36 lines
4.7 KiB
Markdown
36 lines
4.7 KiB
Markdown
# Panoptic Feature Pyramid Networks
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## Introduction
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<!-- [ALGORITHM] -->
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```latex
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@article{Kirillov_2019,
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title={Panoptic Feature Pyramid Networks},
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ISBN={9781728132938},
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url={http://dx.doi.org/10.1109/CVPR.2019.00656},
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DOI={10.1109/cvpr.2019.00656},
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journal={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
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publisher={IEEE},
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author={Kirillov, Alexander and Girshick, Ross and He, Kaiming and Dollar, Piotr},
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year={2019},
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month={Jun}
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}
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```
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## Results and models
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### Cityscapes
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| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
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| ------ | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | ---------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| FPN | R-50 | 512x1024 | 80000 | 2.8 | 13.54 | 74.52 | 76.08 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/sem_fpn/fpn_r50_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x1024_80k_cityscapes/fpn_r50_512x1024_80k_cityscapes_20200717_021437-94018a0d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x1024_80k_cityscapes/fpn_r50_512x1024_80k_cityscapes-20200717_021437.log.json) |
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| FPN | R-101 | 512x1024 | 80000 | 3.9 | 10.29 | 75.80 | 77.40 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/sem_fpn/fpn_r101_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x1024_80k_cityscapes/fpn_r101_512x1024_80k_cityscapes_20200717_012416-c5800d4c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x1024_80k_cityscapes/fpn_r101_512x1024_80k_cityscapes-20200717_012416.log.json) |
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### ADE20K
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| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
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| ------ | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | ------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| FPN | R-50 | 512x512 | 160000 | 4.9 | 55.77 | 37.49 | 39.09 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/sem_fpn/fpn_r50_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x512_160k_ade20k/fpn_r50_512x512_160k_ade20k_20200718_131734-5b5a6ab9.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r50_512x512_160k_ade20k/fpn_r50_512x512_160k_ade20k-20200718_131734.log.json) |
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| FPN | R-101 | 512x512 | 160000 | 5.9 | 40.58 | 39.35 | 40.72 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/sem_fpn/fpn_r101_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x512_160k_ade20k/fpn_r101_512x512_160k_ade20k_20200718_131734-306b5004.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/sem_fpn/fpn_r101_512x512_160k_ade20k/fpn_r101_512x512_160k_ade20k-20200718_131734.log.json) |
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