Semantic segmentation is a vital task in computer vision, where the goal is to classify each pixel in an image into a category. Over the years, DeepLab models have become a popular choice for this due to their accuracy and efficiency. In this tutorial, I’ll share my firsthand experience working with DeepLabV3+ in Keras to … Read more >>
Image segmentation is one of the most exciting tasks in computer vision. It involves classifying each pixel in an image to identify objects or regions of interest. I have worked on various image processing projects using Python, and one architecture that consistently stands out for segmentation tasks is the U-Net. U-Net was originally designed for … Read more >>
Image classification is a fundamental task in computer vision. Over the years, transfer learning has become a popular approach to improve model performance without needing massive datasets. One of the most powerful transfer learning methods is BigTransfer (BiT). I’ve worked extensively with Python Keras, and in this article, I’ll walk you through how to use … Read more >>
Recently, I was working on a project where I needed to classify thousands of product images into multiple categories. Being a Python Keras developer for over four years, I’ve tried several deep learning models, from CNNs to Transformers. In this tutorial, I’ll show you how to perform image classification using the Perceiver model in Keras. … Read more >>
I was working on a deep learning project where I needed a model that could capture spatial relationships more efficiently than traditional convolutional neural networks (CNNs). After exploring various architectures, I came across Involutional Neural Networks, a fascinating concept that reverses the idea of convolution. In this article, I’ll show you how to build an … Read more >>
Working on a computer vision project where I needed to classify hundreds of product images into different categories. As a developer, I always look for efficient deep learning architectures that can deliver strong accuracy without excessive computation. That’s when I came across EANet (External Attention Network), a lightweight yet powerful transformer-based model. In this tutorial, … Read more >>
Working on an image classification project for a U.S.-based retail analytics company, I explored several deep learning architectures. I wanted something lightweight yet powerful enough to classify product images accurately. That’s when I came across ConvMixer, a fascinating model that combines the simplicity of CNNs with the design philosophy of Vision Transformers. In this tutorial, … Read more >>
I was working on a deep learning project where I needed a model that could combine the power of convolutional neural networks (CNNs) and transformers, but without consuming too much memory. That’s when I came across Compact Convolutional Transformers (CCT). These models are designed to be lightweight yet powerful, perfect for mobile and edge devices. … Read more >>
Recently, I worked on a deep learning project to classify chest X-ray images for pneumonia detection using Python and Keras. The challenge was to make the training process faster and more efficient, especially when dealing with thousands of medical images. That’s when I decided to use TPUs (Tensor Processing Units), powerful hardware accelerators that can … Read more >>
Working on a deep learning project where I needed to deploy an image classification model on a mobile device. The challenge was clear: I needed something lightweight yet powerful enough to handle complex image data efficiently. After exploring several architectures, I came across MobileViT, a mobile-friendly Transformer-based model that combines the strengths of Convolutional Neural … Read more >>
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