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

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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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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