aren't just going to run code; we are going to explore the underlying logic of why residual networks excel in healthcare diagnostics.
- [How to Train ConvNeXt in PyTorch on a Custom Dataset](https://eranfeit.net/how-to-train-convnext-in-pytorch-on-a-custom-dataset/): 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 by class names.
- [CNN Image Classification TensorFlow: 30 Musical Instruments](https://eranfeit.net/cnn-image-classification-tensorflow-30-musical-instruments/): Building a robust model for automated visual recognition requires more than just stacking layers; it requires an understanding of how features are extracted from complex shapes. In this CNN image classification with TensorFlow: 30 musical instruments tutorial, we solve the specific challenge of classifying high-variance acoustic and electronic instruments. You will learn how to transition from raw image data to a trained Keras model capable of distinguishing between 30 unique classes. This guide provides the technical logic and architectural insights necessary to move beyond "hello world" examples into functional computer vision applications.
- [Generate synthetic images for image classification in Python](https://eranfeit.net/generate-synthetic-images-for-image-classification-in-python/): 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 produce consistent, labeled images tailored to your exact classification needs.
- [Transfer learning using Xception | ship classifier](https://eranfeit.net/transfer-learning-using-xception-ship-classifier/): Xception Transfer Learning Tensorflow is the fastest way to build a strong ship image classifier without training a deep network from scratch. In this tutorial, you’ll train Xception on ship categories like Cargo, Military, Carrier, Cruise, and Tankers using a full end-to-end TensorFlow pipeline.
- [MediaPipe image classifier Python with EfficientNet-Lite0](https://eranfeit.net/mediapipe-image-classifier-python-with-efficientnet-lite0/): Ever wanted a quick way to recognize what’s inside a photo without training a model or building a huge pipeline.This article is about running MediaPipe image classifier Python code end-to-end, using a lightweight EfficientNet-Lite0 TensorFlow Lite model to classify a real image in seconds.
- [Brain Tumor Segmentation with YOLOv11 in Python](https://eranfeit.net/brain-tumor-segmentation-with-yolov11-in-python/): 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 into a final segmentation map, and visualizing results with OpenCV.
- [How to UNet Image Segmentation TensorFlow on Custom Data | Dolphin Segmentation](https://eranfeit.net/how-to-unet-image-segmentation-tensorflow-on-custom-data-dolphin-segmentation/): This tutorial shows how to build an end-to-end unet image segmentation tensorflow workflow that feels like a real project.You start with raw dolphin photos and JSON polygon annotations.You finish with a trained U-Net model that predicts clean dolphin masks on unseen images.
- [I tried the Ultralytics SAM2 tutorial with YOLO11. Here’s what happened.](https://eranfeit.net/i-tried-the-ultralytics-sam2-tutorial-with-yolo11-heres-what-happened/): 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 clean division of labor: detection handles localization, segmentation handles precision.
- [Here’s What Combining YOLOv11 with SAM2 Taught Me About Segmentation](https://eranfeit.net/heres-what-combining-yolov11-with-sam2-taught-me-about-segmentation/): 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.
- [SAM 2 Video Segmentation Python: Zero-Shot Masking & Tracking Guide](https://eranfeit.net/complete-guide-to-sam2-video-segmentation-in-python/): In this tutorial, we will learn SAM 2 Video Segmentation Python step by step using the Segment Anything Model 2 by Meta AI.This guide explains how to perform video object segmentation, object tracking, and mask propagation across video frames using SAM2 in Python.SAM 2 Video Segmentation Python is useful for computer vision tasks such as video analytics, automatic annotation, surveillance, sports tracking, and medical video segmentation.
- [Interactive SAM2 Segmentation: Points, Boxes, and Masks](https://eranfeit.net/interactive-sam2-segmentation-points-boxes-and-masks/): 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 you want to isolate objects for editing, measurement, labeling, or downstream computer vision tasks like tracking and instance analysis.
- [How to Train Mask R‑CNN on Lung Segmentation Data](https://eranfeit.net/how-to-train-mask-r-cnn-on-lung-segmentation-data/): 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 has transformed lung segmentation from a manual, time-consuming process into an automated and highly precise workflow.
- [Mask R-CNN Python Tutorial: A Complete Guide to Instance Segmentation](https://eranfeit.net/mask-r-cnn-tutorial-guide-to-instance-segmentation/): Object detection can tell you where an object is, but it falls short when you need the exact pixel boundaries. If you are struggling to move beyond basic bounding boxes, this Mask R-CNN Python tutorial for instance segmentation is exactly what you need. In this guide, we will bridge the gap between theoretical computer vision and practical implementation. You will learn how to configure a pre-trained model, properly process image tensors, and extract highly accurate, pixel-perfect masks for distinct objects in your images. Let's dive into the code and mechanics behind state-of-the-art image segmentation.
- [YOLO Image Segmentation for Crack Detection Projects](https://eranfeit.net/yolo-image-segmentation-for-crack-detection-projects/): YOLO image segmentation is a practical way to move from “where is the object” to “which exact pixels belong to it.”Instead of stopping at a bounding box, segmentation gives you a mask that traces the real outline of the target region.That extra detail matters in computer vision tasks where shape, edges, and fine structures carry the information you care about.For thin patterns like cracks, scratches, and surface defects, a pixel mask is often more useful than a rectangle because the signal is narrow and irregular.
- [How to Train YOLO Segmentation on Custom Datasets – Fiber Segmentation](https://eranfeit.net/how-to-train-yolo-segmentation-on-custom-datasets-fiber-segmentation/): YOLO segmentation is one of the fastest ways to turn images into meaningful pixel-level information.Instead of only drawing bounding boxes, it predicts an object mask that outlines the exact shape of what you care about.That extra precision matters when the boundaries are thin, irregular, or overlapping, like fibers, cracks, wires, hair, or medical structures.
- [Instance Segmentation Python Tutorial Using YOLO Models in videos](https://eranfeit.net/instance-segmentation-python-tutorial-using-yolo-models-in-videos/): A solid instance segmentation python tutorial should help you connect the big idea to an end-to-end workflow.You want to understand what the model outputs, how masks are represented, how to draw them correctly, and how to run inference on images and videos reliably.Once those pieces click, you can turn segmentation into a reusable building block in your own projects, from simple demos to production-style pipelines.
- [Ultralytics Annotator: Segment and Label Videos in Python](https://eranfeit.net/segment-and-label-videos-using-ultralytics-annotator/): Manually labeling video frames for computer vision can be a bottleneck, but visualizing model predictions shouldn't be. In this guide, you will learn how to use the ultralytics annotator video segmentation python utility to transform raw model outputs into professional-grade annotated videos. Whether you are debugging a YOLO11 model or building a production-ready demo, we solve the problem of messy visualization by implementing a clean, efficient pipeline that overlays precise masks and class labels onto every frame of your footage.
- [Image Matting U2-Net TensorFlow Tutorial: Step-by-Step Guide](https://eranfeit.net/image-matting-using-u2-net-with-tensorflow-tutorial/): Tired of jagged, pixelated edges when removing image backgrounds? Standard image segmentation often falls short when handling complex visual details like loose hair strands, fine fur, or semi-transparent objects. In this comprehensive U2-Net image matting with TensorFlow tutorial, you will discover how to generate high-resolution alpha mattes for professional-grade background extraction. Using deep learning and the advanced nested U-structure of U2-Net, this guide takes you through the complete Python implementation to transform raw pixels into production-ready masks. Perfect for computer vision developers, this approach guarantees pixel-perfect edge precision for your AI applications.
- [Binary Image Segmentation with VGG16 U-Net for Dust Storm Detection](https://eranfeit.net/binary-image-segmentation-with-vgg16-u-net-dust-storm-segmentation/): Implementing binary image segmentation with VGG16 U-Net for dust storm detection is a critical challenge in environmental monitoring and remote sensing. Standard convolutional neural networks often struggle with the amorphous, low-contrast boundaries of dust clouds. However, by leveraging a pre-trained VGG16 backbone as an encoder within a U-Net framework, we can achieve high-precision pixel-wise classification even with limited training data. In this tutorial, you will solve the problem of identifying complex weather phenomena by building an end-to-end deep learning pipeline that transforms raw imagery into accurate, actionable segmentation masks.
- [How to Train U‑Net EfficientNet-B0 for Water Segmentation](https://eranfeit.net/how-to-train-u-net-efficientnet-b0-for-water-segmentation/): Training modern image segmentation models has become more accessible thanks to powerful pretrained backbones and flexible deep learning frameworks.One of the most effective combinations today is How to Train U-Net EfficientNet B0 for Water Segmentation, which merges a proven segmentation architecture with a lightweight yet expressive encoder.This approach is especially well-suited for satellite imagery, where capturing fine spatial details and large contextual patterns is critical.By leveraging EfficientNet B0 inside a U-Net structure, it is possible to achieve high accuracy while keeping the model efficient and practical to train.
