The budgeted model training challenge aims to train an efficient classification model under resource limitations. To tackle this task in ImageNet-100, we describe a simple yet effective resource-aware backbone search framework composed of profile and instantiation phases. In addition, we employ multi-resolution ensembles to boost inference accuracy on limited resources. The profile phase obeys time and memory constraints to determine the models' optimal batch-size, max epochs, and automatic mixed precision (AMP). And the instantiation phase trains models with the determined parameters from the profile phase. For improving intra-domain generalizations, the multi-resolution ensembles are formed by two-resolution images with randomly applied flips. We present a comprehensive analysis with expensive experiments. Based on our approach, we win first place in International Conference on Computer Vision (ICCV) 2023 Workshop Challenge Track 1 on Resource Efficient Deep Learning for Computer Vision (RCV).

通过资源有限的预算模型训练挑战，我们提出了一种简单而有效的资源感知骨干网搜索框架，包括配置和实例化阶段，并采用多分辨率集合以提升在有限资源下的推理准确性。我们的方法在国际计算机视觉会议(ICCV) 2023年工作坊挑战赛资源高效深度学习路线上荣获第一名。

2023年ICCV研讨会挑战赛一轨道资源高效计算机视觉深度学习：预算模型训练挑战第一名