ImageNet Class

ImageNet, a massive dataset of labeled images, serves as a benchmark for evaluating and training computer vision models. Current research focuses on addressing limitations of ImageNet-trained models, including biases stemming from spurious correlations like watermarks and background features, and improving robustness through techniques like core risk minimization and open-set classification protocols. These efforts aim to create more reliable and generalizable models, impacting various applications by reducing errors caused by artifacts and improving performance in real-world scenarios where unseen data is common. The development of more robust and unbiased models is crucial for advancing the field of computer vision and its applications.