- [UNet PyTorch Tutorial: Build a Segmentation Model](https://eranfeit.net/unet-pytorch-tutorial-build-a-segmentation-model/): In this UNet PyTorch tutorial, you’re building a complete image segmentation workflow that feels like a real project, not a toy example.Instead of stopping at “here’s the model,” you go end-to-end: preparing the dataset, training a U-Net from scratch, and then using the trained weights to predict masks on new images.
- [How to Perform Florence-2 segmentation on Images](https://eranfeit.net/how-to-perform-florence-2-segmentation-on-images/): Florence-2 segmentation is a workflow where you give a model an image and a short natural-language phrase, and it returns the region of the image that matches your phrase.Instead of training a custom segmentation model, you can often get useful masks right away by prompting something simple like “a parrot” or “the red car.”
- [How to segment multiple objects with YOLO Python](https://eranfeit.net/how-to-segment-multiple-objects-with-yolo-python/): YOLO segmentation tutorial Python is a practical and modern way to understand how computers can go beyond bounding boxes and truly understand the shape of objects inside an image.Instead of only detecting where an object is, segmentation allows us to identify the exact pixels that belong to each object, even when multiple objects appear in the same scene.This approach is essential when precision matters, such as image analysis, automation, and visual understanding tasks where object boundaries are important.
- [Image Segmentation with MediaPipe: Replace Background](https://eranfeit.net/image-segmentation-with-mediapipe-replace-background/): Image segmentation with mediapipe is a practical way to separate a subject from its surroundings at the pixel level.Instead of drawing a rectangle around an object, segmentation creates a mask that follows the object’s real outline.That makes edits like background replacement look much cleaner and more realistic.
- [How to Highlight Object in Image with MediaPipe and Python](https://eranfeit.net/how-to-highlight-object-in-image-with-mediapipe-and-python/): Highlight object in image python is a common requirement in modern computer vision workflows, especially when building interactive applications that respond to user input. Instead of manually drawing masks or bounding boxes, segmentation models allow precise pixel-level control over which parts of an image are emphasized. This makes object highlighting far more accurate and visually appealing than traditional detection methods.
- [MediaPipe Image Segmentation Using DeepLabV3](https://eranfeit.net/mediapipe-image-segmentation-using-deeplabv3/): MediaPipe image segmentation is a practical computer vision technique that allows separating foreground objects from the background at the pixel level.Instead of relying on bounding boxes or simple color thresholds, segmentation classifies every pixel in the image, making it ideal for background removal, background blur, and visual effects.
- [How to Use UNETR for Multiclass Image Segmentation](https://eranfeit.net/how-to-use-unetr-for-multiclass-image-segmentation/): 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 same image. This becomes especially useful in real-world computer vision tasks where images naturally contain many structures, like faces, medical scans, traffic scenes, or satellite imagery.
- [Hair segmentation using Transformers | UNETR Image Segmentation](https://eranfeit.net/hair-segmentation-using-transformers-unetr-image-segmentation/): Precise hair segmentation remains one of the most challenging tasks in computer vision due to the fine, irregular boundaries and varying textures of human hair. While traditional CNNs like U-Net excel at local feature extraction, they often struggle with the global context required for complex occlusions. In this guide, you will master Hair Segmentation using UNETR Transformers in Python. By leveraging the power of Vision Transformers (ViT) within an encoder-decoder framework, we will solve the problem of boundary blurring, allowing you to generate high-fidelity semantic masks for augmented reality or portrait editing applications.
- [FasterViT Image Classification Using Custom Dataset | Star wars dataset](https://eranfeit.net/fastervit-image-classification-using-custom-dataset-star-wars-dataset/): 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 non-standard dataset—specifically our Star Wars character collection—and feeding it into the FasterViT pipeline. You’ll learn exactly how to configure the model to leverage both convolutional efficiency and hierarchical attention, transforming raw images into a high-performance classifier that outperforms traditional Vision Transformers.
- [How to Use FasterViT for Image and video Classification](https://eranfeit.net/how-to-use-fastervit-for-image-and-video-classification/): A fastervit image classification tutorial introduces a powerful and efficient way to recognize visual patterns in images using modern deep learning techniques. FasterViT is a hybrid model that combines the strengths of convolutional neural networks (CNNs) with vision transformers to deliver both high accuracy and fast processing. For developers and machine learning practitioners seeking to build advanced computer vision applications, this tutorial provides a practical, hands-on path to mastering image classification with FasterViT.
- [Amazing Guide to fine tune ConvNeXT Quickly](https://eranfeit.net/amazing-guide-to-fine-tune-convnext-quickly/): 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 with the performance boost of transformer-inspired design. We will solve the problem of adapting pre-trained ImageNet-22k weights to your specific classes, ensuring your model generalizes well even with limited data.
- [How to classify images using ConvNext | Easy tutorial](https://eranfeit.net/how-to-classify-images-using-convnext-easy-tutorial/): ConvNeXt image classification is a powerful approach for teaching computers to recognize what appears inside images by using a modern deep-learning architecture. Instead of relying on hand-crafted rules, the model learns directly from large datasets and discovers the visual patterns that define objects, scenes, or categories. This makes ConvNeXt a flexible and accurate foundation for real-world applications such as medical imaging, retail automation, and visual search.
- [Masterclass: Automate Image Labeling with OWL-v2 and Zero-Shot Detection](https://eranfeit.net/how-to-automate-image-labeling-with-owlv2/): Manual data annotation is the primary bottleneck in modern computer vision. Spending hundreds of hours drawing bounding boxes manually is not only expensive but prevents rapid model iteration. In this guide, you will learn how to Automate Image Labeling with OWL-v2 and Zero-Shot Object Detection. By leveraging Google’s Open-World Localization (OWL) transformer, we can detect virtually any object using simple natural language prompts without any task-specific training. We will walk through the technical logic of using Python and Hugging Face to transform raw image directories into labeled datasets instantly.
- [Easy Audio Classification with Transformers & Wav2Vec2](https://eranfeit.net/easy-audio-classification-with-transformers-wav2vec2/): Audio classification with transformers has become one of the most effective ways to understand and analyze sound using modern deep learning. Instead of relying on handcrafted audio features or traditional signal-processing pipelines, transformer-based models learn rich audio representations directly from raw waveforms. This approach allows models to capture both short-term acoustic patterns and longer contextual information in audio signals.
- [How to Fine-tune Vision Transformer (ViT) on Your Own Dataset: A Complete Guide](https://eranfeit.net/fine-tune-vision-transformer-on-your-own-dataset/): To achieve state-of-the-art results in modern image classification, learning how to fine-tune Vision Transformer on custom dataset is a critical skill for any AI developer. While pre-trained models are powerful, specializing them for your specific data is what drives real-world performance. In this tutorial, we will walk through the exact steps to adapt the ViT architecture using your own images, ensuring high accuracy and efficient training
- [Vision Transformer Image Classification PyTorch Tutorial](https://eranfeit.net/vision-transformer-image-classification-pytorch-tutorial/): In the rapidly evolving world of deep learning, the Vision Transformer PyTorch tutorial has become a vital resource for developers looking to move beyond traditional Convolutional Neural Networks (CNNs). Instead of scanning images with spatial filters, Vision Transformers (ViT) treat an image as a sequence of patches, enabling the model to learn global context and long-range dependencies more effectively. This guide provides a hands-on approach to building a ViT classifier from scratch, specifically designed for high-performance results on custom datasets.
- [How to Use Vision Transformer for Image Classification](https://eranfeit.net/how-to-use-vision-transformer-for-image-classification/): Vision Transformer image classification is changing the way computer vision models understand images by treating them as sequences rather than grids of pixels.Instead of relying on convolutional layers, this approach applies transformer architectures—originally designed for natural language processing—directly to visual data.This shift enables models to capture long-range relationships across an image in a more flexible and scalable way.
- [LLaVA Image Recognition in Python with Ollama and Vision Language Models](https://eranfeit.net/llava-image-recognition-in-python-with-ollama-and-vision-language-models/): Understanding LLaVA image recognition Python opens the door to running powerful multimodal artificial intelligence directly from your code. This emerging technology enables developers to combine image inputs with natural language instructions, allowing Python programs to see and understand images the way humans do. Rather than relying solely on traditional computer vision tools, LLaVA merges visual perception and language comprehension into a single intelligent system that can describe, analyze, and answer questions about images.
- [How to Run BLIP-2 Image Analysis with Python](https://eranfeit.net/how-to-run-blip-2-image-analysis-with-python/): Generating human-like descriptions for images no longer requires massive, custom-trained datasets. With the release of Salesforce’s BLIP-2 (Bootstrapping Language-Image Pre-training), developers can leverage frozen image encoders and large language models (LLMs) to achieve state-of-the-art results. In this tutorial, you will solve the challenge of extracting semantic meaning from visuals by learning how to run BLIP-2 for zero-shot image captioning and VQA in Python. Whether you are building an automated accessibility tool or an AI-driven search engine, this guide provides the expert context and technical logic needed to deploy BLIP-2 efficiently using the Hugging Face Transformers library.
