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114 lines
3.4 KiB
Markdown
114 lines
3.4 KiB
Markdown
# sphereDetect
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sphereDetect is a simple neural network, based on a Mask R-CNN, to detect spherical landmarks for image calibration.
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## Built with
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- [Python](https://www.python.org/)
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### Frameworks
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- [PyTorch](https://pytorch.org/)
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- [TorchVision](https://pytorch.org/vision/stable/index.html)
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- [PyTorch Lightning](https://www.pytorchlightning.ai/)
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- [PyTorch Lightning Bolts](https://www.pytorchlightning.ai/bolts)
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- [PyTorch Metrics](https://torchmetrics.readthedocs.io/en/stable/)
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- [ONNXRuntime](https://onnxruntime.ai/)
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### Tools
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- [Poetry](https://python-poetry.org/)
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- [Docker](https://www.docker.com/)
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- [VSCode](https://code.visualstudio.com/)
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- [ms-python](https://marketplace.visualstudio.com/items?itemName=ms-python.python)
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- [Python Docstring Generator](https://marketplace.visualstudio.com/items?itemName=njpwerner.autodocstring)
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- [Conventional Commits](https://marketplace.visualstudio.com/items?itemName=vivaxy.vscode-conventional-commits)
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- [Remote container](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers)
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- [EditorConfig](https://marketplace.visualstudio.com/items?itemName=EditorConfig.EditorConfig)
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- [Docker](https://marketplace.visualstudio.com/items?itemName=ms-azuretools.vscode-docker)
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- [Jupyter](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter)
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## Getting started (with docker and vscode)
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### Requirements
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- [Git](https://git-scm.com/)
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- [Docker](https://www.docker.com/)
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- [NVIDIA-Docker](https://github.com/NVIDIA/nvidia-docker)
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### Installation
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Clone the repository:
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```bash
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git clone git@git.inpt.fr:fainsil/pytorch-reva.git
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```
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Start VS Code:
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```bash
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vscode pytorch-reva
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```
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Install the [Remote Development extension pack](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.vscode-remote-extensionpack). \
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Modify variables `UID` and `GID` in [`.devcontainer/Dockerfile`](https://git.inpt.fr/fainsil/pytorch-reva/-/blob/master/.devcontainer/Dockerfile#L6) if necessary.
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Reopen the workspace in [devcontainer mode](https://code.visualstudio.com/docs/remote/containers).
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### Usage
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Configure [Weights & Biases (local) server](https://docs.wandb.ai/guides/self-hosted/local) at <http://localhost:8080>, and login:
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```bash
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wandb login --host http://localhost:8080
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```
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Press `F5` to launch `src/train.py` in debug mode (with breakpoints, slower) \
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or press `Ctrl+F5` to launch `src/train.py` in release mode.
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## Getting started (without docker)
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### Requirements
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- [Git](https://git-scm.com/)
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- [Poetry](https://python-poetry.org/)
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- [Python](https://www.python.org/)
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- [Docker](https://www.docker.com/) (if local wandb server used)
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### Installation
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Clone the repository:
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```bash
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git clone git@git.inpt.fr:fainsil/pytorch-reva.git
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cd pytorch-reva
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```
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Install the dependencies:
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```bash
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poetry install --with all
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```
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### Usage
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Activate python environment:
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```bash
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poetry shell
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```
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Configure [Weights & Biases (local) server](https://docs.wandb.ai/guides/self-hosted/local), and login:
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```bash
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wandb server start
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wandb login --host http://localhost:8080
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```
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Start a training:
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```bash
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python src/train.py
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```
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## License
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Distributed under the [MIT](https://choosealicense.com/licenses/mit/) license. \
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See [`LICENSE`](https://git.inpt.fr/fainsil/pytorch-reva/-/blob/master/LICENSE) for more information.
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## Contact
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Laurent Fainsin _[loʁɑ̃ fɛ̃zɛ̃]_ \
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\<[laurent@fainsin.bzh](mailto:laurent@fainsin.bzh)\>
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