Motivated by the success of transformers in various fields, such as language understanding and image analysis, this investigation explores their application in the context of the game of Go. In particular, our study focuses on the analysis of the Transformer in Vision. Through a detailed analysis of numerous points such as prediction accuracy, win rates, memory, speed, size, or even learning rate, we have been able to highlight the substantial role that transformers can play in the game of Go. This study was carried out by comparing them to the usual Residual Networks.

翻译：受Transformer在语言理解和图像分析等多个领域取得成功的启发，本研究探索了其在围棋游戏中的应用。具体而言，我们的研究聚焦于视觉Transformer的分析。通过对预测准确率、胜率、内存占用、速度、模型规模乃至学习率等多个维度的详细分析，我们得以揭示Transformer在围棋游戏中可发挥的重要作用。本研究通过将其与常规残差网络进行对比而展开。