ImageNet is a massive visual database designed for use in visual object recognition software research. It contains over 14 million images, sorted into more than 20,000 categories or “synsets”.
Imagine a huge photo album with millions of pictures of all sorts of things, from apples to zebras. This album, which we call ImageNet, helps computers learn what these different things look like so they can recognize them in other pictures.
ImageNet is a vast database or repository of images, over 14 million in total, that are grouped into more than 20,000 categories or “synsets”. Each synset identifies a concept that is scribed by a unique word or phrase, for instance, “apple” or “desk chair”. Many of these images are annotated with information about what’s in the image, offering rich data for training machine learning models.
ImageNet gained fame as the dataset used for a prominent annual contest in visual recognition called the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), which started in 2010. Today, ImageNet is used by researchers worldwide as a common benchmark for computer vision algorithms.
Models trained on ImageNet can identify various objects in images and have had significant success in improving the field of computer vision, a branch of AI that involves enabling machines to ‘see’ and understand the content of digital images such as photographs and videos.
One particular success story involves convolutional neural networks (CNNs). In 2012, a CNN model called AlexNet that was trained on ImageNet dramatically outperformed previous state-of-the-art models in the ILSVRC. This major breakthrough heralded the so-called “deep learning revolution”: the widespread adoption of deep learning techniques, particularly CNNs, in a range of AI tasks from voice recognition to autonomous driving.
However, the ImageNet dataset itself is not without challenges. It was initially created by researchers who used Amazon’s Mechanical Turk platform to categorize its images, each of which was categorized by multiple human raters. This created opportunities for human biases to be captured and propagated in the dataset. Research efforts are devoted to understanding and mitigating these biases, and they are an important aspect to consider when using Imagenet in machine learning applications.