In recent years, advances in machine learning and deep learning techniques have revolutionized the field of computer vision. With the help of these techniques, we can now detect and identify objects in real time with remarkable accuracy. One of the most popular tasks in computer vision is human face detection, Continue Reading
Deep Learning
[Paper Summary] Class-Aware Adversarial Transformers for Medical Image Segmentation
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Transformer-based models have shown remarkable progress in the field of medical image segmentation. However, the existing methods still suffer from limitations such as loss of information and inaccurate segmentation label maps. In this article, we will discuss a new type of adversarial transformer, the Class-Aware Adversarial Transformer (CASTformer), which overcomes Continue Reading
What is U2-Net or U-square Net
U2-Net is a simple and powerful architecture designed for the purpose of salient object detection (SOD). It is a two-level nested U-shaped architecture built using the proposed ReSidual U-blocks (RSU). The U2-Net does not use any pre-trained architecture and is trained from scratch. The architecture comes with two variants: U2-Net Continue Reading
Simple Object Detection with Bounding Box Regression in TensorFlow
Object detection is a fundamental task in computer vision that involves identifying and locating objects within an image or video. In this post, we will be discussing a simple method for object detection using bounding box regression in TensorFlow. Bounding box regression is a technique used to predict the location Continue Reading
TensorFlow vs PyTorch
TensorFlow and PyTorch are both popular open-source frameworks for building and training machine learning models. Both frameworks have their own strengths and weaknesses, and the choice between them depends on the specific needs of the project. Introduction to TensorFlow and PyTorch TensorFlow TensorFlow, which was developed by Google, is a Continue Reading
Implementing Linear Regression in TensorFlow
TensorFlow is a powerful library for machine learning that allows for the easy implementation of various algorithms, including linear regression. In this tutorial, we will be using TensorFlow tape gradient to implement a linear regression model and plot the loss graph and x and y on matplotlib. First, we will Continue Reading
What is ChatGPT?
ChatGPT is a state-of-the-art natural language processing model developed by OpenAI. It is based on transformer architecture and is trained on a massive amount of conversational data. In this blog post, we will take a closer look at what ChatGPT is, how it works, and its applications in the field Continue Reading
Activation Function – Basics of Deep Learning
Deep learning is a subset of machine learning that utilizes neural networks with multiple layers to analyze and identify patterns in data. One of the key components of deep learning is the activation function, which is responsible for determining the output of each neuron in the network. In this blog Continue Reading
What is MobileViT?
This article covers an overall summary of the MobileViT: Light-Weight, General-Purpose, and Mobile-Friendly Vision Transformers research paper. MobileViT is a lightweight and general-purpose vision transformer for mobile vision tasks. It combines the strength of the standard CNN (Convolutional Neural Network) and the Vision Transformers. It has outperformed several CNNs and Continue Reading
Vision Transformer – An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale
In this blog post, we are going to learn about the Vision Transformer (ViT). It is a pure Transformer based architecture used for image classification tasks. Vision Transformer (ViT) has the ability to replace the standard CNNs while achieving excellent results. The Vision Transformer (ViT) attains excellent results when pre-trained Continue Reading