Science Score: 44.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
-
○Academic publication links
-
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (10.9%) to scientific vocabulary
Repository
Official implementation of SegDeformer.
Basic Info
Statistics
- Stars: 9
- Watchers: 1
- Forks: 1
- Open Issues: 2
- Releases: 0
Metadata Files
README.md
A Transformer-based Decoder for Semantic Segmentation with Multi-level Context Mining
Official implementation of the paper "A Transformer-based Decoder for Semantic Segmentation with Multi-level Context Mining",
by Bowen Shi, Dongsheng Jiang, Xiaopeng Zhang, Han Li, Wenrui Dai, Junni Zou, Hongkai Xiong, Qi Tian.
Installation
Our code is based on MMSegmentation. For install and data preparation, please refer to the guidelines in MMSegmentation.
Training
Example: train SegFormer-B1 + SegDeformer on ADE20K:
python startlocaltrain.py --configfile segformer/segformermit-b1512x512160kade20ksegdeformer3.py
Results
ADE20K
| Method| Backbone | Crop Size | Lr schd | mIoU | config | log | | ---------------- | -------- | --------- | -----| ----- | --------- | --------- | SegFormer-B1 | MiT-B1 | 512x512 | 160000 |40.97 | - | - | | SegFormer-B1 + SegDeformer | MiT-B1 | 512x512 | 160000 |44.12 | config | log | | SegFormer-B2 | MiT-B2 | 512x512 | 160000 |45.58 | - | - | | SegFormer-B2 + SegDeformer | MiT-B2 | 512x512 | 160000 | 47.34 | config | log | | SegFormer-B5 | MiT-B5 | 512x512 | 160000 |49.13 | - | - | | SegFormer-B5 + SegDeformer | MiT-B5 | 512x512 | 160000 | 50.34 | config | log |
Note:
- We adapt our code to the latest version of MMSegmentation (v0.29.1), while the pretrained MiT models we used are still the old version provided by MMSegmentation (20210726 version) to keep consistent with our paper. Details can be found in this link.
- The performance is sensitive to the seed values used, so the results might fluctuate.
Acknowledgement
This reposity is based on the MMSegmentation repository. Thanks for their contributions to the community.
Citation
If you find this repository/work helpful in your research, welcome to cite the paper. ``` @inproceedings{shi2022transformer, title={A Transformer-Based Decoder for Semantic Segmentation with Multi-level Context Mining}, author={Shi, Bowen and Jiang, Dongsheng and Zhang, Xiaopeng and Li, Han and Dai, Wenrui and Zou, Junni and Xiong, Hongkai and Tian, Qi}, booktitle={European Conference on Computer Vision}, pages={624--639}, year={2022}, organization={Springer} }
Owner
- Name: min-sbw
- Login: lygsbw
- Kind: user
- Repositories: 1
- Profile: https://github.com/lygsbw
PhD student
Citation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - name: "MMSegmentation Contributors" title: "OpenMMLab Semantic Segmentation Toolbox and Benchmark" date-released: 2020-07-10 url: "https://github.com/open-mmlab/mmsegmentation" license: Apache-2.0
GitHub Events
Total
- Watch event: 2
Last Year
- Watch event: 2
Dependencies
- pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
- pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
- docutils ==0.16.0
- myst-parser *
- sphinx ==4.0.2
- sphinx_copybutton *
- sphinx_markdown_tables *
- mmcls >=0.20.1
- mmcv-full >=1.4.4,<1.7.0
- cityscapesscripts *
- mmcv *
- prettytable *
- torch *
- torchvision *
- matplotlib *
- mmcls >=0.20.1
- numpy *
- packaging *
- prettytable *
- codecov * test
- flake8 * test
- interrogate * test
- pytest * test
- xdoctest >=0.10.0 test
- yapf * test