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 […]
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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 […]
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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 who are interested in getting a brief understanding of the underlying concepts behind autoencoders. So let’s dive in and get familiar with the concept of […]
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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 […]
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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 detection, to speech recognition. However, developing the architecture for each problem is a challenge in itself. In this paper, the authors present a single deep […]
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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 you to increase the diversity of your training data which is essential for developing a robust model. This then, generally speaking, improves the performance of […]
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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.
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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.
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