ImageNet Classifier

ImageNet classifiers are deep learning models trained on the massive ImageNet dataset to categorize images into thousands of object classes. Current research focuses on improving efficiency (e.g., developing lightweight networks for resource-constrained devices), understanding generalization properties of prompt-based methods, and mitigating biases and vulnerabilities such as spurious features and out-of-distribution detection. These efforts aim to enhance the robustness, fairness, and reliability of image classifiers, impacting various applications from computer vision to security and beyond.