milesial

    milesial/Pytorch-UNet

    PyTorch implementation of the U-Net for image semantic segmentation with high quality images

    deep-learning
    machine-learning
    convolutional-networks
    convolutional-neural-networks
    kaggle
    pytorch
    pytorch-unet
    Python
    GPL-3.0
    11.7K stars
    2.8K forks
    11.7K GitHub watchers
    Updated Oct 30, 2026
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    Where it stands

    Rank, growth pace and release cadence relative to everything GStars tracks.

    The project lists 21 contributors on GitHub.

    Use Cases & Benefits

    • Implements the U-Net architecture in PyTorch for high-quality image semantic segmentation tasks.
    • Provides pretrained models and supports mixed precision training for efficient and accurate segmentation.
    • Use for segmenting medical images to identify anatomical structures or abnormalities.
    • Use for automating object masking in high-resolution automotive images like those in the Carvana dataset.
    • Use for developing custom multiclass segmentation models on diverse datasets with easy training and prediction workflows.

    Should you depend on it?

    License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.

    License

    GPL 3.0

    Copyleft: distributing software that includes it generally requires releasing your source under the same license.

    Community files

    • README: present

    Watch-outs

    • GPL-style copyleft: including it in distributed software generally requires releasing your source under the same license.
    • Warning: No commits in over a year (last push Aug 2024).

    General guidance from repository metadata, not legal advice. How we score

    From the README

    Customized implementation of the U-Net in PyTorch for Kaggle's Carvana Image Masking Challenge from high definition images.

    Excerpt from the milesial/Pytorch-UNet README on GitHub (GPL-3.0 license). © its authors.Read the full README on GitHub

    Daily snapshots

    Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots

    Forks

    2.8K+6

    Open issues

    126+0

    Compared with peers in Deep Learning

    Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth

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