Today, in this blog post, we will learn how to train a Convolutional Neural Network (CNN) to detect human facial landmarks, such as eyes, mouth, nose, jawline and more. We will use the pre-trained MobileNetv2 from TensorFlow to build our model and then train it on Landmark Guided Face Parsing...
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...
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...
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...
In this tutorial, we are going to implement the U-Net architecture in TensorFlow, where we will replace its encoder with a pre-trained VGG19 architecture. The VGG19 is already trained on the ImageNet classification dataset. Therefore, it would have already learned the required features, which would help to boost the overall...
PP-LiteSeg is a lightweight encoder-decoder architecture designed for real-time semantic segmentation. It consists of three modules: Encoder: Lightweight network Aggregation: Simple Pyramid Pooling Module (SPPM) Decoder: Flexible and Lightweight Decoder (FLD) and Unified Attention Fusion Module (UAFM) Encoder The STDCNet is the encoder for the proposed PP-LiteSeg for its high...
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...
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...
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...
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...
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
Hey Nikhil, Very nice, to-the-point article on transfer learning. For a small size of dataset, an image augmentation along with…
Truly good blog short article and also valuable.