noisyboundaries
Science Score: 54.0%
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✓CITATION.cff file
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✓codemeta.json file
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✓.zenodo.json file
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○DOI references
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✓Academic publication links
Links to: arxiv.org -
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○Scientific vocabulary similarity
Low similarity (6.1%) to scientific vocabulary
Keywords
Repository
Basic Info
Statistics
- Stars: 39
- Watchers: 1
- Forks: 6
- Open Issues: 10
- Releases: 0
Topics
Metadata Files
README.md
Noisy Boundaries: Lemon or Lemonade for semi-supervised instance segmentation?
This is the mmdetection implementation of our CVPR 2022 paper. ArXiv.
Installation
This code is based on mmdetection v2.18. Please install the code according to the mmdetection step first.
data preparation
bash
noisyboundaries
data
| cityscapes
| | annotations
| | | instancesonly_filtered_gtFine_train.json
| | | instancesonly_filtered_gtFine_val.json
| | leftImg8bit
| | | train
| | | val
| coco
| | annotations
| | | instances_train2017.json
| | | instances_val2017.json
| | images
| | | train2017
| | | val2017
Running scripts
cityscapes
We take the experiment with the 20% labeled images for example.
make the label file first:
bash
mkdir labels
python scripts/cityscapes/prepare_cityscape_data.py --percent 20 --seed 1
Then, to train the supervised model, run:
bash
bash tools/dist_train.sh configs/noisyboundaries/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes_sup.py 8
With the supervised model, generating pseudo labels for semi-supervised learning:
bash
bash scripts/cityscapes/extract_pl.sh 8 labels/rcity.pkl labels/cityscapes_1@20_pl.json
Then, perform semi-supervised learning:
bash
bash tools/dist_train.sh configs/noisyboundaries/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes_pl.py 8
Owner
- Login: zhenyuw16
- Kind: user
- Repositories: 1
- Profile: https://github.com/zhenyuw16
Citation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - name: "MMDetection Contributors" title: "OpenMMLab Detection Toolbox and Benchmark" date-released: 2018-08-22 url: "https://github.com/open-mmlab/mmdetection" license: Apache-2.0
GitHub Events
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- Issues event: 1
- Watch event: 3
- Fork event: 1
Last Year
- Issues event: 1
- Watch event: 3
- Fork event: 1
Dependencies
- pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
- pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
- asynctest *
- cityscapesscripts *
- codecov *
- cython *
- flake8 *
- imagecorruptions *
- interrogate *
- isort ==4.3.21
- kwarray *
- matplotlib *
- mmtrack *
- numpy *
- onnx ==1.7.0
- onnxruntime >=1.8.0
- pycocotools *
- pycocotools-windows *
- pytest *
- scipy *
- six *
- sklearn *
- terminaltables *
- ubelt *
- xdoctest >=0.10.0
- yapf *
- cython *
- numpy *
- docutils ==0.16.0
- recommonmark *
- sphinx ==4.0.2
- sphinx-copybutton *
- sphinx_markdown_tables *
- sphinx_rtd_theme ==0.5.2
- mmcv-full >=1.3.8
- cityscapesscripts *
- imagecorruptions *
- scipy *
- sklearn *
- mmcv *
- torch *
- torchvision *
- matplotlib *
- numpy *
- pycocotools *
- pycocotools-windows *
- six *
- terminaltables *
- asynctest * test
- codecov * test
- flake8 * test
- interrogate * test
- isort ==4.3.21 test
- kwarray * test
- mmtrack * test
- onnx ==1.7.0 test
- onnxruntime >=1.8.0 test
- pytest * test
- ubelt * test
- xdoctest >=0.10.0 test
- yapf * test