Object Classification using XGBoost and VGG16 | Classify vehicles using Tensorflow

Image Classification /

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

Image Classification, TensorFlow tutorials /

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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How to segment X-Ray lungs using U-Net and Tensorflow

Image Segmentation, Unet /

This tutorial provides a step-by-step guide on how to implement and train a U-Net model for Melanoma detection using TensorFlow/Keras.  🔍 What You’ll Learn 🔍:  Building U-net model : Learn how to construct the model using TensorFlow and Keras. Model Training: We’ll guide you through the training process, optimizing your model to generate masks in the

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Medical Melanoma Detection | TensorFlow U-Net Tutorial using Unet

Image Segmentation, Unet /

This tutorial provides a step-by-step guide on how to implement and train a U-Net model for Melanoma detection using TensorFlow/Keras.  🔍 What You’ll Learn 🔍:  Data Preparation: We’ll begin by showing you how to access and preprocess a substantial dataset of Melanoma images and corresponding masks.  Data Augmentation: Discover the techniques to augment your dataset.

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Creating an Animal Segmentation Model with U-Net and TensorFlow Keras

TensorFlow tutorials, Unet /

This tutorial provides a step-by-step guide on how to implement and train a U-Net model for animals segmentation using TensorFlow/Keras. 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

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U-net Image Segmentation | How to segment persons in images

Image Segmentation, TensorFlow tutorials, Unet /

This tutorial provides a step-by-step guide on how to implement and train a U-Net model for persons segmentation using TensorFlow/Keras. 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

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U-net Medical Segmentation with TensorFlow and Keras (Polyp segmentation)

Image Segmentation, Unet /

This tutorial provides a step-by-step guide on how to implement and train a U-Net model for polyp segmentation using TensorFlow/Keras. The tutorial is divided into four parts: 🔹 Data Preprocessing and Preparation In this part, you load and preprocess the polyp dataset, including resizing images and masks, converting masks to binary format, and splitting the

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Build a CNN Model for Retinal Image Diagnosis

Image Classification, TensorFlow tutorials /

👁️ CNN Image Classification for Retinal Health Diagnosis with TensorFlow and Keras! 👁️ How to gather and preprocess a dataset of over 80,000 retinal images, design a CNN deep learning model , and train it that can accurately distinguish between these health categories. What You’ll Learn: 🔹 Data Collection and Preprocessing: Discover how to acquire

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

Image Classification, TensorFlow tutorials /

📽️ 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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