Science Score: 54.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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○Academic publication links
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✓Committers with academic emails
13 of 127 committers (10.2%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (5.9%) to scientific vocabulary
Keywords from Contributors
Repository
mmseg tuned for mlflow
Basic Info
- Host: GitHub
- Owner: ccomkhj
- License: apache-2.0
- Language: Python
- Default Branch: main
- Size: 13.7 MB
Statistics
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Introduction
MMSegmentation is an open source semantic segmentation toolbox based on PyTorch. It is a part of the OpenMMLab project.
The master branch works with PyTorch 1.5+.
My Contribution
MMSegmentation doesn't support hyperOpt (hyper parameter tuner) natively.
hyper_opt.py explains how to run hyper parameter tuning.
tools/train_arg_in gives an example how to modify the source code.
if you want to integrate to MLflow, then follow the shell script below.
bash
export AWS_ACCESS_KEY_ID=
export AWS_SECRET_ACCESS_KEY=
export AWS_DEFAULT_REGION=
export MLFLOW_TRACKING_URI= # if you have a remote server. If you run server locally, then make it empty.
export MLFLOW_S3_ENDPOINT_URL= "http://s3.eu-central-1.amazonaws.com" # This is the case of Frankfurt datacenter.
python hyper_opt.py
In case, mlflowhook is also applied to track, then edit at mmcv/mmcv/runner/hooks/logger/mlflow.py
python
@master_only
def after_run(self, runner) -> None:
if self.log_model:
self.mlflow_pytorch.log_model(
runner.model,
'models',
pip_requirements=[f'torch=={TORCH_VERSION}'])
self.mlflow.end_run() # add this line to conclude per loop.
`
Owner
- Name: Huijo
- Login: ccomkhj
- Kind: user
- Location: Germany
- Company: @hexafarms
- Website: https://ccomkhj.github.io/
- Repositories: 3
- Profile: https://github.com/ccomkhj
Self Learner
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
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Last Year
Committers
Last synced: 9 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| MengzhangLI | m****g@p****n | 113 |
| Jerry Jiarui XU | x****6@g****m | 67 |
| Junjun2016 | h****n@s****n | 52 |
| Miao Zheng | 7****g | 36 |
| 谢昕辰 | x****e@q****m | 21 |
| Rockey | 4****s | 21 |
| sennnnn | 5****n | 19 |
| 谢昕辰 | x****h@o****m | 15 |
| yamengxi | 4****i | 14 |
| FangjianLin | 9****1 | 9 |
| sshuair | s****r@g****m | 8 |
| David de la Iglesia Castro | d****o@g****m | 6 |
| Kyungmin Lee | 3****5 | 5 |
| Kai Chen | c****v@g****m | 5 |
| q.yao | y****n@s****m | 4 |
| John Zhu | 3****a | 4 |
| FreyWang | w****z@q****m | 4 |
| AmirMasoud Nourollah | 6****h | 3 |
| Ziyi Wu | d****6@g****m | 3 |
| andife | f****r@a****e | 3 |
| 谢昕辰 | t****3@f****m | 3 |
| huijo | c****j@g****m | 3 |
| robin Han | d****t | 3 |
| congee | 3****4 | 2 |
| Zaida Zhou | 5****a | 2 |
| Yuan Liu | 3****u | 2 |
| Wencheng Wu | 4****8 | 2 |
| VVsssssk | 8****k | 2 |
| Yinhao Li | l****3@q****m | 2 |
| Sebastian | s****r@i****e | 2 |
| and 97 more... | ||
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: about 1 year ago
All Time
- Total issues: 0
- Total pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 0
- Total pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0