seglight
Super fast and real-time semantic segmentation (cpu only) can be use for 1 core cpu
Science Score: 44.0%
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Low similarity (10.4%) to scientific vocabulary
Keywords
Repository
Super fast and real-time semantic segmentation (cpu only) can be use for 1 core cpu
Basic Info
Statistics
- Stars: 12
- Watchers: 1
- Forks: 2
- Open Issues: 3
- Releases: 0
Topics
Metadata Files
README.md
Light Semantic Segmentation
Approch of this project is implementing light weight semantic segmentation model which has the least inference time as possible on cpu that we use on humanoid soccer robots. Also network architecture has been inspired from our last team project.
How to use
first of all you have to prepare suitable semantic dataset as images and labels files in dataset directory like bellow :
datase/
|_ seri1:
|_ /images/
|_ /labels/
|_ seri2:
|_ /images/
|_ /labels/
...
\
Then you have to set your configuration in config file and intiate your semantic color-map.
Notice : if you don't have reach dataset, you would use repeat option in augmentation data_provider file :
train_dataset = train_dataset.repeat(60)
repeat dataset 60 times !!
Save Model Hint !
in tensorflow version 2.16.0 and above, keras kernel updates into version 3 and it limit us to save models only in .h5 or .keras format;
so as i wanna inference on cpp and cppflow, i need to save as tf format .pb as keras v2 in order to load in cppflow inferencer.\
try to install also keras v2 :
pip install tf-keras~=2.16
then in directory of your project set env :
export TF_USE_LEGACY_KERAS=1
in main code you would change formats as you wish :
model.save("save/path", save_format='tf') # for keras v2
model.save("model.h5") # or may .keras for keras v3
Finally try to run main.py as trainer file to store trained model into that specific folder which you set in main.py.
Star History
Citation
@software{Mahdi_SegLight_Light_Semantic,
author = {Mahdi, Zeinali},{Erfan, Ramezani},
title = {{SegLight (Light Semantic Segmentation For Humanoid Soccer Robots)}},
url = {https://github.com/mahdizynali/SegLight},
version = {1.0}
}
Owner
- Name: Mahdi Zeinali
- Login: mahdizynali
- Kind: user
- Location: Tehran-Qazvin
- Company: Data2Learn
- Website: t.me/zeinali_mahdi
- Repositories: 1
- Profile: https://github.com/mahdizynali
I'm Nothing right now !!
Citation (CITATION.cff)
cff-version: 1.2.0 message: "I will be glad if you cite this project as below." authors: - family-names: "Mahdi" given-names: "Zeinali" - family-names: "Erfan" given-names: "Ramezani" title: "SegLight (Light Semantic Segmentation For Humanoid Soccer Robots)" version: 1.0 date-released: 04/15/2024 url: "https://github.com/mahdizynali/SegLight"
GitHub Events
Total
- Issues event: 2
- Watch event: 3
- Push event: 7
- Pull request event: 3
- Create event: 2
Last Year
- Issues event: 2
- Watch event: 3
- Push event: 7
- Pull request event: 3
- Create event: 2
Committers
Last synced: 8 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Mahdi | z****y@g****m | 68 |
| Erfan Ramezani | E****5@g****m | 5 |
Issues and Pull Requests
Last synced: 8 months ago
All Time
- Total issues: 1
- Total pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 1
- Total pull request authors: 0
- Average comments per issue: 0.0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 1
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 1
- Pull request authors: 0
- Average comments per issue: 0.0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- Erfan-ram (1)
- mahdizynali (1)
Pull Request Authors
- Copilot (2)