Deep visual data processing is underpinning many life-changing applications, such as auto-driving and smart cities. Improving the accuracy while minimizing their inference time under constrained resources has been the primary pursuit for their practical adoptions. Existing research thus has been devoted to either narrowing down the area of interest for the detection or miniaturizing the deep learning model for faster inference time. However, the former may risk missing/delaying small but important object detection, potentially leading to disastrous consequences (e.g., car accidents), while the latter often compromises the accuracy without fully utilizing intrinsic semantic information. To overcome these limitations, in this work, we propose ScaleFlow, a closed-loop scale-adaptive inference that can reduce model inference time by progressively processing vision data with increasing resolution but decreasing spatial size, achieving speedup without compromising accuracy. For this purpose, ScaleFlow refactors existing neural networks to be scale-equivariant on multiresolution data with the assistance of wavelet theory, providing predictable feature patterns on different data resolutions. Comprehensive experiments have been conducted to evaluate ScaleFlow. The results show that ScaleFlow can support anytime inference, consistently provide 1.5× to 2.2× speed up, and save around 25% ∼ 45% energy consumption with < 1% accuracy loss on four embedded and edge platforms.