Eran Feit Blog posts

Binary Image Segmentation with VGG16 U-Net for Dust Storm Detection

Image Segmentation, TensorFlow tutorials /
Image Segmentation with VGG16 U-Net Binary Segmentation

The Role of Transfer Learning in Atmospheric Image 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 […]

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How to Train U‑Net EfficientNet-B0 for Water Segmentation

Image Segmentation, TensorFlow tutorials, Unet /
Water segmentation with U-Net EfficientNet B0

Introduction 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,

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UNet PyTorch Tutorial: Build a Segmentation Model

Image Segmentation, Pytorch /
U‑Net PyTorch tutorial

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. Segmentation is all about

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How to Perform Florence-2 segmentation on Images

Image Segmentation, Pytorch /
Segmentation Using Florence-2

Florence-2 segmentation, explained in a practical way 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

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How to segment multiple objects with YOLO Python

Image Segmentation, Pytorch /
How to segment different objects in image

YOLO segmentation tutorial Python: segmenting multiple objects with confidence 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

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Image Segmentation with MediaPipe: Replace Background

Image Segmentation, OpenCV /
Replace the background with new image

Introduction 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. In this tutorial idea, the goal is

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How to Highlight Object in Image with MediaPipe and Python

Image Segmentation /
highlight object in image python

Introduction 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

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MediaPipe Image Segmentation Using DeepLabV3

Image Segmentation /
Image Segmentation using Media-pipe DeepLabV3

Introduction 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. With MediaPipe, image segmentation becomes accessible

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How to Use UNETR for Multiclass Image Segmentation

VIT, Image Segmentation, TensorFlow tutorials /
multiclass image segmentation

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

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Hair segmentation using Transformers | UNETR Image Segmentation

Uncategorized /
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

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Eran Feit