[ALGORITHM]
We implement SECOND and provide the results and checkpoints on KITTI dataset.
@article{yan2018second,
title={Second: Sparsely embedded convolutional detection},
author={Yan, Yan and Mao, Yuxing and Li, Bo},
journal={Sensors},
year={2018},
publisher={Multidisciplinary Digital Publishing Institute}
}
Backbone | Class | Lr schd | Mem (GB) | Inf time (fps) | mAP | Download |
---|---|---|---|---|---|---|
SECFPN | Car | cyclic 80e | 5.4 | 79.07 | model | log | |
SECFPN | 3 Class | cyclic 80e | 5.4 | 64.41 | model | log |
Backbone | Load Interval | Class | Lr schd | Mem (GB) | Inf time (fps) | mAP@L1 | mAPH@L1 | mAP@L2 | mAPH@L2 | Download |
---|---|---|---|---|---|---|---|---|---|---|
SECFPN | 5 | 3 Class | 2x | 8.12 | 65.3 | 61.7 | 58.9 | 55.7 | log | |
above @ Car | 2x | 8.12 | 67.1 | 66.6 | 58.7 | 58.2 | ||||
above @ Pedestrian | 2x | 8.12 | 68.1 | 59.1 | 59.5 | 51.5 | ||||
above @ Cyclist | 2x | 8.12 | 60.7 | 59.5 | 58.4 | 57.3 |
Note: See more details about metrics and data split on Waymo HERE. For implementation details, we basically follow the original settings. All of these results are achieved without bells-and-whistles, e.g. ensemble, multi-scale training and test augmentation.