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Image Classification

How to classify 525 Bird Species using Inception V3 and TensorFlow

Inception V3

Transfer Learning with Inception V3 in TensorFlow for Images. Introduction 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 […]

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Olympic Sports Image Classification with EfficientNetV2

Efficientnet olympic build model custom dataset

Introduction Image classification is one of the most exciting applications of computer vision. It powers technologies in sports analytics, autonomous driving, healthcare diagnostics, and more. In this project, we take you through a complete, end-to-end workflow for classifying Olympic sports images — from raw data to real-time predictions — using EfficientNetV2S, a state-of-the-art deep learning

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How to Classify images using Efficientnet B0

How to Classify images using Efficientnet B0

Introduction 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. We’ll use a pre-trained EfficientNetB0 model trained on the ImageNet dataset,

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How To Actually Use MobileNetV3 for Fish Classifier

MobileNetV3 classify images transfer learning

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. By employing transfer learning with MobileNet-V3 in TensorFlow, image classification models can achieve improved performance with reduced training time and computational resources. We’ll go step-by-step through: 👉 Watch the full tutorial here

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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 Kaggle dataset! This Tensorflow image recognition tutorial covers from dataset splitting to live

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Self-Supervised Learning Made Easy with LightlyTrain | Image Classification tutorial

LightlyTrain Image classification

In this tutorial, we will show you how to use LightlyTrain to train a model on your own dataset for image classification. Self-Supervised Learning (SSL) is reshaping computer vision, just like LLMs reshaped text. The newly launched LightlyTrain framework empowers AI teams—no PhD required—to easily train robust, unbiased foundation models on their own datasets. Let’s

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How to Classify Vehicles: VGG16 Feature Extraction & XGBoost

Object Classification using XGBoost and VGG16

In this tutorial, we build a vehicle classification model using VGG16 for feature extraction and XGBoost for classification! 🚗🚛🏍️ It will based on Tensorflow and Keras 🔍 What You’ll Learn 🔍:  🖼️ Part 1: We kick off by preparing our dataset, which consists of thousands of vehicle images across five categories. We demonstrate how to

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How to classify Malaria Cells using Convolutional neural network

CNN - Malaria

This tutorial provides a step-by-step easy guide on how to implement and train a CNN model for Malaria cell classification using TensorFlow and Keras. 🔍 What You’ll Learn 🔍:  Data Preparation — In this part, you’ll download the dataset and prepare the data for training. This involves tasks like preparing the data , splitting into training

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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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