We introduce a new synthetic data generator PSP-HDRI$+$ that proves to be a superior pre-training alternative to ImageNet and other large-scale synthetic data counterparts. We demonstrate that pre-training with our synthetic data will yield a more general model that performs better than alternatives even when tested on out-of-distribution (OOD) sets. Furthermore, using ablation studies guided by person keypoint estimation metrics with an off-the-shelf model architecture, we show how to manipulate our synthetic data generator to further improve model performance.
翻译:我们引入了新的合成数据生成器PSP-HDRI$+美元,这证明是图像网络和其他大规模合成数据对应方的高级培训前替代物。 我们证明,我们合成数据培训前将产生一个比替代品更好的通用模型,即使是在分配外(OOOD)组进行测试。 此外,我们利用由人关键点估计指标和现成模型结构指导的反通货膨胀研究,展示了如何操纵我们的合成数据生成器,以进一步改善模型性能。