Computer Vision

Object Detection

How to Train a Scaled-YOLOv4 Object Detection Model

In this blog, we will show an example of how to train and generalize Scaled-YOLOv4 on your custom dataset to detect custom objects.

Computer Vision

Build A Flask Web App To Compress Images Using A Variational Autoencoder

In this tutorial, we'll build a web application using Flask which will allow the user to upload images to be encoded (i.e., compressed) using a pre-trained variational autoencoder (VAE)

Deep Learning

Fine-Tuning Shallow Networks with Keras for Efficient Image Classification

Neural networks with extensively deep architectures typically contain millions of parameters, making them both computationally expensive and time-consuming to train. In this tutorial, we'll achieve state-of-the-art image classification performance using

PyTorch

A Comprehensive Guide to the DataLoader Class and Abstractions in PyTorch

In this post, we'll deal with one of the most challenging problems in the fields of Machine Learning and Deep Learning: the struggle of loading and handling different types of data.

Computer Vision

Object Detection Using Mask R-CNN with TensorFlow 2.0 and Keras

In a previous tutorial, we saw how to use the open-source GitHub project Mask_RCNN with Keras and TensorFlow 1.14. In this tutorial, the project is inspected to replace

Object Detection

Object Detection Using Mask R-CNN with TensorFlow 1.14 and Keras

Mask R-CNN is an object detection model based on deep convolutional neural networks (CNN) developed by a group of Facebook AI researchers in 2017. The model can return both the

Computer Vision

Faster R-CNN Explained for Object Detection Tasks

This article gives a review of the Faster R-CNN model developed by a group of researchers at Microsoft. Faster R-CNN is a deep convolutional network used for object detection, that

Computer Vision

Paper Review: Funnel Activation for Visual Recognition (ECCV 2020)

Component-based research in computer vision has accelerated significantly over the recent years, especially with the advent of adversarial attacks and attention mechanisms. One of the primary components which has been

Computer Vision

How To Speed Up Object Detection Using NumPy Reshape and Transpose

This is Part 4 of our ongoing series on NumPy optimization. In Parts 1 and 2 we covered the concepts of vectorization and broadcasting, and how they can be applied

Object Detection

Object Detection with PyTorch and Detectron2

In this post, we will show you how to train Detectron2 on Gradient to detect custom objects ie Flowers on Gradient. We will show you how to label custom dataset

Deep Learning

Evaluating Object Detection Models Using Mean Average Precision (mAP)

To evaluate object detection models like R-CNN and YOLO, the mean average precision (mAP) is used. The mAP compares the ground-truth bounding box to the detected box and returns a

Computer Vision

Transpose Convolution Explained for Up-Sampling Images

Convolutional neural networks need no introduction when it comes to image processing using Deep Neural Networks (DNNs). CNNs provide a more realistic way to extract and learn features from an

Computer Vision

Efficient Channel Attention for Deep Convolutional Neural Networks (ECA-Net)

In this article we'll dive into an in-depth discussion of a recently proposed attention mechanism, namely ECA-Net, published at CVPR 2020.

Computer Vision

Channel Attention and Squeeze-and-Excitation Networks (SENet)

In this article we will cover one of the most influential attention mechanisms proposed in computer vision: channel attention, as seen in Squeeze-and-Excitation Networks (SENet).

Computer Vision

GhostNet (CVPR 2020) in PyTorch and TensorFlow

In this post we'll take an in-depth look at feature maps in convolutional neural networks, do a thorough review of GhostNet, and break down the code in PyTorch and TensorFlow.

Computer Vision

Image Super-Resolution: A Comprehensive Review

Image Super Resolution refers to the task of enhancing the resolution of an image from low-resolution (LR) to high (HR). It is popularly used in the following applications: Surveillance: to

Neural Network

Capsule Networks: A Quick Primer

Whether you're new to deep learning or a serious researcher, you've surely encountered the term Convolutional Neural Networks. They are one of the most researched and top-performing architectures in the

Computer Vision

Attention Mechanisms in Computer Vision: CBAM

This post covers an in-depth analysis of the Convolution Block Attention Module (CBAM) with a short introduction to Visual Attention Mechanisms.

Coronavirus

Fighting Coronavirus with AI, Part 2: Building a CT Scan COVID-19 Classifier Using PyTorch

Using PyTorch, we create a COVID-19 classifier that predicts whether a patient is suffering from coronavirus or not, using chest CT scans of different patients.

YOLO

How to Improve YOLOv3

YOLO has been a very popular and fast object detection algorithm, but unfortunately not the best-performing. In this article I will highlight simple training heuristics and small architectural changes that can make YOLOv3 perform better than models like Faster R-CNN and Mask R-CNN.

Series: GauGAN

Understanding GauGAN Part 4: Debugging Training & Deciding If GauGAN Is Right For You

In this post we cover how to tackle common training issues that may arise with GauGAN. We conclude with advice on whether GauGAN will fit your business needs or not.

Series: GauGAN

Understanding GauGAN Part 3: Model Evaluation Techniques

In Part 3 of the GauGAN series we cover how to evaluate model performance, and how GauGAN compares to models like Pix2PixHD, SIMS, and CRN.

Computer Vision

Image Captioning With AI

In this tutorial we'll break down how to develop an automated image captioning system step-by-step using TensorFlow and Keras.

Series: GauGAN

Understanding GauGAN Part 2: Training on Custom Datasets

In this article we cover how to train GauGAN on your own custom dataset. This is part of a series on Nvidia GauGANs.

Series: GauGAN

Understanding GauGAN Part 1: Unraveling Nvidia's Landscape Painting GANs

In this article we explain what GauGANs are, and how their architecture and objective functions work. This is part of a series on Nvidia GauGANs.