Eran Feit Blog posts

Generate synthetic images for image classification in Python

Image Classification, Pytorch /
generate synthetic images for image classification

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

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Transfer learning using Xception | ship classifier

Image Classification, TensorFlow tutorials /
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

Image Classification, OpenCV /
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

Image Segmentation, Pytorch /
Brain Tumor Segmentation

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

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

Image Segmentation, TensorFlow tutorials, Unet /
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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I tried the Ultralytics SAM2 tutorial with YOLO11. Here’s what happened.

Image Segmentation, Pytorch /
ultralytics sam2 tutorial

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

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

Image Segmentation, Pytorch /
teeth segmentation

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

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Complete Guide to SAM2 Video Segmentation in Python

Image Segmentation, Pytorch /
Segment Anything in videos

SAM2 video segmentation is a practical way to turn a single user interaction into consistent object masks across an entire video.Instead of segmenting every frame manually, you mark the object once using a few point clicks, and the model carries that object forward through time.This makes video segmentation feel much closer to an interactive editing

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Interactive SAM2 Segmentation: Points, Boxes, and Masks

Image Segmentation, Pytorch /
SAM2 tutorial

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

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How to Train Mask R‑CNN on Lung Segmentation Data

Image Segmentation, Pytorch /
Lung segmentation

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

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