This repo contains the implementation for "Learning from Noisy Anchors for One-stage Object Detection" based on Detectron2.
See INSTALL.md. The following environment has been tested:
Python 3.7
CUDA 10.1
PyTorch 1.4.0
torchvision 0.5.0
The config files are located at ./configs/COCO-Detection-NoisyAnchor. See GETTING_STARTED.md and follow the standard procedure to train/test RetinaNet with our method applied.
Before training, please download ImageNet pre-trained models as instructed in GETTING_STARTED.md and put them under ./outputs.
| Name | lr sched |
box AP |
download |
|---|---|---|---|
| R50-Baseline | 1x | 36.5 | model |
| R50-NoisyAnchor | 1x | 38.6 | model |
| R50-Baseline | 3x | 37.9 | model |
| R50-NoisyAnchor | 3x | 40.2 | model |
| R101-Baseline | 3x | 39.9 | model |
| R101-NoisyAnchor | 3x | 42.0 | model |
If you find this project useful for your research, please use the following BibTeX entry:
@inproceedings{li2020learning,
title={Learning from noisy anchors for one-stage object detection},
author={Li, Hengduo and Wu, Zuxuan and Zhu, Chen and Xiong, Caiming and Socher, Richard and Davis, Larry S},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={10588--10597},
year={2020}
}