ImageNet Benchmark

The ImageNet benchmark serves as a crucial evaluation standard for image classification models, driving advancements in computer vision. Current research focuses on improving model efficiency and robustness, exploring architectures like Vision Transformers and diffusion models, and addressing challenges such as out-of-distribution detection and handling label noise through techniques like test-time adaptation and privileged information utilization. These efforts aim to enhance the accuracy, reliability, and resource efficiency of image classification systems, impacting various applications from medical imaging to autonomous driving.