Image Classification

Introduction to Image Classification with ResNet50 Deep learning has completely transformed the way we classify and recognize images.In this tutorial, we’ll walk through building and training an Alien vs Predator image classifier using ResNet50, one of the most popular pre-trained convolutional neural networks.Our goal is to leverage transfer learning to distinguish between two visually unique […]

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

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, which

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

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

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