Image Classification Benchmark

Image classification benchmarks are crucial for evaluating the performance and robustness of computer vision models, focusing on accuracy, calibration, and resilience to various challenges like adversarial attacks, distribution shifts, and noisy labels. Current research emphasizes improving active learning strategies for efficient data usage, developing more robust models against out-of-distribution data and spurious correlations, and exploring the role of model architectures (including transformers and convolutional neural networks) and training techniques (such as semi-supervised learning and continual learning) in achieving better generalization and calibration. These advancements are vital for deploying reliable and trustworthy image classification systems in diverse real-world applications, ranging from medical diagnosis to autonomous driving.