论文标题
深度学习的合成图像数据
Synthetic Image Data for Deep Learning
论文作者
论文摘要
从3D模型呈现的现实合成图像数据可用于增强图像集和训练图像分类语义分割模型。在这项工作中，我们探讨了基于生产的3D CAD模型有效地创建大型合成数据集的高质量基于物理的渲染和域随机化。我们使用该数据集使用U-NET和Double-U-NET模型来量化合成增强的有效性。我们发现，对于此领域，合成图像是增强有限的实际训练数据集的有效技术。我们观察到，在纯合成图像上训练的模型在真实验证图像上的平均预测非常低。我们还观察到，即使在合成数据集中添加非常少量的真实图像也大大提高了精度，并且在使用合成图像的数据集中训练的模型比仅在真实图像上训练的模型更准确。最后，我们发现，在从增量培训或模型专业化中受益的用例中，对合成图像的基础模型进行了预处理，可大大降低转移学习的训练成本，使多达90 \％的模型培训能够前负载。
Realistic synthetic image data rendered from 3D models can be used to augment image sets and train image classification semantic segmentation models. In this work, we explore how high quality physically-based rendering and domain randomization can efficiently create a large synthetic dataset based on production 3D CAD models of a real vehicle. We use this dataset to quantify the effectiveness of synthetic augmentation using U-net and Double-U-net models. We found that, for this domain, synthetic images were an effective technique for augmenting limited sets of real training data. We observed that models trained on purely synthetic images had a very low mean prediction IoU on real validation images. We also observed that adding even very small amounts of real images to a synthetic dataset greatly improved accuracy, and that models trained on datasets augmented with synthetic images were more accurate than those trained on real images alone. Finally, we found that in use cases that benefit from incremental training or model specialization, pretraining a base model on synthetic images provided a sizeable reduction in the training cost of transfer learning, allowing up to 90\% of the model training to be front-loaded.