- [AI Video Restoration Made Simple for Old Videos](https://eranfeit.net/ai-video-restoration-made-simple-for-old-videos/): AI video restoration has become one of the most practical ways to bring old, low-quality footage back to life.With recent advances in diffusion models and deep learning, it’s now possible to enhance clarity, reduce noise, and recover lost details in videos that were once considered unusable.This shift has made professional-level restoration accessible to developers, researchers, and creators working directly in Python.
- [How to Use AI Face Animation for Lifelike Portraits](https://eranfeit.net/how-to-use-ai-face-animation-for-lifelike-portraits/): Transforming a static portrait into a breathing, speaking avatar used to require a Hollywood-sized VFX budget. Today, you can achieve AI face animation from image using Python with just a few lines of code and the right pre-trained models. Whether you are building an interactive AI assistant or creating dynamic social media content, the challenge lies in making the movement look natural rather than robotic. In this guide, you will solve the "uncanny valley" problem by implementing a robust pipeline that synchronizes audio with facial expressions to create truly lifelike digital human animations.
- [Free AI Image Generator in Python – Build Your Own Text-to-Image Model](https://eranfeit.net/free-ai-image-generator-text-to-image-ai-made-easy/): Free AI image generator in Python — if you're looking for a practical way to generate images from text using AI, this tutorial will walk you through the entire process step by step. You’ll learn how to use modern models like Stable Diffusion to turn simple text prompts into high-quality images, all using Python. By the end of this guide, you’ll be able to build your own text-to-image pipeline, customize outputs, and understand exactly how AI image generation works behind the scenes.
- [How to Use Grounding DINO with Segment Anything Tutorial](https://eranfeit.net/how-to-use-grounding-dino-with-segment-anything-tutorial/): In the world of AI-powered computer vision, combining detection, segmentation, and creative editing in a single pipeline is a major breakthrough. The grounding dino segment anything tutorial introduces precisely such a workflow — allowing you to detect arbitrary objects described in text, segment them precisely, and even manipulate them (for example via inpainting or replacement). This opens a door to powerful applications: from automated annotation and image editing to creative content generation or smart filtering of images.
- [Python Subtitle Generator Faster-Whisper: Create SRT Files Guide](https://eranfeit.net/subtitle-generator-guide-transform-speech-into-text/): Building a Python Subtitle Generator Faster-Whisper is a game-changer for content creators and developers looking for high-speed, accurate transcription. This guide provides a comprehensive workflow to automate speech-to-text conversion using the optimized Faster-Whisper engine. By the end of this tutorial, you will be able to process audio and video files directly into professional SRT subtitle formats with minimal resource usage and maximum precision.
- [AI Object Removal Using Python: A Practical Guide](https://eranfeit.net/ai-object-removal-using-python-a-practical-guide/): AI Object Removal Using Python has quickly become one of the most practical and exciting applications of modern computer vision. It allows anyone—from developers to digital artists—to automatically erase unwanted objects from images without manually editing pixels. Instead of relying on traditional tools like clone stamping or masking, deep learning models can now detect objects with precision, remove them, and intelligently fill in the missing regions. The result is a clean, natural-looking image produced with just a few lines of code.
- [Free Face Swap Tips: Get Realistic Results Easily](https://eranfeit.net/free-face-swap-tips-get-realistic-results-easily/): Free face swap tools make it possible to transform photos and videos with just a few clicks, without paying or installing heavy commercial software. At the most basic level, a free face swap lets you pick a “source” face (your face, a friend, or a stock image) and blend it onto a “target” image or clip. Modern AI models handle the hard work behind the scenes: detecting faces, aligning them, and matching lighting and colors so the final result looks as natural as possible.
- [Image Captioning using PyTorch and Transformers in Python](https://eranfeit.net/image-captioning-using-pytorch-and-transformers-in-python/): Image captioning python is all about teaching a computer to look at a picture and describe it in natural language. Instead of manually writing alt-text or descriptions for every image, you use deep learning models to generate sentences automatically. With a few lines of code in Python, you can load a pre-trained vision–language model, pass in an image, and get a caption like “a dog running on the beach” or “two friends smiling at the camera.” This makes image captioning a powerful tool for accessibility, search, and content automation.
- [Object Detection Heatmap for Tracking Moving Dogs](https://eranfeit.net/object-detection-heatmap-for-tracking-moving-dogs/): To build this YOLO object tracking heatmap, you will need a Python environment with the following libraries installed:
- [YOLOv8 Stanford Dogs Tutorial: XML to YOLO Labels, Train, and Predict](https://eranfeit.net/how-to-train-yolov8-dog-detection-on-stanford-dogs/): Building high-accuracy computer vision models requires more than just code; it requires the right architecture and data synergy. If you are struggling with generic object detection, learning how to train YOLOv8 on the Stanford Dogs dataset is the ultimate way to master fine-grained image classification and detection. In this guide, we solve the challenge of identifying 120 different dog breeds by leveraging transfer learning with the Ultralytics framework. You will move from raw data to a fully functional model capable of distinguishing subtle breed characteristics with high confidence.
- [YOLOv8 YouTube Object Detection in Python (Auto-Label + Live Inference)](https://eranfeit.net/how-to-use-yolov8-for-object-detection-on-youtube-videos/): 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.
- [Automatic Image Annotation with Autodistill and YOLOv8](https://eranfeit.net/automatic-image-annotation-with-autodistill-and-yolov8/): In modern workflows, automatic image annotation usually sits between raw data collection and model training.You gather images or video, run an automatic annotator over them, and get labeled data in formats like YOLO, COCO, or Pascal VOC.From there, you can train custom detectors and segmenters without spending weeks clicking boxes in an annotation tool.The idea is not to completely remove humans, but to move them into a lighter review and correction role instead of every label being drawn from scratch.
- [Train YOLO-NAS on Custom Dataset: A Step-by-Step Aquarium AI Guide](https://eranfeit.net/how-to-train-yolo-nas-on-custom-dataset-aquarium/): 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 this guide, we will solve the common 'thin content' problem by deep-diving into the SuperGradients training pipeline. You will learn how to initialize the AutoNAC-optimized backbone, configure specialized data loaders, and fine-tune hyperparameters to transform raw aquatic imagery into a production-ready vision system.
- [Guide to Object Detection with YOLO-NAS](https://eranfeit.net/guide-to-object-detection-with-yolo-nas/): 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 entire image once and directly predicts bounding boxes and class labels, making it both fast and efficient.
- [YOLOv8 Small Object Detection with SAHI in Python (Sliced Inference)](https://eranfeit.net/how-to-detect-small-objects-with-yolov8-and-sahi/): 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 inference (tiling + merging).
- [How to train YOLOv8 bone fracture detection on X-rays](https://eranfeit.net/how-to-train-yolov8-bone-fracture-detection-on-x-rays/): 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 to oversight. By the end of this walkthrough, you will have a production-ready pipeline that transforms a custom musculoskeletal dataset into a high-accuracy detection model capable of supporting clinical decision-making.
- [YOLOv8 Custom Object Detection: Full Code Walkthrough](https://eranfeit.net/yolov8-custom-object-detection-full-code-walkthrough/): 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 at sea, defects on a production line, or vehicles in satellite imagery. The result is a detector that feels like it was built for your use case, because it actually was.
- [How to Train YOLOv8 Object Detection on a Custom Dataset : Cards detection](https://eranfeit.net/how-to-train-yolov8-object-detection-on-a-custom-dataset-cards-detection/): 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.
- [YouTube Stream Frame Extraction and Real-Time YOLOv8 Detection](https://eranfeit.net/real-time-youtube-video-stream-extraction-and-object-detection/): 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 extraction is the critical first step to ensuring your model stays synced with the live broadcast.
- [How to build yolov8 dental object detection model](https://eranfeit.net/how-to-build-yolov8-dental-object-detection-model/): 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 can detect relevant structures in a single pass, drawing bounding boxes and assigning labels in real time.
- [EigenCAM YOLOv5 Explained: Understanding What YOLOv5 Sees](https://eranfeit.net/how-to-use-eigencam-for-yolov5-object-detection/): This tutorial focuses on EigenCAM YOLOv5 integration to reveal which image regions influence YOLOv5 object detection decisions.
- [How to Train YOLOv5 on a Custom Dataset](https://eranfeit.net/how-to-train-yolov5-on-a-custom-dataset/): Training YOLOv5 on a custom dataset is one of the most powerful ways to move from “toy examples” to real-world computer vision solutions. Instead of relying only on generic datasets like COCO, you can train yolov5 on a custom dataset that reflects your exact problem: boats in aerial footage, vehicles in traffic cameras, products on store shelves, or anything else you care about. This gives you far more control over the classes you detect and the environments your model will see in production.
