Contributed to the ultralytics/ultralytics repository by implementing hardware compatibility for Huawei Ascend NPUs within the device selection logic. This work involved enhancing the select_device path to accurately parse and recognize Ascend hardware, allowing users to perform model inference and training with reduced setup complexity. The feature, released in ultralytics 8.4.22, addressed hardware-specific configuration challenges and improved reliability for diverse deployment environments. The development process emphasized code quality and collaboration, with proper sign-offs and co-authors included. Leveraging Python, PyTorch, and expertise in deep learning and model optimization, the contribution focused on expanding accessibility for machine learning practitioners.