Eran Feit Blog posts

Build a 100-Class Sports Classifier with EfficientNetB0

EfficientNetB0 image classification tutorial

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 […]

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Fast Keras Hub Image Classification Tutorial

Keras Hub Image Classification Tutorial

Staying ahead in computer vision means moving beyond fragmented libraries and embracing a unified ecosystem. This Keras Hub image classification tutorial breaks down the modern way to deploy high-performance models using the latest Keras 3 framework. By focusing on the modular ImageClassifier API, we bridge the gap between complex research architectures and practical, production-ready Python

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Classifying Knee X-Rays with ResNet152V2 & TensorFlow

ResNet152V2 TensorFlow Tutorial

In the field of medical diagnostics, deep learning is no longer just a buzzword—it is a transformative tool that assists clinicians in identifying conditions like knee osteoarthritis with unprecedented precision. This article provides a comprehensive, hands-on ResNet152V2 TensorFlow tutorial designed to take you from raw X-ray data to a fully functional, high-accuracy classification model. By

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How to Train ConvNeXt in PyTorch on a Custom Dataset

ConvNext

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

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CNN Image Classification TensorFlow: 30 Musical Instruments

cnn image classification tensorflow

This article is about building a cnn image classification tensorflow project that can recognize 30 different musical instruments from images, end-to-end. You’ll go from a folder-based dataset to a trained model that can predict the instrument in a new photo, all using a clean, practical workflow. If you’re learning computer vision, it’s easy to get

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Generate synthetic images for image classification in Python

generate synthetic images for image classification

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

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Transfer learning using Xception | ship classifier

xception transfer learning tensorflow

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. In this article, you’ll build a practical ship image classification project

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MediaPipe image classifier Python with EfficientNet-Lite0

mediapipe image classifier python

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. The real value here is that you get a working, practical

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Brain Tumor Segmentation with YOLOv11 in Python

Brain Tumor Segmentation

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

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How to UNet Image Segmentation TensorFlow on Custom Data | Dolphin Segmentation

unet image segmentation tensorflow

U-Net image segmentation in TensorFlow is a go-to approach when you need pixel-level predictions, not just a single label per image.Instead of asking “is there a dolphin in this photo,” segmentation asks “which exact pixels belong to the dolphin,” producing a mask that matches the object shape. TensorFlow/Keras makes this workflow accessible because you can

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