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
Why do we need GPU for Deep Learning?
The current era started to move towards Artificial Intelligence, which massively impacted the world with its ability to achieve the tasks that were a dream of humanity. All of these achievements are mainly due to the research and development in the field of Deep Learning and Neural Network, which are Continue Reading
Extract and Saving Frame from Videos in Python
In this post, we are going to learn and build a python program where we are going to extract and save frames from videos using the OpenCV library. OpenCV is one of the most commonly used libraries for computer vision tasks, such as reading and saving images, face recognition, segmentation, 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
An Introduction to Scraping Images With Python
The Internet is a rich source of data and information in the world that is easy to acquire. This data includes images, PDF, text, audio, and video. To acquire the data it is necessary to scrape it. In this tutorial, we are going to learn about scraping images with python Continue Reading