- [YOLOv5 Object Detection Python Tutorial: Easy 15-Min Setup](https://eranfeit.net/object-detection-in-15-minutes-with-yolov5-python/): Are you struggling to get your first computer vision project off the ground due to complex environment configurations? You are not alone. Setting up deep learning models often feels like a frustrating puzzle, but it doesn't have to be. In this complete YOLOv5 object detection Python tutorial, you will learn exactly how to bypass configuration hell and build a working, real-time image recognition pipeline in just 15 minutes. Whether you aim to track vehicles, detect human faces, or build a smart security camera, this guide provides the exact foundational code, technical logic, and expert tips you need to launch successfully.
- [Getting Started with YOLOX for Object Detection](https://eranfeit.net/getting-started-with-yolox-for-object-detection/): YOLOX is a high-performance object detection model that builds on the YOLO family while rethinking some of its core design choices. Instead of relying on predefined anchor boxes, YOLOX uses an anchor-free approach, which simplifies the detection head and makes training and deployment easier in real-world projects. This design helps YOLOX strike a strong balance between speed and accuracy, making it a solid choice when you need real-time object detection that still delivers competitive results on benchmarks like COCO.
- [How to Use Detr for Smart Bone Fracture Detection](https://eranfeit.net/how-to-use-detr-for-smart-bone-fracture-detection/): Detr (DEtection TRansformer) is a modern approach to object detection that replaces many of the hand-crafted tricks in classic detectors with a clean, transformer-based design. Instead of relying on anchors, custom assignment rules, and complex post-processing, Detr treats detection as a direct set prediction problem: given an image, it predicts a fixed set of bounding boxes and classes in one shot. A convolutional backbone extracts features, a transformer encoder–decoder reasons globally over the scene, and the model outputs final detections without needing non-maximum suppression.
- [How to Train Detectron2 on Custom Object Detection Data](https://eranfeit.net/how-to-train-detectron2-on-custom-object-detection-data/): Train Detectron2 on Custom Dataset in Python to leverage the full power of Facebook AI Research’s state-of-the-art object detection framework. While the official documentation is a great starting point, moving from public benchmarks to your own private data often introduces complex hurdles in dataset registration and configuration. This guide is designed to solve those specific technical bottlenecks, walking you through the end-to-end pipeline—from mapping your custom COCO-formatted annotations to fine-tuning a pre-trained model for high-precision inference on your unique use case.
- [DeepFaceLive Tutorial: Real-time Face Swapping](https://eranfeit.net/deepfacelive-tutorial-real-time-face-swapping/): If you’ve ever wanted to swap your face in real time during a video call or live stream, a DeepFaceLive tutorial is exactly where you start.DeepFaceLive is a Windows application that captures your webcam feed, tracks your facial landmarks, and replaces your face with another one on the fly.Instead of editing a video offline, you see the transformed face live, as you move, talk, and react in front of the camera.
- [FaceFusion Face Swap Is WILD (Full FaceFusion Installation and Tutorial)](https://eranfeit.net/facefusion-face-swap-is-wild-full-facefusion-installation-and-tutorial/): FaceFusion Face Swap takes the classic idea of swapping faces and pushes it into serious, production-grade territory. Instead of a simple filter, you get an “industry leading face manipulation platform” that runs locally, giving you precise control over how faces are detected, aligned, swapped, and enhanced in both images and videos. With FaceFusion Face Swap you can work on high-resolution content, experiment with different models, and keep everything on your own machine for maximum privacy and flexibility.
- [Easy Detectron2 Object Detection Tutorial for Beginners](https://eranfeit.net/easy-detectron2-object-detection-tutorial-for-beginners/): Detectron2 Object Detection Tutorial is the gateway to mastering high-performance computer vision. Developed by Meta (Facebook) AI Research, Detectron2 has become the industry standard for researchers and developers who need a flexible, modular, and fast library for object detection and segmentation tasks.
- [SSD MobileNet v3 Object Detection Explained for Beginners](https://eranfeit.net/ssd-mobilenet-v3-object-detection-explained-for-beginners/): If you’re looking for a practical way to get started with modern deep learning–based object detection, SSD MobileNet v3 object detection is one of the best places to begin.It’s lightweight, fast, and works great even on standard laptops, which makes it perfect for real-world projects, demos, and tutorials.
- [Boost Your Dataset with YOLOv8 Auto-Label Segmentation](https://eranfeit.net/boost-your-dataset-with-yolov8-auto-label-segmentation/): Boost Your Dataset with yolov8 auto-label segmentation and stop wasting time on manual annotations.In this tutorial, we’ll use a pre-trained YOLOv8 segmentation model to automatically detect objects in each video frame, draw high-quality masks, and save labeled outputs you can directly reuse for training or fine-tuning.You’ll see how to process video streams frame by frame, organize segmented instances into class-based folders, and instantly turn raw footage into a structured dataset.Whether you’re working on real-world detection, segmentation, or rapid prototyping, this workflow gives you a clean, scalable way to generate powerful datasets with minimal effort.
- [Fast YOLOv8 Dog Segmentation Tutorial for Video & Images](https://eranfeit.net/fast-yolov8-dog-segmentation-tutorial-for-video-images/): YOLOv8 has quickly become one of the most powerful tools for real-time object detection and segmentation, combining speed, accuracy, and a clean developer experience into one flexible framework. With its segmentation capabilities, you can move beyond simple bounding boxes and generate precise masks that separate objects from their background at the pixel level. In this yolov8 segmentation tutorial, we focus on how to leverage these features in practical projects, so you can build production-ready workflows for images and videos with minimal hassle.
- [YOLOv8 Multi-Class Segmentation Tutorial for Football Analytics](https://eranfeit.net/yolov8-segmentation-tutorial-for-multi-class-football/): Traditional object detection often fails in sports analytics because bounding boxes overlap in crowded scenes, making it impossible to calculate precise player distances or pitch coverage. To solve this, we must move to pixel-level understanding. In this YOLOv8 Multi-Class Segmentation Tutorial for Football Analytics, you will learn how to build a model that doesn’t just see players, but understands the exact boundaries of the pitch, the ball, and the athletes. We will walk through building an end-to-end pipeline that transforms raw football footage into a rich, segmented data source ready for professional tactical analysis.
- [YOLOv8 Segmentation Tutorial for Real Flood Detection](https://eranfeit.net/yolov8-segmentation-tutorial-for-real-flood-detection/): YOLOv8 isn’t just a detection network.It also offers a powerful segmentation head that lets you generate pixel-level masks in real time, which is exactly what we need for fast, actionable flood mapping.In this yolov8 segmentation tutorial, you’ll walk through a complete, production-style pipeline: preparing a flood dataset, converting binary masks into YOLO polygon labels, splitting the data, wiring a clean config.yaml, training a one-class YOLOv8-seg model, and finally generating high-quality flood masks on new images.Everything is written in practical Python, kept readable, and structured so you can adapt it to any other water, damage, or environmental segmentation task without rewriting your entire stack.
- [Quick Yolov5 segmentation tutorial in minutes](https://eranfeit.net/quick-yolov5-segmentation-tutorial-in-minutes/): YOLOv5 is one of the most popular deep learning frameworks for real-time object detection, and for good reason.It’s fast, lightweight, and flexible, making it a great fit for everything from quick experiments on your laptop to full production pipelines running on GPUs or edge devices. This Yolov5 segmentation tutorial walks you through environment setup and real examples so you can create clean, production-ready masks.Instead of treating detection as a slow, multi-stage process, YOLOv5 predicts bounding boxes and class probabilities in a single pass, which is exactly why it’s called “You Only Look Once.”
- [YOLOv8 + SAM in Python: Fast, Clean Segmentation Masks](https://eranfeit.net/segment-anything-tutorial-generate-yolov8-masks-fast/): YOLOv8 SAM segmentation Python is a simple “detect then segment” workflow: YOLOv8 finds the object, and Segment Anything (SAM) turns that box into a clean pixel-accurate mask. In this tutorial, you’ll run the full pipeline in Python, visualize the masks, and learn the small details that keep results aligned and sharp.
- [One-Click Segment Anything in Python (SAM ViT-H)](https://eranfeit.net/one-click-segment-anything-in-python-sam-vit-h/): Segment Anything in Python lets you segment any object with a single click using SAM ViT-H, delivering three high-quality masks instantly.In this tutorial, you’ll set up the environment, load the checkpoint, click a point, and export overlays—clean, practical code included.Whether you’re labeling datasets or prototyping, this one-click workflow is quick, reliable, and easy to reuse.
- [Segment Anything Python — No-Training Image Masks](https://eranfeit.net/segment-anything-python-no-training-image-masks/): Generating high-quality training data is often the biggest bottleneck in computer vision. In this Segment Anything Python tutorial, you will solve the problem of manual image labeling by leveraging Meta’s SAM model to produce pixel-perfect masks instantly. Instead of spending weeks annotating datasets or training custom U-Net architectures, you will learn how to implement a zero-shot segmentation pipeline that works on any object right out of the box. Whether you are building an automated labeling tool or a creative AI app, this guide provides the exact implementation logic needed to extract masks without a single line of training code.
