Pseudo-IoU-for-Anchor-Free-Object-Detection
July 23, 2021 ยท View on GitHub
This is the repo to host the code for Pseudo-IoU in the following paper: Arxiv link
By Jiachen Li, Bowen Cheng, Rogerio Feris, Jinjun Xiong, Thomas S.Huang, Wen-Mei Hwu and Humphrey Shi.
Introduction
Current anchor-free object detectors are quite simple and effective yet lack accurate label assignment methods, which limits their potential in competing with classic anchor-based models that are supported by welldesigned assignment methods based on the Intersectionover-Union (IoU) metric. In this paper, we present Pseudo Intersection-over-Union (Pseudo-IoU): a simple metric that brings more standardized and accurate assignment rule into anchor-free object detection frameworks without any additional computational cost or extra parameters for training and testing, making it possible to further improve anchor-free object detection by utilizing training samples of good quality under effective assignment rules that have been previously applied in anchor-based methods. By incorporating Pseudo-IoU metric into an end-toend single-stage anchor-free object detection framework, we observe consistent improvements in their performanceon general object detection benchmarks such as PASCAL VOC and MSCOCO. Our method (single-model and singlescale) also achieves comparable performance to other recent state-of-the-art anchor-free methods without bells and whistles.
Prerequisites
- Python 3.7
- PyTorch 1.7.0
- CUDA 11.0
- MMdetection v2.11.0
Please following the installation of mmdetection and merges Pseudo-IoU configs and models into mmdetection folder.
Results
More models will be released soon
| Backbone | Lr schd | box_mAP | box_mAP_50 | box_mAP_75 | box_mAP_s | box_mAP_m | box_mAP_l | Config | Download |
|---|---|---|---|---|---|---|---|---|---|
| R-50 | 1x | 38.4 | 57.4 | 40.9 | 23.8 | 42.5 | 48.8 | config | model |
| R-101 | 1x | 40.4 | 59.5 | 40.9 | 23.7 | 44.9 | 51.4 | config | model |
| R-101-DCN | 2x | 43.5 | 62.9 | 46.6 | 25.7 | 47.4 | 57.6 | config | model |
Citation
@article{li2021pseudoiou,
title={Pseudo-IoU: Improving Label Assignment in Anchor-Free Object Detection},
author={Jiachen Li, Bowen Cheng, Rogerio Feris, Jinjun Xiong, Thomas S.Huang, Wen-Mei Hwu and Humphrey Shi},
journal={arXiv preprint arXiv:2104.14082},
year={2021}
}