UNET Implementation in TensorFlow using Keras API

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 Continue Reading

What is RESUNET

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 Continue Reading

What is UNET?

UNET is an architecture developed by Olaf Ronneberger and his team at the University of Freiburg in 2015 for biomedical image segmentation. It is a highly popular approach for semantic segmentation tasks. It is a fully convolutional neural network that is designed to learn from fewer training samples. This architecture Continue Reading

Data Augmentation for Semantic Segmentation – Deep Learning

All the technological advancements in the field of Artificial Intelligence (AI) is facilitated due to the availability large amount of dataset and the computational hardware’s like GPU’s and TPU’s. In some fields like medical imaging, the availability of huge amount of data is not possible, as it takes good amount Continue Reading

DCGAN – Implementing Deep Convolutional Generative Adversarial Network in TensorFlow

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 Continue Reading

GAN – What is Generative Adversarial Network?

Generative Adversarial Network or GAN is a machine learning approach used for generative modelling designed by Ian Goodfellow and his colleagues in 2014. It is made of two neural networks: generator network and a discriminator network. The generator network learns to generate new examples, while the discriminator network tries to Continue Reading

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 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 Continue Reading