- [Segment Anything Tutorial: Fast Auto Masks in Python](https://eranfeit.net/segment-anything-tutorial-fast-auto-masks-in-python/): In this Segment Anything tutorial, we’ll demystify Meta’s foundation model for image segmentation and show you how to get production-ready masks with just a few lines of Python.Segment Anything (SAM) is designed to generalize: it can segment any object in an image—even ones it has never seen during training—using simple prompts or fully automatic mask generation.
- [Detectron2 custom dataset Training Made Easy](https://eranfeit.net/detectron2-custom-dataset-training-made-easy/): Detectron2 custom dataset training means taking your own images (not COCO), labeling them with polygon masks, registering them in Detectron2, and fine-tuning Mask R-CNN so it can detect and segment your specific objects.In this tutorial, we’ll walk through that full process using a fruit dataset (apples, bananas, grapes, strawberries, oranges, lemons): annotation, COCO export, dataset registration, training on Windows CPU and Ubuntu/WSL GPU, and finally inference on new test images.By the end, you’ll have a working instance segmentation model that was trained on your data, not a generic dataset — and you’ll actually see it draw masks around your objects.
- [Detectron2 Panoptic Segmentation Made Easy for Beginners](https://eranfeit.net/detectron2-panoptic-segmentation-made-easy-for-beginners/): Understanding a visual scene requires more than just drawing boxes around cars; it requires identifying every pixel, from the individual vehicles to the road and sky. In this Detectron2 Panoptic Segmentation Python Tutorial, you will solve the complex problem of 'complete scene understanding.' While instance segmentation tracks objects (things) and semantic segmentation labels regions (stuff), panoptic segmentation merges them into a single, unified output. By the end of this guide, you’ll have a production-ready Python script that leverages Facebook AI Research’s (FAIR) Detectron2 to perform pixel-perfect analysis on any image.
- [Make Instance Segmentation Easy with Detectron2](https://eranfeit.net/make-instance-segmentation-easy-with-detectron2/): Detectron2 instance segmentation gives you pixel-accurate masks for every object in an image, not just bounding boxes, so you can measure areas, crop precisely, and visualize scenes clearly. Built on PyTorch, Detectron2 ships with reliable Mask R-CNN configs from the model zoo, letting you go from setup to first results in minutes. You can begin on CPU for quick tests and then switch to CUDA for speed, while keeping the exact same workflow. With sensible defaults, clean APIs (e.g., DefaultPredictor and Visualizer), and easy dataset registration, Detectron2 instance segmentation is a practical choice for both beginners prototyping and teams pushing models into production.
- [Complete YOLOv8 Classification Tutorial for Beginners](https://eranfeit.net/complete-yolov8-classification-tutorial-for-beginners/): The YOLOv8 classification model follows a CNN-based encoder-only design, leveraging the core principles of feature extraction, aggregation, and classification.At a high level, it consists of three key parts:
- [YOLOv8 Tutorial : Build a Car Image Classifier](https://eranfeit.net/yolov8-tutorial-build-a-car-image-classifier/): YOLOv8, developed by Ultralytics, represents the latest evolution of the renowned “You Only Look Once” family of deep learning models for object detection, segmentation, and classification.It’s a highly efficient, real-time architecture that balances speed, accuracy, and ease of use, making it one of the most powerful computer vision frameworks available today — and the focus of this YOLOv8 tutorial.
- [YOLOv5 Image Classification — Complete Tutorial](https://eranfeit.net/yolov5-image-classification-complete-tutorial/): In this tutorial, you’ll build a complete YOLOv5 image classification pipeline using the Animals10 dataset — a collection of over 26,000 animal photos categorized into 10 species such as cats, dogs, horses, squirrels, and butterflies.We’ll start from environment setup, continue through dataset preparation and model training, and finish by predicting new images interactively with OpenCV.
- [VGG19 Transfer Learning Explained for Beginners](https://eranfeit.net/vgg19-transfer-learning-explained-for-beginners/): Transfer learning has become one of the most effective techniques in deep learning for achieving great accuracy without starting from scratch.In this tutorial, we’ll explore how to apply VGG19 transfer learning using TensorFlow and Keras on an Aerospace Images dataset — a collection of aircraft, balloons, and flying machines that’s perfect for demonstrating image classification.
- [Build an Image Classifier with Vision Transformer](https://eranfeit.net/build-an-image-classifier-with-vision-transformer/): In this tutorial, we’ll dive into how to use the Vision Transformer (ViT) — a model that has changed how computers “see” images.We’ll not only walk through a working Python example step-by-step, but also explain what makes the Vision Transformer image classification approach so effective.
- [מסלול טיול לפורטוגל 7 ימים  ליסבון, פורטו ,סינטרה , נזרה , ועמק הדורו בשבוע אחד](https://eranfeit.net/%d7%9e%d7%a1%d7%9c%d7%95%d7%9c-%d7%98%d7%99%d7%95%d7%9c-%d7%9c%d7%a4%d7%95%d7%a8%d7%98%d7%95%d7%92%d7%9c-7-%d7%99%d7%9e%d7%99%d7%9d-%d7%9c%d7%99%d7%a1%d7%91%d7%95%d7%9f-%d7%a4%d7%95%d7%a8%d7%98/): מדובר במסלול טיול לפורטוגל 7 ימים , מפורט מאד , ומכיל את כל האטרקציות שביקרנו , מלונות , מסעדות וכו
- [How to Build a DenseNet201 Model for Sports Image Classification](https://eranfeit.net/how-to-build-a-densenet201-model-for-sports-image-classification/): In this tutorial, we’ll walk through building a sports image classification model that can automatically recognize Olympic sports from images.
- [Alien vs Predator Image Classification with ResNet50 (Tutorial)](https://eranfeit.net/alien-vs-predator-image-classification-with-resnet50-complete-tutorial/): Deep learning has completely transformed the way we classify and recognize images.In this tutorial, we’ll walk through building and training an Alien vs Predator image classifier using ResNet50, one of the most popular pre-trained convolutional neural networks.Our goal is to leverage transfer learning to distinguish between two visually unique categories: aliens and predators.
- [How to classify 525 Bird Species using Inception V3 and TensorFlow](https://eranfeit.net/how-to-classify-525-bird-species-using-inception-v3-and-tensorflow/): In this guide you will build a full image classification pipeline using Inception V3. You will prepare directories, preview sample images, construct data generators, and assemble a transfer learning model.You will compile, train, evaluate, and visualize results for a multi-class bird species dataset.This tutorial embeds best practices that highlight how the inception v3 architecture scales depth and width while controlling computation.
- [How to perform a face Swap in a Video with Your Own image using Roop](https://eranfeit.net/face-swap-in-a-video-with-your-own-image-using-roop/): If you’ve ever wanted to drop your face into a video—cleanly and with minimal setup—this guide walks you through Roop, a simple face-swap tool you can run locally. We’ll cover the full workflow: creating a clean Conda environment, installing the exact dependencies, preparing your image and target video, and running Roop on both CPU and GPU. By the end, you’ll press Start, watch the swap happen, and know how to fix the most common snags along the way.
- [Olympic Sports Image Classification with EfficientNetV2](https://eranfeit.net/olympic-sports-image-classification-with-tensorflow-efficientnetv2/): Image classification is one of the most exciting applications of computer vision. It powers technologies in sports analytics, autonomous driving, healthcare diagnostics, and more.
- [מסלול טיול בסלובקיה ודרום פולין למשפחה (11 ימים): הרי הטטרה, מערות, טירות, רכבלים ,אגמים, פסגות, ורכבות הרים](https://eranfeit.net/amazing-trip-to-slovakia-hebrew/): הטיול מסכם שני ביקורים שלנו בסלובקיה הנהדרת , כאשר פעם אחת נסענו לבד ופעם נסענו עם הילדים
- [How to Classify images using Efficientnet B0](https://eranfeit.net/how-to-classify-images-using-efficientnet-b0/): In this tutorial, we’ll explore how to use EfficientNetB0, a powerful deep learning model available in TensorFlow and Keras, for image classification. EfficientNet models are known for their efficiency and accuracy, making them an excellent choice for tasks like classifying objects in images.
- [How To Actually Use MobileNetV3 for Fish Classifier](https://eranfeit.net/how-to-actually-use-mobilenetv3-for-fish-classifier/): This is a transfer learning tutorial for image classification using TensorFlow involves leveraging pre-trained model MobileNet-V3 to enhance the accuracy of image classification tasks.
- [Create Stunning AI Music with AudioCraft (Step-by-Step Guide)](https://eranfeit.net/watch-a-song-composed-by-artificial-intelligence/): AI music generation has become practical, fast, and genuinely creative.With Meta’s AudioCraft (and its MusicGen models), you can go from a short text prompt — or even a guiding melody — to a polished audio sample in minutes using an AI music generator.This post walks you through a reliable setup, a browser-based workflow, and a melody-guided two-step process you can reuse for any style.
