Abstract
Object detection and instance recognition play a central role in many AI applications like autonomous driving, video surveillance and medical image analysis. However, training object detection models on large scale datasets remains computationally expensive and time consuming. This paper presents an efficient and open source object detection framework called SimpleDet which enables the training of state-of-the-art detection models on consumer grade hardware at large scale. SimpleDet supports up-to-date detection models with best practice. SimpleDet also supports distributed training with near linear scaling out of box. Codes, examples and documents of SimpleDet can be found at https://github.com/tusimple/simpledet .
Abstract (translated)
目标检测和实例识别在自动驾驶、视频监控和医学图像分析等许多人工智能应用中发挥着核心作用。然而，在大规模数据集上训练目标检测模型的计算成本和时间仍然很高。本文提出了一种高效、开源的对象检测框架simpledet，它可以在大规模的消费级硬件上对最先进的检测模型进行培训。SimpleDet支持具有最佳实践的最新检测模型。simpledet还支持分布式训练，具有近似线性的开箱即用扩展。simpledet的代码、示例和文档可以在https://github.com/tusimple/simpledet上找到。
URL
https://arxiv.org/abs/1903.05831
