In this tutorial, we are going to implement a Deep Convolutional Generative Adversarial Network (DCGAN) on Anime faces dataset. The code is written in TensorFlow […]
Category: Computer Vision
Nikhil Tomar
UNET Segmentation with Pretrained MobileNetV2 as Encoder
In this tutorial, we are going to work on UNet segmentation and use it for biomedical image segmentation tasks. This time we are going to […]
Building Convolutional Autoencoder using TensorFlow 2.0
We are going to continue our journey on the autoencoders. In this article, we are going to build a convolutional autoencoder using the convolutional neural […]
Introduction to Autoencoders
In today’s article, we are going to discuss a neural network architecture called autoencoders. This article is aimed at Machine Learning and Deep Learning beginners […]
Polyp Segmentation using UNET in TensorFlow 2.0
In this tutorial, we will learn about how to perform polyp segmentation using deep learning, UNet architecture, OpenCV, and other libraries. We will use a […]
Review: One Model To Learn Them All
Recent advancement in the field of deep learning has enabled us to develop models that yield impressive results across various fields, from image classification, object […]
What is Data Augmentation?
Data augmentation is a process that enables you to increase the amount of training data by making reasonable modifications in your existing data. It helps […]
Unet Segmentation in TensorFlow
In recent years deep learning is a huge success in the world of computer vision, making deep learning the new tool in the digital image analysis. It has made computers understand the visual data much better than ever before. In this article, I’ll go into details about one specific task in computer vision: Semantic Segmentation using the UNET Architecture.
Dog Breed Classification using Transfer Learning in TensorFlow
In this article, we will go through the TensorFlow to build a Convolutional Neural Network that will help you to identify the breed of a dog from its image. In the process, we will use Transfer Learning and learn how to design, train and test models with the provided dataset.