ImageNet Large Scale Visual Recognition Challenge
It provides a foundational benchmark for the computer vision community, facilitating progress in object recognition through standardized evaluation.
The paper tackles the problem of large-scale visual recognition by creating the ImageNet benchmark dataset, which has enabled significant advances in object classification and detection, with results showing computer vision accuracy approaching human performance.
The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the five years of the challenge, and propose future directions and improvements.