[ALGORITHM]
@inproceedings{hashmi2023boxmask,
title={BoxMask: Revisiting Bounding Box Supervision for Video Object Detection},
author={Hashmi, Khurram Azeem and Pagani, Alain and Stricker, Didier and Afzal, Muhammad Zeshan},
booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
pages={2030--2040},
year={2023}
}We observed that the performance of this method has a fluctuation of about 0.5 mAP. The checkpoint provided below is the best one from two experiments.
Note that the numbers of selsa modules in this method and SELSA are 3 and 2 respectively. This is because another selsa modules improve this method by 0.2 points but degrade SELSA by 0.5 points. We choose the best settings for the two methods for a fair comparison.
| Method | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP@50 | Config | Download |
|---|---|---|---|---|---|---|---|
| TROI_R-50-DC5 | pytorch | 7e | 4.14 | - | 79.8 | config | model | log |
| BoxMask R-50-DC5 | pytorch | 7e | 4.14 | - | 80.7 | config | | |
| TROI_R-101-DC5 | pytorch | 7e | 5.83 | - | 82.6 | config | model | log |
| TROI_X-101-DC5 | pytorch | 7e | 9.74 | - | 84.1 | config | model | log |