The “SemBang AIoT” live session began with an introduction to Generative AI, followed by an in-depth exploration of computer vision. It highlighted how deep learning is becoming mainstream in AI, from which Generative AI has also emerged. In the session, Kien Leong shared some history about the Deep learning AI that drove the computer vision in the past and it is closely related to the ImageNet.  

ImageNet is a large-scale visual database designed for use in visual object recognition research. It was created and maintained by researchers at Princeton University, Stanford University, and the University of North Carolina, Chapel Hill. Here are some key points about ImageNet:

Scale and Scope: ImageNet contains more than 14 million images, each annotated with labels identifying the objects present. These images span over 20,000 categories, which are organized according to the WordNet hierarchy.

Use in Competitions: ImageNet is famously known for the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), an annual competition where research teams submit algorithms for various tasks such as image classification, object detection, and image segmentation. The ILSVRC has been a driving force in the development of deep learning techniques, particularly convolutional neural networks (CNNs).

Impact on AI Research: The 2012 ILSVRC marked a significant milestone in AI research when a CNN-based model called AlexNet, developed by Geoffrey Hinton’s team, achieved a substantial improvement in image classification accuracy. This breakthrough demonstrated the potential of deep learning and sparked a surge of interest and advancements in the field.

Applications: The techniques and models developed using ImageNet have broad applications, including automated image tagging, self-driving cars, medical image analysis, and many other fields requiring image recognition and processing capabilities.

Accessibility: ImageNet is publicly accessible for academic and research purposes, providing a valuable resource for developing and testing computer vision algorithms.

ImageNet has played a crucial role in advancing the state-of-the-art in machine learning and computer vision, making it a cornerstone dataset in these fields..