Official code for "Unifying Visual Perception by Dispersible Points Learning". The implementation is based on United-Percepion.
UniHead is a plug-in perception head which can be used in different detection frameworks (two-stage or one-stage pipelines), and different tasks (image classification, object detection, instance segmentation and pose estimation).
Currently, configs can be found in configs/unihead.
Our original implementation is based on the unreleased internal detection framework so there may be a small performance gap.
| Pipeline | mAP | Config | Model |
|---|---|---|---|
| two-stage | 42.0 | config | |
| cascade | 42.8 | config |
| Task | mAP | Config | Model |
|---|---|---|---|
| detection | 42.0 | config | |
| instance segmentation | 30.3 | config | |
| pose estimation | 57.6 | config |
More results and models will soon be released.
This project is released under the MIT license. Please see the LICENSE file for more information.
@article{liang2022unifying,
author = {Jianming Liang, Guanglu Song, Biao Leng, Yu Liu},
journal = {arXiv:2208.08630},
title = {Unifying Visual Perception by Dispersible Points Learning},
year = {2022},
}