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HSSN

Description

Author: AI-Tianlong

This project implements Deep Hierarchical Semantic Segmentation inference on cityscapes dataset

Usage

Prerequisites

Python 3.8
PyTorch 1.6 or higher
MMSegmentation v1.0.0rc5
mmcv v2.0.0rc4
mmengine >=0.4.0

Dataset preparing

Preparing cityscapes dataset following this Dataset Preparing Guide

Testing commands

Please put hieraseg_deeplabv3plus_r101-d8_4xb2-80k_cityscapes-512x1024_20230112_125023-bc59a3d1.pth to mmsegmentation/checkpoints

Multi-GPUs Test

# --tta optional, multi-scale test, need mmengine >=0.4.0
bash tools/dist_test.sh [configs] [model weights] [number of gpu]  --tta

Example

bash tools/dist_test.sh projects/hssn/configs/hssn/hieraseg_deeplabv3plus_r101-d8_4xb2-80l_cityscapes-512x1024.py checkpoints/hieraseg_deeplabv3plus_r101-d8_4xb2-80k_cityscapes-512x1024_20230112_125023-bc59a3d1.pth 2 --tta

Results

Cityscapes

Method	Backbone	Crop Size	mIoU	mIoU (ms+flip)	config	model
DeeplabV3+	R-101-D8	512x1024	81.61	82.71	config	model

Citation

This project is modified from qhanghu/HSSN_pytorch

@article{li2022deep,
  title={Deep Hierarchical Semantic Segmentation},
  author={Li, Liulei and Zhou, Tianfei and Wang, Wenguan and Li, Jianwu and Yang, Yi},
  journal={CVPR},
  year={2022}
}

Checklist

Milestone 1: PR-ready, and acceptable to be one of the projects/.
- Finish the code
- Basic docstrings & proper citation
- Test-time correctness
- A full README
Milestone 2: Indicates a successful model implementation.
- Training-time correctness
Milestone 3: Good to be a part of our core package!
- Type hints and docstrings
- Unit tests
- Code polishing
- Metafile.yml
Move your modules into the core package following the codebase's file hierarchy structure.
Refactor your modules into the core package following the codebase's file hierarchy structure.

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