Scaling laws have been recently employed to derive compute-optimal model size (number of parameters) for a given compute duration. We advance and refine such methods to infer compute-optimal model shapes, such as width and depth, and successfully implement this in vision transformers. Our shape-optimized vision transformer, SoViT, achieves results competitive with models that exceed twice its size, despite being pre-trained with an equivalent amount of compute. For example, SoViT-400m/14 achieves 90.3% fine-tuning accuracy on ILSRCV2012, surpassing the much larger ViT-g/14 and approaching ViT-G/14 under identical settings, with also less than half the inference cost. We conduct a thorough evaluation across multiple tasks, such as image classification, captioning, VQA and zero-shot transfer, demonstrating the effectiveness of our model across a broad range of domains and identifying limitations. Overall, our findings challenge the prevailing approach of blindly scaling up vision models and pave a path for a more informed scaling.

该研究利用最新的比例定律推导了计算持续时间下最优计算模型的尺寸（参数数量），并进一步推进和改良了比例定律，以推断出计算优化的模型形状，成功地将其实现在视觉变换器中，并证明了我们的形状优化的视觉变换器（SoViT）在多个任务中都能取得出色的效果，挑战了当前盲目扩大视觉模型的做法并为更有信息的比例扩展铺平了道路。

ViT 训练规模的变化：计算优化模型设计的缩放定律