️ 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 and prepare retinal images for optimal model training. CNN Architecture Design: Create a customized architecture tailored to retinal image... Build a CNN Model for Retinal Image Diagnosis ..read more

How to detect and count coins in an image using Python and OpenCV? In this tutorial, we'll walk you through the step-by-step process of using image processing techniques to identify coins in an image, sort them by size, and mark each coin with a corresponding number. We'll start by converting the image to grayscale and applying a blur to help filter out noise. Then, we'll use the Canny function to detect edges and find contours around each of the coins. After sorting the detected... Easy Coin Detection with Python and OpenCV ..read more

In this video I show you how to make an optical character recognition (OCR) using Python, OpenCV and EasyOCR ! Following the steps of this 10 minutes tutorial you will be able to detect text on images ! You can find more similar tutorials in my blog posts page here : https://eranfeit.net/blog/ check out our video here : &list=UULFTiWJJhaH6BviSWKLJUM9sg Enjoy, Eran #Python #OpenCV #ObjectDetection #ComputerVision #EasyOCR ..read more

️ 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 into 120 classes. You'll learn how to load and preprocess the data using Python, OpenCV, and Numpy, ensuring it's perfectly ready for training. CNN Architecture and the... 120 Dog Breeds, more than 10,000 Images: Deep Learning Tutorial for dogs classification ..read more

Welcome to our comprehensive Dinosaur Image Classification Tutorial! We’ll learn how use Convolutional Neural Network (CNN) to classify 5 dinosaur categories , based on 200 images : - Data Preparation: We'll begin by downloading a curated dataset of dinosaur images, neatly categorized into five distinct classes. You'll learn how to load and preprocess the data using Python, OpenCV, and Numpy, ensuring it's perfectly ready for training. - CNN Architecture: Unravel the secrets of... How to Classify Dinosaurs | CNN tutorial ..read more

Hi everyone, I’m trying to use include_once('config.php'); in my index.php, but I keep getting a "file not found" error. The file is in the same directory, and I’ve checked the permissions and paths, but nothing works. Any ideas what could be wrong? Thanks ..read more

This tutorial provides a step-by-step guide on how to implement and train a Res-UNet model for skin Melanoma detection and segmentation using TensorFlow and Keras. What You'll Learn : - Building Res-Unet 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 distinguish Melanoma from non-Melanoma skin lesions. - Testing and Evaluation: Run the... How to Segment Skin Melanoma using Res-Unet ..read more

In this tutorial in Python and OpenCV, we'll explore how to find differences in similar images. Using OpenCV functions, we'll extract two similar images out of an original image, and then Using HSV, masking and more OpenCV functions, we'll create a new image with the differences. Finally, we will extract and mark theses differences over the two original similar images . You can find more similar tutorials in my blog posts page here : https://eranfeit.net/blog/ check out our... Advanced OpenCV Tutorial: How to Find Differences in Similar Images ..read more

Discover how to perform image segmentation using K-means clustering algorithm. In this video, you will first learn how to load an image into Python and preprocess it using OpenCV to convert it to a suitable format for input to the K-means clustering algorithm. You will then apply the K-means algorithm to the preprocessed image and specify the desired number of clusters. Finally, you will demonstrate how to obtain the image segmentation by assigning each pixel in the image to its... How to Segment Images using K-means ..read more

What If we asked our deep neural network to draw it’s best image for a trained model ? What it will draw ? What is the optimized image for each model category ? We can discover that using the class maximization method on the Vgg16 model. You can find more similar tutorials in my blog posts page here : https://eranfeit.net/blog/ You can find the link for the video tutorial here: &list=UULFTiWJJhaH6BviSWKLJUM9sg Enjoy Eran ..read more