- [From Photo to Speaker: The Ultimate Guide to SadTalker AI](https://eranfeit.net/lip-sync-bring-your-image-to-life-using-sadtalker/): SadTalker tutorial: Bringing your portraits to life has never been easier. Imagine taking a static photo of a historical figure, a digital character, or even yourself, and making it speak with realistic facial expressions and head movements. In the past, this required a professional animation studio. Today, thanks to the SadTalker open-source project, you can achieve professional results for free on your own computer.
- [How To Actually Fine-Tune MobileNetV2 | Classify 9 Fish Species](https://eranfeit.net/how-to-actually-fine-tune-mobilenetv2-classify-9-fish-species/): In this hands-on video, I’ll show you how I built a deep learning model that can classify 9 different species of fish using MobileNetV2 and TensorFlow 2.10 — all trained on a real dataset!
- [My Kraków Travel Guide: Best Places to Eat, Stay & Explore](https://eranfeit.net/my-krakow-travel-guide-best-places-to-eat-stay-explore/): Planning a trip to Kraków, Poland? check my Kraków Travel Guide
- [קרקוב טיפים והמלצות  – מדריך למטייל לקרקוב : מלונות , אטרקציות , מסעדות ומקומות טעימים](https://eranfeit.net/krakow-krakow-travel-guide-best-places-to-eat-stay-explore-hebrew/): בהמשך המייל רכזתי רשימת מלונות , בסמוך לאטרקציות בקרקוב , שאני ממליץ עליהם בחום , ונוהג לחזור לשם בכל ביקור
- [Free Image Upscaler : How to Enhance Photos and Videos with AI Super Resolution](https://eranfeit.net/upscale-your-images-and-videos-using-super-resolution/): If you’ve ever tried to enlarge a photo, you know how quickly it becomes blurry and pixelated. A free image upscaler solves this by using AI super resolution to make your images and videos sharper, clearer, and higher in resolution—without losing quality. In this guide, I’ll show you exactly how a free image upscaler works, compare the best online options, and explain how AI tools can improve your visuals in just a few clicks.
- [Super-Quick Image Classification with MobileNetV2](https://eranfeit.net/super-quick-image-classification-with-mobilenetv2/): Image Classification with MobileNetV2
- [Amazing Color Transfer between Images](https://eranfeit.net/amazing-color-transfer-between-images/): Color transfer is a practical method to change the appearance of a source image according to the color patternof a target image.This program is the implementation of the paper Color Transfer between Images by Erik Reinhard, Michael Ashikhmin, Bruce Gooch and Peter Shirley.
- [Self-Supervised Learning Made Easy with LightlyTrain | Image Classification tutorial](https://eranfeit.net/self-supervised-learning-made-easy-with-lightlytrain-image-classification-tutorial/): In this tutorial, we will show you how to use LightlyTrain to train a model on your own dataset for image classification.
- [Animate Face Photo Free with TPSMM: Realistic AI Face Animation](https://eranfeit.net/transform-static-images-into-lifelike-animations/): Animate your face photo free — give life to static images with motion, expressions, or even lip sync. In this post, you’ll learn how to turn a single face photo into a dynamic animated portrait—for free.
- [How to Classify Vehicles: VGG16 Feature Extraction & XGBoost](https://eranfeit.net/object-classification-using-xgboost-and-vgg16-classify-vehicles-using-tensorflow/): In this tutorial, we build a vehicle classification model using VGG16 for feature extraction and XGBoost for classification! 🚗🚛🏍️
- [Malaria Cell Classification CNN in TensorFlow (Keras) – Full Tutorial](https://eranfeit.net/how-to-classify-malaria-cells-using-convolutional-neural-network/): malaria cell classification CNN is a practical way to learn how convolutional neural networks recognize patterns in microscope cell images. In this tutorial, you’ll build a complete pipeline in TensorFlow and Keras: preprocessing infected vs uninfected cell images, training a compact CNN, saving the best checkpoint, and running inference on a brand-new test image.
- [How to segment X-Ray lungs using UNet and Tensorflow](https://eranfeit.net/how-to-segment-x-ray-lungs-using-u-net-and-tensorflow/): This article provides a comprehensive hands-on guide to Medical Image Segmentation with U-Net and TensorFlow: A Lung X-Ray Tutorial, focusing on the intersection of deep learning and clinical diagnostics. We dive deep into the technical implementation of a neural network designed specifically to identify and isolate anatomical structures within chest radiographs. By shifting from simple classification to pixel-level precision, you will learn how to build a system capable of assisting medical professionals in identifying lung boundaries with mathematical accuracy.
- [TensorFlow U-Net for Skin Lesion Segmentation (Melanoma / ISIC 2018)](https://eranfeit.net/medical-melanoma-detection-tensorflow-u-net-tutorial-using-unet/): One of the most frequent hurdles when implementing a Medical Image Segmentation TensorFlow U-Net Tutorial is the "Empty Mask" phenomenon, where the model predicts a completely black image. This usually occurs because of a severe class imbalance; the background pixels far outnumber the melanoma pixels, leading the model to find a local minimum by simply predicting "background" for everything. To fix this, you should replace the standard accuracy metric with a Dice Loss or Focal Loss function. These metrics are specifically designed to penalize the model more heavily for missing the minority class, forcing the U-Net to prioritize the lesion's boundaries.
- [U-Net Image Segmentation with TensorFlow/Keras (Oxford-IIIT Pets)](https://eranfeit.net/creating-an-animal-segmentation-model-with-u-net-and-tensorflow-keras/): This tutorial provides a step-by-step guide on how to implement and train a U-Net Image Segmentation TensorFlow .
- [U-Net Image Segmentation Tutorial | Deep Learning Image Segmentation Guide](https://eranfeit.net/u-net-image-segmentation-how-to-segment-persons-in-images/): This tutorial demonstrates a complete U-Net image segmentation workflow. It is designed as a practical image segmentation tutorial, showing how deep learning image segmentation can be applied to
- [U-net Medical Segmentation with TensorFlow and Keras (Polyp segmentation)](https://eranfeit.net/u-net-medical-segmentation-with-tensorflow-and-keras-polyp-segmentation/): This tutorial provides a step-by-step guide on how to implement and train a U-Net model for polyp segmentation using TensorFlow/Keras.
- [Build a CNN Model for Retinal Image Diagnosis](https://eranfeit.net/build-a-cnn-model-for-retinal-image-diagnosis/): How to gather and preprocess a dataset of over 80,000 retinal images, build a CNN model , and train it that can accurately distinguish between these health categories.
- [120 Dog Breeds, more than 10,000 Images: Deep Learning Tutorial for dogs classification](https://eranfeit.net/120-dog-breeds-more-than-10000-images-deep-learning-tutorial-for-dogs-classification/): 📽️ In our latest video tutorial, we will create a dog breed recognition model using the NasLarge pre-trained model 🚀 and a massive dataset featuring over 10,000 images of 120 unique dog breeds 📸.
- [5-Day Ireland Itinerary: The Perfect Route for Cliffs, Castles, Pubs & Wild Atlantic Views](https://eranfeit.net/unforgettable-trip-to-ireland-full-itinerary/): • 5-Day Ireland Itinerary: Cliffs, Castles, Pubs & Wild Atlantic Viewshttps://eranfeit.net/unforgettable-trip-to-ireland-full-itinerary/
- [A Revolutionary Way to Play Atari 2600 – ( Play Atari 2600 with AI )](https://eranfeit.net/a-revolutionary-way-to-play-atari-2600-using-ai/): This new, amazing concept of playing Atari 2600 games is based on using your hands instead of a joystick. Play Atari with AIEvery movement in the game is done by the hands. You can control the game with no joystick. Yes, no Joystick!!!
- [Dinosaur Image Classification with a Convolutional Neural Network (Keras/TensorFlow)](https://eranfeit.net/dinosaur-image-classification-with-cnn-tutorial/): Building an accurate computer vision model from scratch requires more than just compiling code; it demands an optimized data pipeline and a robust network architecture. In this comprehensive dinosaur image classification cnn python tutorial, you will solve the specific challenge of classifying high-variance prehistoric species using deep learning. You will learn how to load a structured image dataset from local directories, apply normalization, and design a custom convolutional neural network (CNN) in TensorFlow and Keras. This workflow eliminates training bottlenecks and safeguards against overfitting, converting messy image folders into predictions you can trust.
