mmdetection-lars
Science Score: 26.0%
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○CITATION.cff file
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○Scientific vocabulary similarity
Low similarity (11.7%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: sugihAF
- License: apache-2.0
- Language: Python
- Default Branch: main
- Size: 1020 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
mmdetection-lars
This project involves adapting the mmdetection library to perform panoptic segmentation using the LaRS dataset. The mmdetection library originally does not support the LaRS dataset, so custom modifications were made to create a new dataloader and adjust the library to be compatible with LaRS for panoptic segmentation tasks.
Features
- Panoptic Segmentation: Uses mmdetection's powerful panoptic segmentation capabilities.
- LaRS Dataset Support: Custom dataloader and adjustments to support the LaRS dataset.
- Flexible and Extensible: The project maintains the flexibility of mmdetection while extending its capabilities to new datasets.
Getting Started
Prerequisites
Ensure you have the following installed:
- Python 3.8+
- PyTorch (compatible version)
- mmdetection (installed as per the official instructions)
Installation
- Clone the repository:
bash git clone https://github.com/sugihAF/mmdetection-lars.git - Navigate to the project directory:
bash cd mmdetection-lars - Install the required dependencies:
bash pip install -r requirements.txt
Setting Up LaRS Dataset
- Ensure the LaRS dataset is properly formatted and located in the specified directory.
- Modify the dataset path and configuration in the provided configuration files to point to your LaRS dataset.
Running the Panoptic Segmentation
To start training or evaluation with the LaRS dataset:
- Configure the model and dataset settings in the config files.
- Run the training script:
bash python tools/train.py configs/your_config_file.py - For evaluation:
bash python tools/test.py configs/your_config_file.py checkpoints/your_checkpoint.pth
Contributing
Contributions are welcome! Please fork the repository and submit a pull request with your changes. Ensure your code is well-documented and tested before submission.
License
This project is licensed under the MIT License.
Contact
For any questions or inquiries, feel free to reach out to the project maintainer Sugih AF.
Owner
- Name: Sugih Ahmad Fauzan
- Login: sugihAF
- Kind: user
- Repositories: 12
- Profile: https://github.com/sugihAF
GitHub Events
Total
Last Year
Dependencies
- actions/checkout v2 composite
- actions/setup-python v2 composite
- pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
- pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
- pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
- pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
- albumentations >=0.3.2
- cython *
- numpy *
- docutils ==0.16.0
- myst-parser *
- sphinx ==4.0.2
- sphinx-copybutton *
- sphinx_markdown_tables *
- sphinx_rtd_theme ==0.5.2
- urllib3 <2.0.0
- mmcv >=2.0.0rc4,<2.2.0
- mmengine >=0.7.1,<1.0.0
- fairscale *
- jsonlines *
- nltk *
- pycocoevalcap *
- transformers *
- cityscapesscripts *
- emoji *
- fairscale *
- imagecorruptions *
- scikit-learn *
- mmcv >=2.0.0rc4,<2.2.0
- mmengine >=0.7.1,<1.0.0
- scipy *
- torch *
- torchvision *
- urllib3 <2.0.0
- matplotlib *
- numpy *
- pycocotools *
- scipy *
- shapely *
- six *
- terminaltables *
- tqdm *
- asynctest * test
- cityscapesscripts * test
- codecov * test
- flake8 * test
- imagecorruptions * test
- instaboostfast * test
- interrogate * test
- isort ==4.3.21 test
- kwarray * test
- memory_profiler * test
- nltk * test
- onnx ==1.7.0 test
- onnxruntime >=1.8.0 test
- parameterized * test
- prettytable * test
- protobuf <=3.20.1 test
- psutil * test
- pytest * test
- transformers * test
- ubelt * test
- xdoctest >=0.10.0 test
- yapf * test
- mmpretrain *
- motmetrics *
- numpy <1.24.0
- scikit-learn *
- seaborn *