Here, we take MMSegmentation v0.13.0 as an example, applying PVTv2 to SemanticFPN.
For details see Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions.
If you use this code for a paper please cite:
@misc{wang2021pyramid,
title={Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions},
author={Wenhai Wang and Enze Xie and Xiang Li and Deng-Ping Fan and Kaitao Song and Ding Liang and Tong Lu and Ping Luo and Ling Shao},
year={2021},
eprint={2102.12122},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Install MMSegmentation.
First, prepare ADE20K according to the guidelines in MMSegmentation.
Then, download the weights pretrained on ImageNet at here, and put them in a folder pretrained/
| Backbone | Iters | mIoU | Config | Download |
|---|---|---|---|---|
| PVTv2-B0 + Semantic FPN | 40K | 37.2 | config | |
| PVTv2-B1 + Semantic FPN | 40K | 42.5 | config | |
| PVTv2-B2 + Semantic FPN | 40K | 45.2 | config | |
| PVTv2-B3 + Semantic FPN | 40K | 47.3 | config | |
| PVTv2-B4 + Semantic FPN | 40K | 47.9 | config | |
| PVTv2-B5 + Semantic FPN | 40K | 48.7 | config |
To evaluate PVTv2-B2 + SemFPN on a single node with 8 gpus run:
dist_test.sh configs/sem_fpn/PVT/fpn_pvtv2_b2_ade20k_40k.py /path/to/checkpoint_file 8 --out results.pkl --eval mIoU
To train PVTv2-B2 + SemFPN on a single node with 8 gpus run:
dist_train.sh configs/sem_fpn/PVT/fpn_pvtv2_b2_ade20k_40k.py 8
This repository is released under the Apache 2.0 license as found in the LICENSE file.