- [מסלול טיול בצפון יוון למשפחות – רעיונות ואטרקציות מובילות](https://eranfeit.net/our-amazing-trip-to-greece-hebrew-version/): הי , אני משתף את סיכום הטיול המשפחתי שלנו בצפון יוון
- [Deep Learning for Skin Melanoma Segmentation: A Res-UNet Approach in Python](https://eranfeit.net/how-to-segment-skin-melanoma-using-res-unet/): Early detection of skin cancer is critical, yet manual lesion tracing is time-consuming and prone to human error. This guide provides a robust technical solution for Deep Learning Skin Melanoma Segmentation using Res-UNet, an advanced architecture that combines the strengths of Residual Blocks with the UNet framework. By following this tutorial, you will solve the challenge of accurately isolating malignant boundaries in dermatoscopic images. We will move beyond simple classification to achieve pixel-level precision, ensuring your computer vision models are equipped for real-world clinical datasets where boundary clarity is paramount.
- [How to build a Facial Emotion Detection Model using Deep learning](https://eranfeit.net/cnn-model-for-emotion-detection-tensorflow-and-keras-tutorial-%f0%9f%96%a5%ef%b8%8f%f0%9f%a4%94%f0%9f%98%80/): Emotion detection has become one of the most exciting applications of computer vision and deep learning.
- [OpenCV Coin Detection in Python with Canny and Contours](https://eranfeit.net/easy-coin-detection-with-python-and-opencv/): This tutorial shows how to perform OpenCV coin detection in Python using Canny edge detection and contour detection.
- [Northern Greece Itinerary: Athens, Meteora, Tzoumerka, Ioannina & Nafpaktos (7 Days)](https://eranfeit.net/my-amazing-trip-to-greece/): Planning a northern Greece itinerary that goes beyond the islands?This guide takes you from Athens to Meteora’s monasteries, through Tzoumerka’s rafting canyons and Ioannina’s lakeside old town, finishing on the beaches of Nafpaktos, and back to Athens where I focus on great places for visiting and eating.
- [CNN Feature Visualization with Activation Maximization in Keras (VGG16)](https://eranfeit.net/what-the-network-thinks-is-the-best-image-for-the-cnn-model-class-maximization-tutorial/): Have you ever wondered exactly what a deep learning model visualizes when it detects a specific category? In this comprehensive cnn class activation maximization tutorial, you will look inside the black box of neural networks using Python and Keras. Instead of just passing an image through a pre-trained network like VGG16, you will use gradient ascent in the input space to numerically generate the "ideal" image for a targeted class. By the end of this guide, you will master how to extract, optimize, and visualize learned representations, turning abstract weights into interpretable visual patterns.
- [Find Waldo with OpenCV Template Matching in Python](https://eranfeit.net/learn-how-to-find-wally-in-images-using-python-and-opencv/): This tutorial shows how to use OpenCV template matching in Python to find a smaller image inside a larger image.We will solve a fun Where’s Waldo style problem using a single, simple algorithm.The code reads two images, converts the scene to grayscale, and runs cv2.matchTemplate to measure visual similarity.We then extract the best match location and draw a bounding box to highlight Waldo in the scene.This approach is fast, easy to understand, and requires no training data.It is perfect for quick image matching tasks and for learning the fundamentals of search by template in computer vision.
- [Real-Time Object Detection in Python with Voice Commands (OpenCV + YOLOv4-tiny)](https://eranfeit.net/how-to-build-a-real-time-object-detection-with-your-own-voice/): This section prepares all dependencies, loads the YOLOv4-tiny model into OpenCV’s DNN module, configures input size and scale, and reads the classes.txt file to map detections to human-readable labels.It establishes the foundation for real-time object detection in Python and ensures class names are available for your voice filter.
- [Text detection with Python and Opencv | OCR using EasyOCR](https://eranfeit.net/text-detection-with-python-and-opencv-ocr-using-easyocr-computer-vision-tutorial/): This Python script demonstrates how to perform text recognition in image files using the combined power of OpenCV, EasyOCR, and Matplotlib.
- [OpenCV Image Comparison in Python: Detect Differences Like a Pro.](https://eranfeit.net/advanced-python-and-opencv-tutorial-how-to-find-differences-in-similar-images/): This tutorial shows how to build an OpenCV image comparison pipeline in Python that detects visual differences between two images and highlights them with bounding boxes.You will learn how to preprocess the input, isolate the two pictures inside a collage, align their sizes, compute pixel-wise differences, reduce noise, and extract clean contours of the changed regions.The final result is a simple and effective “spot the difference” solution that is fast, easy to understand, and production-ready for many computer vision tasks.
- [Advanced Motion Detection in Video with OpenCV and Python (MOG2 Optimization Guide)](https://eranfeit.net/how-to-detect-moving-objects-in-video-using-opencv-and-python/): Implementing advanced motion detection in video with OpenCV is a fundamental milestone for computer vision engineers building traffic monitoring or automated surveillance pipelines. While basic background subtraction looks simple on paper, deploying it to production requires handling physical environmental noise, dynamic lighting shifts, and hardware limitations. In this guide, we will optimize Python scripts to track moving objects cleanly and efficiently.
- [How to Extract Text from Scanned Books with Python and OpenCV](https://eranfeit.net/extracting-words-from-scanned-books-a-step-by-step-tutorial-with-python-and-opencv/): Optical Character Recognition (OCR) is a technology used to extract text from images which is used in applications like document digitization, license plate recognition and automated data entry.
- [Image Segmentation in OpenCV with Python and Contours](https://eranfeit.net/image-segmentation-in-opencv-with-python-and-contours/): In this tutorial, you will learn a practical pipeline for OpenCV image segmentation in Python.We will convert an image to grayscale, apply a smart binary threshold, detect contours, and then build a mask to extract the main object.This workflow is fast, reproducible, and ideal for object extraction, background removal, and preprocessing for computer vision tasks.The example focuses on choosing the largest contour to isolate the dominant subject in the scene.
- [Python Image Segmentation Made Easy with OpenCV and K-means Algorithm](https://eranfeit.net/python-image-segmentation-made-easy-with-opencv-and-k-means-algorithm/): Image segmentation is the process of partitioning an image into meaningful regions (segments) — for example separating foreground from background, isolating objects, or splitting an image into zones of similar color or texture. One classic and relatively simple technique for segmentation is K-means clustering applied in color (or feature) space.
- [How to install Mediapipe on the Jetson Nano ?](https://eranfeit.net/how-to-install-mediapipe-on-the-jetson-nano/): This tutorial elaborates how to install MediaPipe on the Jetson Nano.
- [Real-Time Image Classification with Jetson Nano and OpenCV — Complete Guide](https://eranfeit.net/how-to-classify-objects-in-live-camera-using-jetson-nano/): This guide is focused on image classification with Jetson Nano and OpenCV.By following along, you will learn the basics of combining OpenCV with NVIDIA Jetson’s jetson.inference library, which is designed for high-performance AI tasks on edge devices.
- [Jetson Nano Video Classification Python: Real-Time GoogLeNet Tutorial](https://eranfeit.net/how-to-classify-objects-in-videos-using-jetson-nano-opencv-python/): In this masterclass, we will implement a high-performance Jetson Nano video classification Python OpenCV pipeline. By offloading the heavy mathematical lifting of deep learning to the NVIDIA Maxwell GPU via TensorRT, we achieve real-time speeds that were previously impossible on low-power hardware.
- [Mastering Edge AI: Real-Time Object Classification with Jetson Nano and OpenCV](https://eranfeit.net/how-to-classify-objects-using-jetson-nano-inference-and-opencv/): Mastering Jetson Nano object classification is the key to building powerful, independent AI systems on the edge. While many developers struggle with slow frame rates and high latency, this guide focuses on utilizing the full potential of NVIDIA’s hardware. By integrating TensorRT inference with OpenCV, you will transform your Jetson Nano into a high-speed vision engine capable of identifying objects in real-time. Whether you are a computer vision engineer or a community enthusiast, this step-by-step tutorial provides the professional framework needed for efficient deep learning deployment.
- [Getting started with Jetson Nano | How to install Jetson Nano inference tutorial to classify objects](https://eranfeit.net/getting-started-with-jetson-nano-how-to-install-jetson-nano-inference-tutorial-to-classify-objects/): Setting up Jetson Nano correctly is the fastest way to start deploying AI models at the edge. In this guide you will install all required developer tools, clone NVIDIA’s jetson-inference repository, and compile it from source. You will also see how to install Python development headers and NumPy to enable clean bindings and examples. Finally, you will perform a tidy system registration with ldconfig, and optionally add Visual Studio Code for a smoother workflow. Let’s dive into the Jetson Nano setup process.
- [What actually sees a CNN Deep Neural Network model ?](https://eranfeit.net/what-actually-sees-a-cnn-deep-neural-network-model/): Welcome to our comprehensive image classification tutorial series! In this tutorial playlist, consisting of five informative videos, we will guide you through the entire process of classifying monkey species in images, utilizing vgg16 transfer learning and CNN filter visualization.
- [How to classify monkeys images using convolutional neural network , Keras tuner hyper parameters , and transfer learning ? (part4)](https://eranfeit.net/how-to-classify-monkeys-images-using-convolutional-neural-network-keras-tuner-hyper-parameters-and-transfer-learning-part4/): Welcome to our comprehensive image classification tutorial series! In this tutorial playlist, consisting of five informative videos, we will guide you through the entire process of classifying monkey species in images, utilizing vgg16 transfer learning.
