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30 changes: 20 additions & 10 deletions README.md
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PartiNet is a three-stage pipeline for automated particle picking in cryo-EM micrographs, combining advanced denoising with state-of-the-art deep learning detection.

Installation and Usage can be found at our [Documentation](https://wehi-researchcomputing.github.io/PartiNet/)

Use our pretrained model at [Model Weights](https://huggingface.co/MihinP/PartiNet)


## Features

- 🧹 Advanced denoising for improved signal-to-noise ratio
- 🎯 Deep learning-based particle detection
- 🧹 Heuristic denoising for improved signal-to-noise ratio
- 🎯 Dynamic deep learning particle detection
- ⚡ Multi-GPU support for faster processing
- 🔄 Seamless integration with RELION workflows
- 🔄 Seamless integration with cryoSPARC and RELION workflows
- 📊 Confidence-based particle filtering
- 🖼️ Visual detection validation

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```

Alternatively, use our containers:
```
# Singularity/Apptainer
apptainer exec --nv --no-home \
-B /path/to/your/data:/data oras://ghcr.io/wehi-researchcomputing/partinet:main-singularity \
partinet --help
```

```bash
```
# Docker
docker run ghcr.io/wehi-researchcomputing/partinet:latest

# Singularity/Apptainer
singularity run oras://ghcr.io/wehi-researchcomputing/partinet:latest
docker pull ghcr.io/wehi-researchcomputing/partinet:main
docker run --gpus all -v /path/to/your/data:/data \
ghcr.io/wehi-researchcomputing/partinet:main partinet --help
```

## Directory Structure
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- Verify GPU availability: `nvidia-smi`
- Check CUDA installation
- Ensure proper device selection
- AMD and Intel GPUs are currently untested

- **Path Issues**
- Verify directory permissions
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## Contributing

We welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details.
We welcome contributions! Please raise issues or initiate pull requests on this repo.

## License

This project is licensed under the terms of the LICENSE file included in the repository.
This project is licensed under the terms of MIT license.

## Citation

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