OpenMMLab Detection Toolbox and Benchmark
https://mmdetection.readthedocs.io/
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| README.md | ||
| lad_r50_paa_r101_fpn_coco_1x.py | ||
| lad_r101_paa_r50_fpn_coco_1x.py | ||
README.md
Improving Object Detection by Label Assignment Distillation
@inproceedings{nguyen2021improving,
title={Improving Object Detection by Label Assignment Distillation},
author={Chuong H. Nguyen and Thuy C. Nguyen and Tuan N. Tang and Nam L. H. Phan},
booktitle = {WACV},
year={2022}
}
Results and Models
We provide config files to reproduce the object detection results in the WACV 2022 paper for Improving Object Detection by Label Assignment Distillation.
PAA with LAD
| Teacher | Student | Training schedule | AP (val) | Config |
|---|---|---|---|---|
| -- | R-50 | 1x | 40.4 | |
| -- | R-101 | 1x | 42.6 | |
| R-101 | R-50 | 1x | 41.6 | config |
| R-50 | R-101 | 1x | 43.2 | config |
Note
- Meaning of Config name: lad_r50(student model)_paa(based on paa)_r101(teacher model)_fpn(neck)_coco(dataset)_1x(12 epoch).py
- Results may fluctuate by about 0.2 mAP.