BiaPy

Accessible deep learning on bioimages

Latest release notes

🔥NEWS🔥: BiaPy's paper is finally out in Nature Methods!
[Preprint in bioRxiv]

  •   Download
    Get Additional Installers   |  

    Please install Docker to use the GUI following these instructions.
    Find instructions on how to use the GUI in this video.

    You can also install previous versions of BiaPy's graphical user interface.

  • For each workflow we have both 2D and 3D versions:

    Image classification
    (2D)
    Image denoising
    (2D)
    Image to image
    (2D)
    Instance segmentation
    (2D)
    Object detection
    (2D)
    Self-supervision
    (2D)
    Semantic segmentation
    (2D)
    Super-resolution
    (2D)
    Image classification
    (3D)
    Image denoising
    (3D)
    Image to image
    (3D)
    Instance segmentation
    (3D)
    Object detection
    (3D)
    Self supervision
    (3D)
    Semantic segmentation
    (3D)
    Super-resolution
    (3D)

    For just predicting/inference you can use the following notebook:

    Inference
    (2D/3D)

  • We have a container prepared to run BiaPy:

    latest-11.8
    Pytorch 2.9.1
    CUDA 11.8
    Ubuntu 22.04

    Docker Engine is available for Windows, macOS, and Linux, through Docker Desktop. For instructions on how to install Docker Desktop, see:

  • You have three different options to install BiaPy. Choose one or another depending on your preferences:

    • To use BiaPy via the command line, you will need to set up a conda environment. To do this, you will first need to install Conda. Then choose one of the following options based on your machine capabilities:

      A. GPU-capable machine (NVIDIA GPU)

      conda config --set channel_priority strict
conda create -n BiaPy_env -c conda-forge python=3.11 biapy pytorch-gpu
conda activate BiaPy_env

      Verify GPU at runtime:

      python -c 'import torch; print(torch.__version__)'
>>> 2.9.1
python -c 'import torch; print(torch.cuda.is_available())'
>>> True

      B. CPU-only machine

      conda config --set channel_priority strict
conda create -n BiaPy_env -c conda-forge python=3.11 biapy
conda activate BiaPy_env

    • Before you begin, ensure you have Mamba installed. Mamba is a faster alternative to Conda and can be used to manage your conda environments.Once you have mamba installed you will to choose one of the following options based on your machine capabilities:

      A. GPU-capable machine (NVIDIA GPU)

      mamba create -n BiaPy_env -c conda-forge python=3.11 biapy pytorch-gpu
mamba activate BiaPy_env

      Verify GPU at runtime:

      python -c 'import torch; print(torch.__version__)'
>>> 2.9.1
python -c 'import torch; print(torch.cuda.is_available())'
>>> True

      B. CPU-only machine

      mamba create -n BiaPy_env -c conda-forge python=3.11 biapy
mamba activate BiaPy_env

    • Set up a conda/mamba environment:

      mamba create -n BiaPy_env -c conda-forge python=3.11
mamba activate BiaPy_env

      Clone BiaPy repository:

      git clone https://github.com/BiaPyX/BiaPy.git

      Install PyTorch first, choosing GPU if available. Use the official PyTorch selector for your platform (CUDA / ROCm / CPU). Example (CUDA, just as an example-use the selector’s exact command):

      pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

      Install BiaPy in editable mode:

      cd BiaPy
pip install --editable .