However, it is important to note that despite the longer training time required for our dedicated-purpose model compared to the general-purpose model, it offers a shorter inference time. This means that once the model is trained, it becomes faster to process videos of any other character for inference. Furthermore, our approach achieves an average Frames Per Second (FPS) of 15 during the inference phase. In terms of model training resources, we utilize a server equipped with NVIDIA GeForce RTX 2080 Ti GPUs. Hence, we can efficiently train and deploy our model for real-time video processing tasks

你来充当论文编辑专家修正语法错误让段落表达更学术化逻辑更清晰并多采用thereforehoweverhenceconsequencetlyalthough等逻辑关系的转折词。注意我不需要你在原文上扩展只需要润色我给出的段落。段落如下:However we would like to emphasize that although our dedicated-purpose model requir

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