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README.md

GSTO: Gated Scale-Transfer Operation for Multi-Scale Feature Learning in Pixel Labeling

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.

Introduction

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.

Citation

If you use our code/model/data, please cite our paper: https://arxiv.org/abs/2005.13363

Semantic Segmentation Results

Cityscapes val set

single scale and no flipping, without using OHEM

model #Params GFLOPs mIoU
GSTO-HRNet-W48 65.93M 714.0 82.1

Cityscapes test set

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

LIP

model Extra. Pixel acc. avg acc. mIoU
GSTO-HRNet-W48 Train 88.38 68.36 57.4

Pose Estimation Results

COCO val set

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

COCO test set

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

Quick inference

Install

  1. PyTorch=0.4.1
  2. git clone https://github.com/VDIGPKU/GSTO
  3. 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

Data

follow the prepare instruction in HRNet-Semantic-Segmentation

Test

cityscapes test mIoU 82.4

CUDA_VISIBLE_DEVICES=0 python test.py --cfg cfg_files/gsto_hrnet_w48_cityscapes.yaml

LIP test mIoU 57.4

CUDA_VISIBLE_DEVICES=0 python test.py --cfg cfg_files/gsto_hrnet_w48_lip.yaml

About

official implementation of paper: GSTO: Gated Scale-Transfer Operation for Multi-Scale Feature Learning in Pixel Labeling

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