SimpleDet: A Simple and Versatile Distributed Framework for Object Detection and Instance Recognition
This provides a practical solution for researchers and practitioners in fields like autonomous driving and medical imaging who need scalable object detection training, though it is incremental as it builds on existing models and best practices.
The paper tackles the problem of computationally expensive and time-consuming training of object detection models on large-scale datasets by presenting SimpleDet, an efficient open-source framework that enables training state-of-the-art detection models on consumer-grade hardware with near-linear scaling in distributed settings.
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 .