A Deep learning pipeline for segmentation of fluorescent labels in microscopy images
Official repository of DeepFLasH2 - a deep learning pipeline for segmentation of fluorescent labels in microscopy images.
Why using deepflash2?
The best of two worlds: Combining state of the art deep learning with a barrier free environment for life science researchers.
- End-to-end process for life science researchers
- graphical user interface - no coding skills required
- free usage on Google Colab at no costs
- easy deployment on own hardware
- Rigorously evaluated deep learning models
- Model Library
- easy integration new (pytorch) models
- Best practices model training
- leveraging the fastai library
- mixed precision training
- learning rate finder and fit one cycle policy
- advanced augementation
- Reliable prediction on new data
- leveraging Bayesian Uncertainties
- Be sure to change the Colab runtime to "GPU" to have it run fast!
- Use Firefox or Google Chrome if you want to upload your images.
You can install deepflash2 on your own machines with conda (highly recommended):
conda install -c fastai -c pytorch -c matjesg deepflash2
To install with pip, use
pip install deepflash2
If you install with pip, you should install PyTorch first by following the PyTorch installation instructions.
- CPU only
docker run -p 8888:8888 matjesg/deepflash
- With GPU support (Nvidia-Docker must be installed.) has an editable install of fastai and fastcore.
docker run --gpus all -p 8888:8888 matjesg/deepflashAll docker containers are configured to start a jupyter server. deepflash2 notebooks are available in the
We provide a model library with pretrained model weights. Visit our model library documentation for information on the datasets of the pretrained models.
Creating segmentation masks with Fiji/ImageJ
A Deep-learning pipeline for Fluorescent Label Segmentation that learns from Human experts
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