Cell Detection with PyTorch.
Project description
Cell Detection
⭐ Showcase
Nuclei of U2OS cells in a chemical screen
https://bbbc.broadinstitute.org/BBBC039 (CC0)
P. vivax (malaria) infected human blood
https://bbbc.broadinstitute.org/BBBC041 (CC BY-NC-SA 3.0)
🛠 Install
Make sure you have PyTorch installed.
PyPI
pip install celldetection
GitHub
pip install git+https://github.com/FZJ-INM1-BDA/celldetection.git
💡 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.
🔬 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
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