This article covers an overall summary of the MobileViT: Light-Weight, General-Purpose, and Mobile-Friendly Vision Transformers research paper. MobileViT is a lightweight and general-purpose vision transformer for mobile vision tasks. It combines the strength of the standard CNN (Convolutional Neural Network) and the Vision Transformers. It has outperformed several CNNs and ViT-based networks across different tasks…
In this blog post, we are going to learn about the Vision Transformer (ViT). It is a pure Transformer based architecture used for image classification tasks. Vision Transformer (ViT) has the ability to replace the standard CNNs while achieving excellent results. The Vision Transformer (ViT) attains excellent results when pre-trained on larger datasets and transferred…
In this work, we present a lightweight matting objective decomposition network (MODNet) for portrait matting in real-time with a single input image. MODNet inputs a single RGB image and applies explicit constraints to solve matting sub-objectives simultaneously in one stage. The research paper is accepted at AAAI 2022 conference. Research paper: https://arxiv.org/pdf/2011.11961v4.pdf Code: https://github.com/ZHKKKe/MODNet The…
With the development in the field of deep learning, it has become a frontier in solving multiple challenging problems in computer vision, games, self-driving cars and many more. Deep learning has even achieved superhuman performance in some tasks, but still, it lacks some fundamental features which are required for a truly intelligent system. In this…
In this tutorial, we are going to learn how to use deep learning to remove background from images with TensorFlow. In short, we’ll use DeepLabV3+, a semantic segmentation based model to extract the background and foreground mask from the image. We are going to use these masks to extract the background from the image and…
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…
Semi-supervised learning is a type of machine learning where we use a combination of a large amount of unlabelled data and a small amount of labelled data to train the model. It is a hybrid approach between supervised learning and unsupervised learning. The basic difference between the two is that supervised learning algorithms use labelled…
Convolutional Neural Network (CNN) has been most widely used in the field of computer vision and visual perception to solve multiple tasks such as image classification, semantic segmentation and many more. However, there is a need for approaches that can further improve its performance. One such approach is to add some attention mechanism to an…
Nikhil Kumar Tomar
AI Researcher and a part-time blogger and YouTuber. Most of my research is focused medical imaging.
Hello Nikhil, Great article and video really informative and very efficient way of implementing data pipeline but when I was…
Hi Nikhil, Thanks for your dedications and explored very well. it is great helps to me. by Palani
ImportError: cannot import name ‘load_data’ from ‘data’ (C:\Users\Aravinda\anaconda3\lib\site-packages\data\__init__.py)
sir can you share a code of using squeeze and excitation network on custom CNN for a classification
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