Science Score: 26.0%

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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
Created over 2 years ago · Last pushed almost 2 years ago
Metadata Files
Readme Contributing License Code of conduct Citation

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:

Installation

  1. Clone the repository: bash git clone https://github.com/sugihAF/mmdetection-lars.git
  2. Navigate to the project directory: bash cd mmdetection-lars
  3. Install the required dependencies: bash pip install -r requirements.txt

Setting Up LaRS Dataset

  1. Ensure the LaRS dataset is properly formatted and located in the specified directory.
  2. 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:

  1. Configure the model and dataset settings in the config files.
  2. Run the training script: bash python tools/train.py configs/your_config_file.py
  3. 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

GitHub Events

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Dependencies

.github/workflows/deploy.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
.circleci/docker/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
docker/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
docker/serve/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
docker/serve_cn/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
requirements/albu.txt pypi
  • albumentations >=0.3.2
requirements/build.txt pypi
  • cython *
  • numpy *
requirements/docs.txt pypi
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requirements/mminstall.txt pypi
  • mmcv >=2.0.0rc4,<2.2.0
  • mmengine >=0.7.1,<1.0.0
requirements/multimodal.txt pypi
  • fairscale *
  • jsonlines *
  • nltk *
  • pycocoevalcap *
  • transformers *
requirements/optional.txt pypi
  • cityscapesscripts *
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  • fairscale *
  • imagecorruptions *
  • scikit-learn *
requirements/readthedocs.txt pypi
  • mmcv >=2.0.0rc4,<2.2.0
  • mmengine >=0.7.1,<1.0.0
  • scipy *
  • torch *
  • torchvision *
  • urllib3 <2.0.0
requirements/runtime.txt pypi
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  • numpy *
  • pycocotools *
  • scipy *
  • shapely *
  • six *
  • terminaltables *
  • tqdm *
requirements/tests.txt pypi
  • 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
requirements/tracking.txt pypi
  • mmpretrain *
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requirements.txt pypi
setup.py pypi