The majority of the people interested in deep learning must have used the TensorFlow library. It is the most popular and widely used deep learning framework. We have used the different layers provided by the tf.keras API to build different types of deep neural networks. But, there are many times when some layer is not…
In this article, we are going to implement the most widely used image segmentation architecture called UNET. We are going to replace the UNET encoder with the VGG16 implementation from the TensorFlow library. The UNET encoder would learn the features from scratch, while the VGG16 is already trained on the Image ImageNet classification dataset. Therefore…
The Squeeze and Excitation network is a channel-wise attention mechanism that is used to improve the overall performance of the network. In today’s article, we are going to implement the Squeeze and Excitation module in TensorFlow and PyTorch. What is Squeeze and Excitation Network? The squeeze and excitation attention mechanism was introduced in the year…
In this article, you will learn to perform person segmentation with DeepLabV3+ architecture on human images. Here, we will cover the entire process of image segmentation starting from data processing to evaluation. The entire code is written in Python programming language using TensorFlow 2.5 framework. Table of Content What is image segmentation? Person segmentation dataset.…
In this post, you will learn how to implement UNET architecture in TensorFlow using Keras API. The post helps you to learn about UNET, and how to use it for your research. UNET is one of the most popular semantic segmentation architecture. Olaf Ronneberger et al. developed this network for Biomedical Image Segmentation in 2015. …
RESUNET refers to Deep Residual UNET. It’s an encoder-decoder architecture developed by Zhengxin Zhang et al. for semantic segmentation. It was initially used for the road extraction from the high-resolution aerial images in the field of remote sensing image analysis. Later, it was adopted by researchers for multiple other applications such as polyp segmentation, brain…
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 2.2 and Python3.8 . According to Yann LeCun, the director of Facebook AI, GAN is the “most interesting idea in the last 10 years of machine learning.” Overview: What are…
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 use pre-trained MobileNetV2 as the encoder for the UNet architecture. We are going to integrate the pre-trained MobileNetV2 with the UNet and have an efficient network architecture. The MobileNetV2 is…
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 network (CNN) in TensorFlow 2.0. Let us first revise, what are autoencoders? Autoencoders are neural networks that attempt to mimic its input as closely as possible to its output. It…
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 polyp segmentation dataset to understand how semantic segmentation is applied to real-world data. In polyp segmentation, the images with polyp are given to a trained model and it will give…
Nikhil Kumar Tomar
AI Researcher and a part-time blogger and YouTuber. Most of my research is focused medical imaging.
