This is the official implementation of paper: GSTO: Gated Scale-Transfer Operation for Multi-Scale Feature Learning in Pixel Labeling
by Zhuoying Wang, Yongtao Wang.
Contact us with wzypku@pku.edu.cn, wyt@pku.edu.cn.
The project is only free for academic research purposes, but needs authorization for commerce. For commerce permission, please contact wyt@pku.edu.cn.
If you use our code/model/data, please cite our paper: https://arxiv.org/abs/2005.13363
single scale and no flipping, without using OHEM
model | #Params | GFLOPs | mIoU |
---|---|---|---|
GSTO-HRNet-W48 | 65.93M | 714.0 | 82.1 |
multi-scale with flipping
model | Train set | OHEM | mIoU |
---|---|---|---|
GSTO-HRNet-W48 | Train | False | 81.9 |
GSTO-HRNet-W48 | Trainval | False | 82.3 |
GSTO-HRNet-W48 | Trainval | True | 82.4 |
model | Extra. | Pixel acc. | avg acc. | mIoU |
---|---|---|---|---|
GSTO-HRNet-W48 | Train | 88.38 | 68.36 | 57.4 |
model | Input size | #Params | GFLOPs | AP |
---|---|---|---|---|
GSTO-HRNet-W32 | 384x288 | 29.6M | 18.2 | 76.5 |
GSTO-HRNet-W48 | 384x288 | 66.0M | 37.6 | 76.7 |
model | Input size | #Params | GFLOPs | AP |
---|---|---|---|---|
GSTO-HRNet-W32 | 384x288 | 29.6M | 18.2 | 75.5 |
GSTO-HRNet-W48 | 384x288 | 66.0M | 37.6 | 75.8 |
- PyTorch=0.4.1
- git clone https://github.com/VDIGPKU/GSTO
- install dependencies EasyDict==1.7 opencv-python==3.4.1.15 shapely==1.6.4 Cython scipy pandas pyyaml json_tricks scikit-image yacs>=0.1.5 tensorboardX>=1.6 tqdm ninja
follow the prepare instruction in HRNet-Semantic-Segmentation
CUDA_VISIBLE_DEVICES=0 python test.py --cfg cfg_files/gsto_hrnet_w48_cityscapes.yaml
CUDA_VISIBLE_DEVICES=0 python test.py --cfg cfg_files/gsto_hrnet_w48_lip.yaml