The text discusses the reproduction of the current state-of-the-art ImageNet training performance on a single Turing GPU server, achieving 93% Top-5 accuracy in just 2.36 hours. This was made possible by using dynamic-size images and replacing fully connected layers with global pooling layers, which reduced unnecessary preprocessing and allowed for more efficient inference. Additionally, the team employed progressive training with images of multiple resolutions, increasing the resolution step-by-step while adjusting the batch size and learning rate to achieve optimal performance. The results demonstrate a significant reduction in training time compared to previous approaches, showcasing the effectiveness of these techniques in achieving state-of-the-art performance on ImageNet.