SODA10M: Towards Large-Scale Object Detection Benchmark for Autonomous Driving

By Jianhua Han, Xiwen Liang, Hang Xu, Lanqing Hong, Chao Ye, Wei Zhang, Zhenguo Li, Xiaodan Liang, Chunjing Xu. 2021

49 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Liyuan Wang
2. Xingxing Zhang
3. Jun Zhu
4. Dapeng Hu
5. Lanqing Hong
6. Qizhengqiu Lu
7. Shipeng Yan
8. Yifan Zhang

Citations in the corpus

• Memory Replay with Data Compression for Continual Learning (2022, positive): “For Unbiased Teacher, we follow the default setting and change the input size to comply with SODA10M.”
• How Well Does Self-Supervised Pre-Training Perform with Streaming Data? (2021, background): “A typical example is that large-scale autonomous driving data in Han et al. (2021) are collected in different domains, such as different weather conditions and cities, but share similar classes.”
• Slim-neck by GSConv: a lightweight-design for real-time detector architectures (2022, methodology): “Ultimately, we use the SODA10M [43] to test the real-world performance of these models in a traffic environment.”
• Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles (2022, methodology): “We use the publicly available data sets WiderPerson [40], PASCAL VOC [41], SODA10M [42] and DOTA1.”