# SimpleDet: A Simple and Versatile Distributed Framework for Object Detection and Instance Recognition

arXiv: https://arxiv.org/abs/1903.05831 · HTML: https://mlsanity.com/paper/1903.05831/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2019.

## Built on by (most cited first, 3 of 3)

- [Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges](https://mlsanity.com/paper/2102.12219/) (2021)
- [AlphaRotate: A Rotation Detection Benchmark using TensorFlow](https://mlsanity.com/paper/2111.06677/) (2021)
- [Toward Edge-Efficient Dense Predictions with Synergistic Multi-Task Neural Architecture Search](https://mlsanity.com/paper/2210.01384/) (2022)

## Abstract (arXiv)

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 .