- [Keras Tuner Hyperparameter Optimization: A Guide to High-Accuracy CNNs](https://eranfeit.net/how-to-classify-monkeys-images-using-convolutional-neural-network-keras-tuner-hyper-parameters-and-transfer-learning-part3/): Building a deep learning model is only the first step; the real challenge lies in finding the exact configuration that yields the highest accuracy. Keras Tuner Hyperparameter Optimization is the professional standard for automating this process, replacing manual trial-and-error with sophisticated search algorithms like Bayesian Optimization. In this guide, you will solve the common problem of 'model plateauing' by learning how to dynamically tune learning rates, layer units, and dropout rates. We will implement these techniques specifically for a monkey species image classification task, ensuring your model generalizes perfectly to real-world data.
- [How to classify monkeys images using convolutional neural network , Keras tuner hyper parameters , and transfer learning ? (part2)](https://eranfeit.net/how-to-classify-monkeys-images-using-convolutional-neural-network-keras-tuner-hyper-parameters-and-transfer-learning-part2/): This is part of our TensorFlow image classification tutorial series!
- [How to classify monkeys ? (part1)](https://eranfeit.net/how-to-classify-monkeys-images-using-convolutional-neural-network-keras-tuner-hyper-parameters-and-transfer-learning-part1/): Welcome to our comprehensive image classification tutorial series! In this tutorial playlist, consisting of five informative videos, we will guide you through the entire process of classifying monkey species in images.
- [ResNet50 Transfer Learning for Car Image Classification in TensorFlow and Keras.](https://eranfeit.net/resnet50-tutorial-classifying-car-images-with-transfer-learning-and-tensorflow/): Are you struggling to train an accurate image classifier on a limited dataset? In this complete resnet50 car classification tutorial with tensorflow, you will solve this problem by leveraging deep learning transfer learning. Instead of spending days training complex convolutional neural networks from scratch, we will repurpose the powerful ImageNet-trained weights of the ResNet50 architecture. By the end of this practical guide, you will have a functional, end-to-end Python script that loads, trains, and evaluates a highly accurate car classification model capable of distinguishing vehicle types or brands with minimal computational resources.
- [MobileNet Image Classification in Python: Complete Keras & OpenCV Tutorial](https://eranfeit.net/tensorflow-transfer-learning-classify-images-with-mobilenet-and-python/): This Keras MobileNet tutorial assembles a complete MobileNet image classification pipeline using a pretrained MobileNet Python setup in TensorFlow.It is a practical Python computer vision walkthrough that covers image loading, resizing, normalization, inference, and result visualization end to end.By loading weights="imagenet", the model is ready for TensorFlow image classification across the 1000 ImageNet categories without extra training.Your MobileNet classify image script performs four core stages: image loading and resizing, normalization with preprocess_input, model inference with model.predict, and human-readable decoding via decode_predictions.Finally, it overlays the best class label on the original image using an OpenCV putText example to give immediate visual confirmation.
- [How to Classify Landmarks Using TensorFlow, TensorHub, and Python](https://eranfeit.net/how-to-classify-landmarks-using-tensorflow-tensor-hub-and-python/): When building deep learning models, one of the biggest challenges is training from scratch — it’s time-consuming, requires a massive dataset, and demands significant computational power.That’s where TensorFlow Hub (often shortened to TensorHub) comes in.
- [How to Build a CNN for Chess Piece Image Classification](https://eranfeit.net/building-a-cnn-model-for-chess-piece-recognition-in-python-and-tensorflow/): This end-to-end tutorial shows how to build a complete image classification pipeline in Python using TensorFlow Keras, focusing specifically on image classification with keras.You will prepare the dataset folders, split images into train and validation sets, build and train a convolutional neural network (CNN) with augmentation and callbacks, and finally run single-image predictions with OpenCV visualization.The example uses a chess piece dataset with six classes and demonstrates best practices like normalization, data augmentation, EarlyStopping, and saving the best model for reproducible results.
- [How to classify weather scenes using TensorFlow, Keras, and Transfer learning – Vgg19 ?](https://eranfeit.net/how-to-classify-weather-scenes-using-tensorflow-keras-and-transfer-learning-vgg19/): In this post, we’ll build a complete weather image classification pipeline in Python using Keras with a VGG19 backbone and explore Keras VGG19 transfer learning.You’ll see how to split raw images into train and validation sets, set up data augmentation, attach a lightweight classification head to VGG19, train and evaluate with callbacks, and finally run predictions on unseen images.
- [How to build a Keras CNN for Weather Image Classification](https://eranfeit.net/%f0%9f%8c%a6%ef%b8%8f-cnn-tensorflow-image-classification-weather-image-classification-%f0%9f%8c%a6%ef%b8%8f/): This tutorial shows a complete TensorFlow image classification pipeline using Keras CNN.You will learn how to split a weather photo dataset into train and validation sets.You will apply image data augmentation with Keras ImageDataGenerator to improve generalization.You will build and train a deep convolutional neural network for five weather classes.You will visualize accuracy and loss, save the best model, and run fast batch predictions on test images.
- [How to build a Brain Tumor Classification using Deep learning](https://eranfeit.net/brain-tumor-classification-using-deep-learning/): This tutorial builds a complete deep learning pipeline for brain tumor classification from brain MRI images using Python and Keras.You will organize the dataset into train, validation, and test splits to ensure reliable medical image classification results.A compact CNN architecture will be trained with image augmentation, binary cross-entropy, and the Adam optimizer for stable convergence.To improve generalization, the training loop applies EarlyStopping and ModelCheckpoint so only the best model is saved.You will visualize accuracy and loss curves to track performance and prevent overfitting.
- [TensorFlow Image Classification Tutorial: Flower Recognition with Keras](https://eranfeit.net/tensorflow-cnn-tutorial-flower-classification-with-python-%f0%9f%8c%b7/): In this comprehensive TensorFlow Image Classification Tutorial, we will explore how to build and deploy a robust deep learning model to recognize various flower species using Python. Image classification is a fundamental pillar of Computer Vision, and by leveraging the Keras API within TensorFlow, we can develop powerful Convolutional Neural Networks (CNN) with high accuracy. This guide walks you through the entire pipeline: from advanced data augmentation with ImageDataGenerator to real-time interactive predictions using OpenCV. Whether you are building a model from scratch or optimizing for speed, this tutorial provides the production-ready code you need.
- [How to classify images using MobileNet Tensorflow and ResNet50](https://eranfeit.net/tensorflow-image-classification-tutorial-resnet50-vs-mobilenet/): This tutorial shows how to perform TensorFlow image classification using Keras pretrained models—specifically MobileNetV2 and ResNet50—without any additional training with mobilenetv2 tensorflow.You’ll learn how to load a single image, preprocess it to the expected input shape, run inference against ImageNet weights, and translate the numeric outputs into human-readable labels using decode_predictions.
- [How To Build Sports Image Classification Model Using MobileNet](https://eranfeit.net/tensorflow-sports-image-classification-building-a-custom-model-with-tensorflow-and-keras/): In the rapidly evolving world of sports analytics, the ability to automatically identify athletic disciplines in visual data is a game-changer. This tutorial provides a comprehensive guide to build a custom sports image classifier with TensorFlow and Keras. Whether you are automating highlights or organizing massive datasets, the challenge lies in distinguishing between visually similar sports environments. We will solve the problem of data preparation, neural network architecture design, and model evaluation, transforming raw images into a high-accuracy predictive tool using Python’s most powerful deep learning libraries.
- [TensorFlow Food Image Classification: 36 Fruits and Vegetables in Python (Keras + OpenCV)](https://eranfeit.net/food-recognition-classifying-36-fruits-and-vegetables-in-tensorflow/): TensorFlow food image classification is a practical way to recognize what’s inside an image and turn pixels into a label your app can use.In this tutorial, you’ll build a 36-class fruits and vegetables classifier in Python using TensorFlow / Keras, while using OpenCV for consistent preprocessing.You’ll load a dataset, standardize image sizes, create NumPy arrays for fast training, train the model, evaluate accuracy, and run predictions on unseen images.
- [PixelLib Mask R-CNN Tutorial: Custom Training with LabelMe + TensorFlow](https://eranfeit.net/image-classification-and-object-detection-with-tensorflow-and-pixellib/): Are you struggling to build a precise instance segmentation pipeline without dealing with hundreds of lines of complex boilerplate code? In this hands-on guide, you will master PixelLib Mask R-CNN custom training with LabelMe to detect, segment, and extract specific objects in your images. Whether you are building an industrial defect detection system or an advanced academic project, this tutorial eliminates the guesswork. You will learn exactly how to convert raw image annotations from LabelMe into a robust, inference-ready Mask R-CNN model using TensorFlow, allowing you to bridge the gap between dataset preparation and real-time deployment.

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