ImageNet Classification

ImageNet classification, a benchmark task in computer vision, aims to train models that accurately categorize images into predefined classes. Current research focuses on improving efficiency and accuracy through advancements in model architectures like Vision Transformers and diffusion models, as well as exploring techniques such as token merging/pruning, synthetic data augmentation, and novel initialization methods to enhance training and performance. These efforts contribute to a deeper understanding of visual representation learning and have significant implications for various applications, including object detection, image generation, and other downstream tasks.