Semantic FPN (CVPR'2019)
@article{Kirillov_2019,
title={Panoptic Feature Pyramid Networks},
ISBN={9781728132938},
url={http://dx.doi.org/10.1109/CVPR.2019.00656},
DOI={10.1109/cvpr.2019.00656},
journal={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
publisher={IEEE},
author={Kirillov, Alexander and Girshick, Ross and He, Kaiming and Dollar, Piotr},
year={2019},
month={Jun}
}
Backbone | Pretrain | Crop Size | Schedule | Train/Eval Set | mIoU | Download |
---|---|---|---|---|---|---|
R-50-D32 | ImageNet-1k-224x224 | 512x512 | LR/POLICY/BS/EPOCH: 0.01/poly/16/60 | trainaug/val | 70.88% | cfg | model | log |
R-101-D32 | ImageNet-1k-224x224 | 512x512 | LR/POLICY/BS/EPOCH: 0.01/poly/16/60 | trainaug/val | 72.51% | cfg | model | log |
Backbone | Pretrain | Crop Size | Schedule | Train/Eval Set | mIoU | Download |
---|---|---|---|---|---|---|
R-50-D32 | ImageNet-1k-224x224 | 512x512 | LR/POLICY/BS/EPOCH: 0.01/poly/16/130 | train/val | 38.16% | cfg | model | log |
R-101-D32 | ImageNet-1k-224x224 | 512x512 | LR/POLICY/BS/EPOCH: 0.01/poly/16/130 | train/val | 39.85% | cfg | model | log |
Backbone | Pretrain | Crop Size | Schedule | Train/Eval Set | mIoU | Download |
---|---|---|---|---|---|---|
R-50-D32 | ImageNet-1k-224x224 | 512x1024 | LR/POLICY/BS/EPOCH: 0.01/poly/8/220 | train/val | 76.09% | cfg | model | log |
R-101-D32 | ImageNet-1k-224x224 | 512x1024 | LR/POLICY/BS/EPOCH: 0.01/poly/8/220 | train/val | 76.39% | cfg | model | log |
You can also download the model weights from following sources:
- BaiduNetdisk: https://pan.baidu.com/s/1gD-NJJWOtaHCtB0qHE79rA with access code s757