Metadata-Version: 2.1
Name: CellDetection
Version: 0.2.1
Summary: Cell Detection with PyTorch.
Home-page: https://celldetection.org
Author: Eric Upschulte
Author-email: e.upschulte@fz-juelich.de
License: Apache License, Version 2.0
Keywords: cell,detection,object,segmentation,pytorch,cpn,contour,proposal,network,deep,learning,unet,fzj,julich,juelich,ai
Platform: UNKNOWN
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
License-File: LICENSE

# Cell Detection

[![Downloads](https://pepy.tech/badge/celldetection?l)](https://pepy.tech/project/celldetection)
[![PyPI](https://img.shields.io/pypi/v/celldetection?l)](https://pypi.org/project/celldetection/)

## ⭐ Showcase

![bbbc039](https://raw.githubusercontent.com/FZJ-INM1-BDA/celldetection/main/assets/bbbc039-cpn-u22-demo.png "BBBC039 demo with CpnU22 - Find the dataset here: https://bbbc.broadinstitute.org/BBBC039")
*https://bbbc.broadinstitute.org/BBBC039 (CC0)*

## 🛠 Install
Make sure you have [PyTorch](https://pytorch.org/get-started/locally/) installed.
### PyPI
```
pip install celldetection
```

### GitHub
```
pip install git+https://github.com/FZJ-INM1-BDA/celldetection.git
```
The examples already include toy data, so you can simply install it and start.
## 💡 How to train 
Here you can see some examples of how to train a detection model.
The examples already include toy data, so you can get started right away.
- [Train your own model](https://github.com/FZJ-INM1-BDA/celldetection/blob/main/demos/demo-binary.ipynb)
- [Train a model with multiple object classes](https://github.com/FZJ-INM1-BDA/celldetection/blob/main/demos/demo-multiclass.ipynb)

## 🔬 Models
`from celldetection import models`

###### Contour Proposal Networks:
- `models.CpnU22`
- `models.CpnSlimU22`
- `models.CpnWideU22`
- `models.CpnResNet18FPN`
- `models.CpnResNet34FPN`
- `models.CpnResNet50FPN`
- `models.CpnResNet101FPN`
- `models.CpnResNet152FPN`
- `models.CpnResNeXt50FPN`
- `models.CpnResNeXt101FPN`
- `models.CpnResNeXt152FPN`
- `models.CpnWideResNet50FPN`
- `models.CpnWideResNet101FPN`
- `models.CpnMobileNetV3SmallFPN`
- `models.CpnMobileNetV3LargeFPN`
- `models.CPN`

###### U-Nets:
- `models.U22`
- `models.SlimU22`
- `models.WideU22`
- `models.U17`
- `models.U12`
- `models.UNetEncoder`
- `models.UNet`

###### Feature Pyramid Networks:
- `models.ResNet18FPN`
- `models.ResNet34FPN`
- `models.ResNet50FPN`
- `models.ResNet101FPN`
- `models.ResNet152FPN`
- `models.ResNeXt50FPN`
- `models.ResNeXt101FPN`
- `models.ResNeXt152FPN`
- `models.WideResNet50FPN`
- `models.WideResNet101FPN`
- `models.MobileNetV3SmallFPN`
- `models.MobileNetV3LargeFPN`
- `models.FPN`

###### Residual Networks:
- `models.ResNet18`
- `models.ResNet34`
- `models.ResNet50`
- `models.ResNet101`
- `models.ResNet152`
- `models.ResNeXt50_32x4d`
- `models.ResNeXt101_32x8d`
- `models.ResNeXt152_32x8d`
- `models.WideResNet50_2`
- `models.WideResNet101_2`

###### Mobile Networks:
- `models.MobileNetV3Small`
- `models.MobileNetV3Large`



## 📝 Citing

```
@misc{upschulte2021contour,
      title={Contour Proposal Networks for Biomedical Instance Segmentation}, 
      author={Eric Upschulte and Stefan Harmeling and Katrin Amunts and Timo Dickscheid},
      year={2021},
      eprint={2104.03393},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```

## 🔗 Links
- [Abstract (arXiv)](https://arxiv.org/abs/2104.03393 "Contour Proposal Networks for Biomedical Instance Segmentation")
- [PDF (arXiv)](https://arxiv.org/pdf/2104.03393 "Contour Proposal Networks for Biomedical Instance Segmentation")
- [Bibtex (arXiv)](https://arxiv.org/bibtex/2104.03393 "Contour Proposal Networks for Biomedical Instance Segmentation")
- [PyPI](https://pypi.org/project/celldetection/ "CellDetection")


