TensorFlow tutorials

How to Use UNETR for Multiclass Image Segmentation

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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How To Actually Fine-Tune MobileNetV2 | Classify 9 Fish Species

How To Actually Fine-Tune MobileNetV2 | Classify 9 Fish Species

🎣 Classify Fish Images Using MobileNetV2 & TensorFlow 🧠 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! This Tensorflow image recognition tutorial covers from dataset splitting to live predictions

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Malaria Cell Classification CNN in TensorFlow (Keras) – Full Tutorial

CNN - Malaria

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. This

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U-Net Image Segmentation with TensorFlow/Keras (Oxford-IIIT Pets)

Unet Animals

This tutorial provides a step-by-step guide on how to implement and train a U-Net Image Segmentation TensorFlow . The tutorial is divided into four parts: Part 1: Data Preprocessing and Preparation In this part, you load and preprocess the persons dataset, including resizing images and masks, converting masks to binary format, and splitting the data

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U-Net Image Segmentation Tutorial | Deep Learning Image Segmentation Guide

Unet - segment people

Deep Learning Image Segmentation with U-Net 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 Check out our tutorial here : https://youtu.be/ZiGMTFle7bw The tutorial is divided into four parts: Part 1: Data Preprocessing and Preparation In

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Build a CNN Model for Retinal Image Diagnosis

Tensorflow retinal eye

👁️ CNN Image Classification for Retinal Health Diagnosis with TensorFlow and Keras! 👁️ 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. What You’ll Learn: 🔹 Data Collection and Preprocessing: Discover how to acquire and prepare

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120 Dog Breeds, more than 10,000 Images: Deep Learning Tutorial for dogs classification

dog breed recognition

📽️ 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 📸. What You’ll Learn: 🔹 Data Preparation: We’ll begin by downloading a dataset of of more than 20K Dogs images, neatly categorized

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Dinosaur Image Classification with a Convolutional Neural Network (Keras/TensorFlow)

Tensorflow dinosaor

Introduction Welcome to our comprehensive Dinosaur Image Classification Tutorial! Let’s learn a simple way for Dinosaur lower Classifications 🦕 Check out our tutorial here : https://www.youtube.com/watch?v=ZhTGcw0C3Dk Link for the full code here : https://eranfeit.lemonsqueezy.com/buy/f4d35093-1b80-4643-821c-45b407b0b348 or here : https://ko-fi.com/s/a902042be9 This hands-on tutorial walks you through building a Convolutional Neural Network (CNN) to classify 5 dinosaur categories

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ResUNet Skin Lesion Segmentation in TensorFlow (Step-by-Step Tutorial)

How to Segment Skin Melanoma using Res-Unet

Introduction – Skin Melanoma Segmentation using ResUnet ResUNet skin lesion segmentation TensorFlow is the goal of this tutorial.You’ll train a ResUNet in Keras to predict pixel-accurate masks from dermoscopic images, evaluate Dice/IoU, and visualize overlays on new samples.This is an educational computer-vision guide only and not medical advice. You’ll see the full workflow end-to-end: how

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