ImageNet Accuracy

ImageNet accuracy, a benchmark for evaluating image classification models, has driven significant advancements in computer vision, but its limitations as a sole metric are increasingly recognized. Current research focuses on improving model robustness to distribution shifts (e.g., ImageNet-A, real-world geographic variations), exploring alternative training paradigms like autoregressive pretraining and masked autoencoders, and analyzing the relationship between ImageNet performance and downstream task transferability across various architectures (e.g., Vision Transformers, ConvNets). These efforts aim to develop more generalizable and reliable models, moving beyond simple accuracy metrics to encompass factors like feature diversity, robustness, and alignment with human perception.