Transfer Learning For Multi-Class Image Classification Using Deep Convolutional Neural Network

In this article, we will implement the multiclass image classification using the VGG-19 Deep Convolutional Network used as a Transfer Learning framework where the VGGNet comes pre-trained on the ImageNet dataset. For the experiment, we will use the CIFAR-10 dataset and classify the image objects into 10 classes. The classification accuracies of the VGG-19 model will be visualized using the non-normalized and normalized confusion matrices.

Intel & DARPA’s Project On Making Object Detection Resilient Against Attacks

Intel, along with the Georgia Institute of Technology (Georgia Tech) recently obtained a multimillion-dollar deal from the Defense Advanced Research Projects Agency (DARPA) in the US. As per the four-year contract, both will work on ‘Guaranteeing Artificial Intelligence (AI) Robustness…

Introductory Guide To Real-Time Object Detection With Python 

Researchers have been studying the possibilities of giving machines the ability to distinguish and identify objects through vision for years now. This particular domain, called Computer Vision or CV, has a wide range of modern-day applications. From being used by…

Soap In London = Sandwich In Nepal: Does Object Recognition Work The Same Way For Everyone?

The accuracy of state-of-the-art object detection systems is often under scanner for seemingly obvious reasons. From unlocking the phone to self-driving cars, object detection is almost everywhere.  As computer vision applications grow in popularity, it has become crucial to keep…

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