Bouldering is a sport where athletes aim to climb up an obstacle using a set of defined holds called a route. Typically routes are assigned a grade to inform climbers of its difficulty and allow them to more easily track their progression. However, the variation in individual climbers technical and physical attributes and many nuances of an individual route make grading a difficult and often biased task. In this work, we apply classical and deep-learning modelling techniques to the 2016, 2017 and 2019 Moonboard datasets, achieving state of the art grade prediction performance with 0.87 MAE and 1.12 RMSE. We achieve this performance on a feature-set that does not require decomposing routes into individual moves, which is a method common in literature and introduces bias. We also demonstrate the generalization capability of this model between editions and introduce a novel vision-based method of grade prediction. While the generalization performance of these techniques is below human level performance currently, we propose these methods as a basis for future work. Such a tool could be implemented in pre-existing mobile applications and would allow climbers to better track their progress and assess new routes with reduced bias.

通过应用经典和深度学习模型技术于2016年、2017年和2019年的Moonboard数据集，本研究取得了0.87的MAE和1.12的RMSE的最新成果，而无需将路线分解为单个动作，该方法在文献中常见且易引入偏见。我们还展示了该模型在版本之间的泛化能力，并引入了一种新颖的基于视觉的等级预测方法。尽管这些技术的泛化性能目前低于人类水平，但我们将其作为未来工作的基础。这样的工具可以被实施在现有的移动应用程序中，使攀岩者能够更好地跟踪他们的进展并评估新的路线，以减少偏见。

板到板：评估Moonboard等级预测的泛化能力